Fourier Transform Infrared Spectra. Applications to Chemical Systems

CONTRIBUTORS

MICHAEL COLEMAN W. G. FATELEY JOHN R. FERRARO P. C. GILLETTE WILLIAM G. GOLDEN J. A. GRAHAM W. M. GRIM 111 J. L. KOENIG K. KRISHNAN J. B. LANDO A. G. NERHEIM PAUL PAINTER PRASAD L. POLAVARAPU J. F. RABOLT ALAN J. REIN MICHAEL STARSINIC J. D. SWALEN

FOURIER TRANSFORM INFRARED SPECTROSCOPY APPLICATIONS TO CHEMICAL SYSTEMS

Edited by

JOHN R. FERRARO LOUIS J. BASILE Department of Chemistry Chemistry Division Loyola University Argonne National Chicago, Illinois Laboratory

Argonne, Illinois

VOLUME 4

1985

A C A D E M I C P R E S S , I N C . (Harcourt Brace Jovanovich, Publishers)

Orlando San Diego New York London Toronto Montreal Sydney Tokyo

COPYRIGHT © 1985, BY ACADEMIC PRESS, INC. ALL RIGHTS RESERVED. NO PART OF THIS PUBLICATION MAY BE REPRODUCED OR TRANSMITTED IN ANY FORM OR BY ANY MEANS, ELECTRONIC OR MECHANICAL, INCLUDING PHOTOCOPY, RECORDING, OR ANY INFORMATION STORAGE AND RETRIEVAL SYSTEM, WITHOUT PERMISSION IN WRITING FROM THE PUBLISHER.

ACADEMIC PRESS, INC. Orlando, Florida 32887

United Kingdom Edition published by ACADEMIC PRESS INC. (LONDON) LTD. 24-28 Oval Road, London NW1 7DX

Library of Congress Cataloging in Publication Data

Main entry under title:

Fourier transform infrared spectroscopy.

Includes bibliographies and index. 1. Infra-red spectrometry. 2. Fourier transform

spectroscopy. I. Ferraro, John, R., Date II. Basile, Louis J. QD96.I5F68 543'.08583 77-75571 ISBN 0-12-254104-9 (v. 4)

PRINTED IN THE UNITED STATES OF AMERICA

85 86 87 88 9 8 7 6 5 4 3 2 1

One editor (JRF) wishes to dedicate this volume to his co-editor, Dr. Louis J. Basile, who has undergone heart surgery.

CONTRIBUTORS

Numbers in parentheses indicate the pages on which the authors' contributions begin.

MICHAEL COLEMAN (169), Polymer Science Program, Department of Materials Science and Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802

W. G. FATELEY (345), Department of Chemistry, Kansas State Univer-sity, Manhattan, Kansas 66506

JOHN R. FERRARO (243), Department of Chemistry, Loyola University, Chicago, Illinois 60626

P. C. GILLETTE1 (1), Department of Macromolecular Science, Case Western Reserve University, Cleveland, Ohio 44106

WILLIAM G. GOLDEN (315), IBM Instruments, Inc., San Jose, California 95110

J. A. GRAHAM (345), Hercules Inc. Research Center, Wilmington, Dela-ware 19894

W. M. GRIM III (345), Nicolet Analytical Instruments, Burlington, Massa-chusetts 01803

J. L. KOENIG (1), Department of Macromolecular Science, Case Western Reserve University, Cleveland, Ohio 44106

K. KRISHNAN (97), Digilab Division, Bio-Rad Laboratories, Cambridge, Massachusetts 02139

J. B. LANDO (1), Department of Macromolecular Science, Case Western Reserve University, Cleveland, Ohio 44106

A. G. NERHEIM (147), Analytical Services Division, Standard Oil Com-pany (Indiana), Naperville, Illinois 60566

PAUL PAINTER (169), Polymer Science Program, Department of Mate-rials Science and Engineering, The Pennsylvania State University, Uni-versity Park, Pennsylvania 16802

PRASAD L. POLAVARAPU (61), Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235

J. F. RABOLT (283), IBM Research Laboratory, San Jose, California 95193

ALAN J. REIN (243), IBM Instruments, Inc., Danbury, Connecticut 06810 MICHAEL STARSINIC (169), Polymer Science Program, Department of

Materials Science and Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802

J. D. SWALEN (283), IBM Research Laboratory, San Jose, California 95193

1 Present address: Hercules Inc. Research Center, Wilmington, Delaware 19894.

XI

PREFACE

Several reasons can be cited for the need to publish Volume 4 in this treatise.

First, interest in Fourier transform interferometry (FT-IR) has contin-ued. The number of commercial manufacturers of FT-IR instrumentation has increased, reflecting the increase in demand for such instrumentation. The main thrust in FT-IR instrumentation has focused on applications, and many techniques using FT-IR instrumentation have been generated in order to solve problems heretofore unsolvable. The interest in surfaces relative to catalysts, polymers, and electrical conductors has escalated. Three chapters in Volume 4 are devoted to surfaces. Second, the great acceptance of Volumes 1 through 3 and the demand to continue the trea-tise have induced us to publish Volume 4.

The present volume contains nine chapters, making it the largest of the four volumes. Chapter 1 deals with infrared data processing techniques. Chapter 2 concerns itself with circular dichroism-FT-IR. Chapter 3 presents an update on GC-FT-IR, a rapidly moving field. Chapter 4 deals with the combination of FT-IR and thermal analysis. Advances in coal analyses using FT-IR are presented in Chapter 5. Reflectance studies are highlighted in Chapters 6, 7, and 8. Chapter 6 deals with structural charac-terizations made with Langmuir-Blodgett monolayers. Also in Chapter 6, the extension of DRIFT into the far-infrared region is shown to be feasible and valuable. Reflection-absorption surface studies (FT-IRRAS) are dis-cussed in Chapter 8. Chapter 9 updates us on photoacoustic spectros-copy-FT-IR.

All of the contributions are made by working experts in these areas. It is the hope that Volume 4 continues in the spirit of the purpose of these volumes, namely, to keep the scientific communities abreast of new de-velopments in FT-IR as applied to chemical systems.

XIII

1 A SURVEY OF INFRARED SPECTRAL DATA PROCESSING TECHNIQUES

P. C. Gillette J. B. Lando J. L. Koenig Department of Macromolecular Science Case Western Reserve University Cleveland, Ohio

IV.

V.

I. INTRODUCTION

Introduction General Quantitative Infrared Spectroscopy Considerations A. Instrumental Effects B. Optical Effects Data Processing Techniques for a Single Spectrum A. Measuring Peak Intensity-Location of

Isolated Bands B. Detection of Overlapped Bands:

Derivative Spectroscopy C. Band Shape Analysis D. Self-Deconvolution E. Interpolation F. Smoothing G. Baseline Correction Data Processing Routines for the Quantitative Analysis of Mixtures Using Multiple Spectra A. Spectral Stripping (Subtraction) B. Ratio Method C. Least Squares D. Factor Analysis E: Cross Correlation Automated Identification-Interpretation References

1

4 4 6

9

9

12 13 17 19 20 21

22 22 23 24 30 38 43 47

The advent of low-cost computer-controlled infrared (ir) spectrometers has resulted in a proliferation of spectral data processing techniques. Digitization of spectra enables the spectroscopist to extract information in

FOURIER TRANSFORM Copyright © 1985 by Academic Press, Inc. INFRARED SPECTROSCOPY, VOL. 4 All rights of reproduction in any form reserved.

ISBN 0-12-254104-9

2 P. C Gillette, J. B. Lando, and J. L. Koenig

a matter of seconds, which was not possible with older analog spectra. Shelves of spectra recorded on chart paper have been replaced by racks of magnetic tapes and disks. Spectroscopists are often overwhelmed by the amount of data produced by modern instruments. As in virtually all areas of analytical chemistry (Borman, 1982), methods for the rapid anal-ysis of spectra are becoming increasingly important.

The appeal of ir spectroscopy can be attributed to a number of factors realized at the turn of the century:

In this and in a previous research it has been shown that certain absorption bands in the infra-red are due to particular groups of atoms. The relation of these results to the question of the structure of crystals will be obvious to the reader. For, if the crystal is composed of molecules of, say, water and calcium sulphate, which separately have characteristic absorption bands, then, if these molecules or certain groups of atoms in them undergo no physical change when they combine to form a crystal (of selenite in this case), one would naturally infer that the absorption spectrum of the product will be the composite of the absorption bands of the two constituents.

W. W. Coblentz, 1906

Hence, bands characteristic of specific functional groups combine to form a unique representation of every molecule, serving identification pur-poses. Beer's law provides a fundamental relationship in which the amount of light absorbed is directly proportional to the concentration of the compound, serving quantification purposes. In the absence of interac-tions between compounds, the ir spectra of mixtures are simply a linear combination of the spectra of the pure compounds.

Broadly speaking, ir spectral data processing techniques fall into two categories: (1) those that extract information from a single spectrum and (2) procedures involving the processing of several spectra containing more than one component. Advances in both of these areas are discussed in this chapter.

A new generation of procedures has evolved since the first set of inte-grated spectral computer programs for analysis of a single spectrum was developed by Jones (1969). Single-spectrum techniques are often de-signed to detect and measure the peak frequency, intensity, and band shapes of peaks within the spectrum (e.g., derivative, integration, and moment analysis) or identify an unknown compound via comparisons with known spectra.

Spectroscopists are rarely faced with a problem in which a single spec-trum contains all of the information required for the solution. More often than not, one must compare a series of spectra to determine similarities or differences. Whenever possible, one should employ techniques that uti-

1 Infrared Spectral Data Processing 3

lize as much of the information both in an individual spectrum and in a series of spectra rather than rely on information derived from gross spec-tral features. Not only will the accuracy of quantitative measurements improve, but minute spectral changes that otherwise might have been overlooked can be recognized.

Procedures for the analysis of a series of mixtures are dependent on the amount of information at hand (i.e., knowledge of the spectra of the components). Spectral subtraction is useful for extracting the spectrum of an underlying unknown compound in a mixture when the spectra of some of the pure components are known. The absorbance ratio method can be used to obtain the spectra of pure components from a series of mixtures if each pure component has a characteristic peak. Spectral least squares curve fitting provides quantitative analysis of mixtures when spectra of all the pure components are known. Factor analysis represents the most powerful multicomponent technique in that it is possible not only to deter-mine how many pure components are present, but also to isolate pure components via extraction or library searching. Cross-correlation tech-niques are useful in detecting the presence of a compound in a noisy baseline. All of these multispectra algorithms permit the extraction or processing of the entire spectral domain of pure-component spectra from mixture spectra.

This chapter summarizes recent results in this area of computerized spectral analysis, with the view to establishing the general nature of the techniques and their application to quantitative ir measurements. Al-though ir spectroscopy is the focus, virtually all of the procedures may be applied to other forms of data. Most commercial instruments are equipped with a computer that can perform the necessary calculations for the procedures to be described. Retrofitting older instruments with a com-puter (Edgell et al., 1980; Harris, 1977) can both greatly enhance produc-tivity and permit one to extract other forms of information from spectro-scopic measurements. For a discussion of numerical procedures for the theoretical calculation of absorption frequencies and intensities, the reader is referred to several textbooks that cover this problem in detail (Woodward, 1974; Painter et al., 1982; Wilson et al., 1980). Review arti-cles related to this topic include those dealing with ir spectroscopy in general (McDonald, 1980), data processing of high-resolution spectra (Blass, 1976), computer retrieval of spectral information (Hippe and Hippe, 1980; Gribov and Elyashberg, 1979), band shape analysis (Mad-dams, 1980), theoretical considerations of band shapes and intensities (Seshadri and Jones, 1963) and statistical-mathematical data processing (Shoenfeld and DeVoe, 1976).

4 P. C. Gillette, J. B. Lando, and J. L. Koenig

II. GENERAL QUANTITATIVE INFRARED SPECTROSCOPY CONSIDERATIONS

A. Instrumental Effects

An ir spectrum contains molecular contributions (in the form of both absorption and emission) and perturbations arising from instrumental lim-itations, sample defects, and optical phenomena. The nature of instru-mental "artifacts" is, of course, a function of the type of spectrometer used. Griffiths et al. (1977) have made detailed comparisons of both Fourier and dispersive instruments.

The observed ir spectrum is actually the result of the convolution of the true molecular spectrum with an instrument line shape (ILS) function (Anderson and Griffiths, 1975, 1978; Torleington, 1980; Mertz, 1967; Cod-ding and Horlick, 1973). The failure of dispersive instruments to isolate purely monochromatic radiation from a polychromatic source is due to a finite slit width (Rautian, 1958; Potts and Smith, 1967) and results in an ILS function that will distort an ideal spectral line that is a δ function into a sine2 function. The relationship between the collected interferogram 1(8) as a function of mirror path difference (δ), obtained by Fourier transform instruments and single-beam spectrum S(v) as a function of frequency (v), can be expressed as

S(v) = j Α(δ)/(δ) exp(-27rn7jt) db (1)

where S(v) is the single-beam spectrum, Λ(δ) the apodization function, and /(δ) the interferogram.

In Fourier transform instruments the apodization of the interferogram controls the ILS function. The three most commonly used apodization functions in Fourier transform interferometry (FT-IR) are boxcar, trian-gular, and Happ-Genzel (Rabolt and Bellar, 1981; Happ and Genzel, 1961):

B{b) = 1 boxcar (2a)

Γ(δ) = 1 - (ΙδΙ/L) triangular (2b)

HG(Ö) = a + b COS(TTÖ/2L) Happ-Genzel (2c)

Here L is the maximum retardation of the moving mirror (all functions 0 for δ < —L and δ > L). Boxcar apodization retains the highest resolution but has the undesirable property of generating negative side lobes for narrow lines. Most users favor triangular apodization, which sacrifices some of the resolution without generating negative peaks, although


Happ-Genzel apodization is increasing in popularity. As a general rule for quantitative analysis, the effects of ILS can be minimized if spectra are compared that have the same ILS and contain peaks of roughly the same magnitude and shape.

One of the fundamental differences (and advantages) of FT-IR spec-trometers relative to dispersive instruments lies in the measurements made using polychromatic as opposed to monochromatic radiation. Mea-surement of the interferogram is limited not only by the finite travel of the mirror, but also by the discrete sampling of this continuous function at fixed intervals. If the interferogram is sampled at a frequency 2F, then the maximum detectable frequency is F. Frequencies greater than F, how-ever, become folded (Cooper, 1978; Swanson et ai, 1975) back into the spectral domain. This effect can be understood with reference to Fig. 1. If the two sine waves were sampled only at the points indicated, it would be impossible to distinguish between them. (In fact, an infinite number of high-frequency sine waves could be drawn through the points.) Several factors minimize this effect. The compound under study must absorb light at these high frequencies in order to produce an anomaly in the spectrum; otherwise, nothing would be seen, because the sample and reference would effectively cancel each other. Furthermore; both optical and elec-tronic filters are employed to minimize these effects (Cooper, 1978; Grif-fiths et aL, 1972), which arise due to undersampling of the interferogram. By careful calibration of the value corresponding to the frequency of the laser used to sample the interferogram (Hawkins et al., 1983), it is possi-ble to obtain peak positions accurate to within ±0.003 cm-1 over regions spanning several thousand wavenumbers using FT instruments.

In FT-IR spectrometers some error can be introduced into the spectrum if the word size of the computer is too small due to the effects of roundoff error during the calculation of the FT of the interferogram (Foskett, 1976). The number of bits allocated for storage relative to the size of the analog-to-digital converter restricts the number of scans that can be coadded to improve the signal-to-noise {SIN) ratio. Interferometer instability is yet another potential source of error (DeHaseth, 1982), although this is not

\m\M Fig. 1. Illustration of undersampling of a signal. The two sine waves are not uniquely

defined by measurements made only at the points indicated.


generally not a problem, except when a very large number of scans are being coadded.

The subject of optimizing parameters for dispersive instruments to ex-tract as much spectral information in a minimum amount of time (trading rules) was first discussed by Potts and Smith (1967). Specific recommen-dations were made for general-purpose work, spectra with low noise lev-els, energy-limited systems, high-resolution work, and fast-scan studies for dispersive spectrometers. Edgell et al. (1980) developed a computer-controlled grating spectrometer in which the computer modifies the spec-trum acquisition parameters as the spectrum is collected. Care must be exercised with grating instruments in setting the recorder time constant (McWilliam and Bolton, 1969a,b), because too large a value alters band profiles and positions significantly. More recently, Griffiths (1972; Grif-fiths et aL, 1972) considered this optimization problem in relation to FT instruments. The S/N ratios in FT-IR have been discussed in terms of instrumental requirements (Hirschfeld, 1976c), resolution (Pickett and Strauss, 1972), and practical measurement (Foskett and Hirschfeld, 1976). The increased energy throughput of an FT instrument relative to a grating spectrometer (Jacquinot advantage) is ultimately limited by detec-tor and sample considerations (Hirschfeld, 1977). Recommendations for microsampling (Hirschfeld, 1976d) have also been discussed.

B. Optical Effects

Optical phenomena such as vignetting (Hirschfeld, 1976e) and the Christiansen effect can also produce apparent deviations from Beer's law. Nonuniform [e.g., pinholes (Hirschfeld and Cody, 1977) or wedged (Hirschfeld, 1979a,b; Koenig, 1964)] samples can provide an additional source of error. Differences in the optical path of the sample and refer-ence beams (Hirschfeld, 1978) are yet another potential source of error if precautions are not taken. Orientation effects (Krishnan et a/., 1982; Koenig and Itoga, 1971), as found in some polymer samples, can further complicate sampling if not properly compensated for by special measure-ments. Significant deviations (Jones, 1952) can arise if samples in KBr pellets or mulls contain aggregated domains. This inhomogeneous behav-ior can be modeled by considering the sample to be composed of two domains: in a fraction a of the sample, Beer's law is obeyed, whereas in the remaining fraction (1 - a), the light is freely transmitted through the sample. The true absorbance At of that portion of the system actually containing sample can be expressed as (Jones, 1952)

At = a log(o/0//') (3)


where / ' is the intensity transmitted through sample, and 70 the total source intensity. The measured absorbance Am, however, includes the voids where the light may freely pass through the sample (Jones, 1952):

Am = log ./' + (1 - a)h\ (4)

Comparison of these two quantities (Fig. 2) for various values of a indi-cates that the magnitude of error increases with strong absorbances and poor particle distribution (i.e., increased voids). The effect can often be identified (Koenig, 1964) by strong peaks that are clipped. By the place-ment of such samples as close to the detector as possible (Hirschfeld and Cody, 1977), beam scatter or refraction can be minimized. Hirschfeld proposed (Hirschfeld, 1979b) that the following correction be made to correct samples for wedge effects:

At — Am + In 24 10

ΔΖΛ2 n L / (5)

Here, At is the true absorbance spectrum, Am the measured absorbance spectrum, L the mean sample width, and AL the maximum sample width deviation from mean. The parameters required to make this correction are seldom known, however, so proper sample preparation, rather than an attempt to compensate the spectrum, is recommended. For samples in the form of KBr pellets, this can often be achieved by increased grinding or grinding at liquid nitrogen temperatures.

2 .0

1.5

1.0

0.5

0 .0

0 .0 0 .5

X=0.50

2 .0 1.0 TRUE A

Fig. 2. Deviation of true and measured absorbances for varying degrees of nonuni-formity (pinholes) described by Eq. (4). x = a [in Eq. (3)].


The spectra of thin films or liquid samples often contain a superimposed sine wave (Koenig, 1964; Hirschfeld and Mantz, 1976; Clark and Moffatt, 1978). Such channel spectra or fringes result from interference effects produced by reflection. Theoretically (Randall and Rawcliffe, 1967), the intensity of these peaks can be calculated as

where n is the sample refractive index, and no the cell window refractive index. The period of the peaks can be computed as

Δν = mlldn cos Θ (7)

where m is the order of refraction, d the sample thickness, and Θ the sample tilt relative to beam (usually 90°). Although this phenomenon is sometimes useful for obtaining accurate measurements of sample thick-ness of refractive index (Hawranek et al., 1976b), more often it compli-cates other data processing procedures. Rather significant distortions of the ideal sine wave can occur (Hawranek et al., 1976a; Jones et al., 1973) for a wedge-shaped cavity. Errors in beam convergence (Hawranek et al., 1976a; Jones et al., 1973) have been found to alter the intensity and result in a phase shift of the sine wave.

Hirschfeld and Mantz (1976) proposed several methods for removing fringes based on interferogram modification. A sine wave in a spectrum will manifest as a spike in an interferogram. This point in the interfero-gram can be either replaced with a zero or interpolated before Fourier transformation to remove the fringes from the spectrum. A more desirable method, however, is to replace the point with one obtained from an inter-ferogram measured with the sample tilted. (The tilt causes a shift in the location of the spike in the interferogram.) By substituting several points in the spike region, one can compensate for slight variations in the fre-quency of the sine wave. An example of the approach is presented in Fig. 3, in which the spectrum of Mylar [poly(ethylene terephthalate)] has been corrected for fringing by the tilting-interferogram modification method.

Hawranek and Jones suggested an automated, iterative, nonlinear least squares procedure to fit the sine wave. (Hawranek et al., 1976a) Interac-tive methods for direct fringe removal (Clark and Moffatt, 1978) in the spectrum have also been developed in which the phase, amplitude, and period of the wave are modified in real time with the results displayed on an oscilloscope. An advantage of the latter approach is that the resulting spectrum does not suffer from any spectral degradation that would arise from selective zeroing of an interferogram, but it does suffer from the fact that it is a subjective procedure.


■^ΛΛ^

SIGNATURE

( V W ^ M / W V \ M ^ - ^

100

75

50

25

0

/^^m^M^ffi %

REPLACED REGION

[fMMV^^'WVVw^

3900 3400 2900 2400

WAVENUMBER

Fig. 3. Correction of fringing by interferogram modification, (a) Original spectrum of Mylar film; (b) interferogram of sample beam of (a); (c) modified interferogram; (d) spectrum computed using modified interferogram. From Hirschfeld and Mantz (1976).

The preceding discussion represents an oversimplification of the true optical phenomenon, which implicitly assumes that the refractive index remains relatively constant as a function of frequency. It is well known, however, that absorption bands are accompanied by substantial changes in refractive index, so that any quantitative information extracted from spectra with fringes removed must be viewed with a great deal of reserva-tion. Perhaps the simplest solution to the problem (Koenig, 1964) is to roughen the sample surface.

III. DATA PROCESSING TECHNIQUES FOR A SINGLE SPECTRUM

A. Measuring Peak Intensity-Location of Isolated Bands

The measurement of peak intensity-location represents one of the most basic forms of spectral data processing that must be performed by the


spectroscopist. Because most graphics terminals have a provision for either an interactive cursor or light pen, it is often simplest to allow the user to select the approximate peak locations interactively. Computer-based automated procedures are often complicated by random noise. Many algorithms require that the user specify some minimum-intensity threshold, below which no peaks will be identified. This approach, how-ever, has a tendency to overlook weak bands in the spectrum. The situa-tion is complicated by the fact that very often the background is not simply a linear offset, but rather a function of frequency.

Peak locations can be accurately computed via a simple quadratic inter-polation. For a spectrum sampled at uniform intervals, the location and intensity can be calculated as

a- = * ~ fl«,-, - 2a, - a,+ i) ( 8 a )

-m = . _ Av(a^- a m ) ( 8 b )

where am is the interpolated maximum absorbance, vm the interpolated frequency of am, αι-χ, at, and ai+\ are the absorbances of three points defining peak, vt is the frequency corresponding to ax, and Δν the sampling interval on the cm- 1 axis. Some care must be exercised when interpolated data are used, because in some cases changes in the band profile will result in apparent changes in peak intensity and position. The use of interpolation in conjunction with moments analysis (to be dis-cussed later in this section) is recommended.

For bands that are well resolved (i.e., not overlapped) it is desirable to compute integrated peak intensities. (By integrating, one is improving the measurement in a manner analogous to coadding more scans to improve the S/N ratio.) Ramsay concluded that the band profiles of liquids closely approximated Lorentzian curves and proposed (Ramsay, 1952) a method that involved measurements of peak intensity, half-width, and slit width. Cabana and Sandorfy (1960) later modified Ramsay's approach by mea-suring the peak width at several points to obtain a better estimate of the actual band profile. In another study (Vance et ai, 1979), however, better results were obtained by integration with a planimeter.

With the digitization of spectra, integrated absorbances of well-isolated bands can be readily computed by the use of standard numerical integra-tion methods with no assumption of a band profile function. For example, the well-known trapezoidal rule can be computed using the following summation:

Area = A i 7 «2 + 2» + Σ fl/ ( 9 )


Here, Δ^ is the increment between points, and ax the absorbance of the /th point in the spectrum. Hirschfeld (1976b) investigated the theoretical im-provements realized by integration by considering a Lorentzian peak pro-file.

The use of truncated moments (Jones et al., 1963; Grushka et al., 1969, 1970) permits characterization of peak shape. Such moments are com-puted as

U(r) = j {v - vm)rA{v) a*

Hr j A(v) a, (10)

where U(r) is the rth moment, A{y) the absorbance spectrum defining peak, vm the position of maximum intensity, and H the half-width at half-height. For symmetric peaks, all odd moments are zero. Practical applica-tions of this formula require that the above integrals in Eq. (10) be evalu-ated over definite limits. Jones et al. (1963) developed expressions that measure the sensitivity of errors in the location of band maxima and baseline drift on moments. By varying the limits of integration in Eq. (10), one can obtain a profile that can be correlated to known band profiles. For accurate evaluation of moments, spectra must be collected at high resolu-tion to ensure that a large number of points define the band profile. (It is not sufficient simply to interpolate points in a low-resolution spectrum.) Figure 4 illustrates this procedure for theoretical Lorentzian and Gaussian band shapes.

UJ

C/5

2.5

2 .0 H

1.5 1 o Q

§ l.o H

0.5 H

0 .0 0 .0

LORENTZIAN

4 . 0

LIMITS OF INTEGRATION (HWHH UNITS) Fig. 4. Determination of band profile function using moments analysis by varying the

limits of integration for Lorentzian and Gaussian band shapes. HWHH (cm1).


B. Detection of Overlapped Bands: Derivative Spectroscopy

Derivative spectroscopy (Martin, 1959; O'Haver and Begley, 1981; Ca-hill and Padera, 1980; Cahill, 1980; O'Haver, 1979; Savitsky and Golay, 1964; Grushka and Monacelli, 1972; Collier and Singleton, 1956; Giese and French, 1955; O'Haver and Green, 1976; Betty and Horlick, 1976; Horlick, 1972a; Whitbeck, 1981; McWilliam, 1969; Ashley and Reilley, 1965; Kauppinen et ai, 1981a) is a useful technique for identifying over-lapped peaks by accentuating band profiles. Small shoulders on intense peaks can be readily identified in even-order-derivative spectra. Random noise is also greatly "enhanced," so this procedure should be used only on spectra with high SIN ratios. Figure 5 illustrates a simple example of peak enhancement in a model derivative spectrum. The small shoulder in the original spectrum appears as a sharp minimum in the second-deriva-tive spectrum. (In the first-derivative spectrum, peak maxima are identi-fied as zero crossings in the absence of a sloped baseline.)

An additional benefit of derivative spectroscopy (Whitbeck, 1981) lies in the fact that the technique is biased toward the sharper features of the spectrum. For Lorentzian (Martin, 1959) the half-width at half-height de-creases to ~ i in a second derivative and ~£ in a fourth derivative relative to the original peak. In even-order derivatives "satellite peaks" of negative intensity (Martin, 1959; Collier and Singleton, 1956) appear on both sides of the main peak. A second-derivative spectrum will completely eliminate a sloped linear baseline (Collier and Singleton, 1956).

Fig. 5. Use of derivative spectroscopy for peak detection. Curve A, Original spectrum synthesized from two Lorentzian peak profiles; curve A', first derivative; curve A", second derivative.


O'Haver and Begley (1981) and O'Haver and Green (1976) investigated the S/N ratio in derivative spectra. The use of smoothing in conjunction with derivatives (O'Haver and Begley, 1981; Kauppinen et al., 1981a) often permits information to be obtained from higher-order derivatives when S/N becomes a problem. Giese and French (1955) illustrated a wide range of synthetic spectra and their derivatives that provide some insight into the resolving power of the technique.

Early methods of obtaining derivative spectra (Martin, 1959; Collier and Singleton, 1956; McWilliam, 1969; Ashley and Reilley, 1965) utilized electronic circuits for the direct recording of the derivative spectrum. Numerical approximations of derivatives are readily calculated via Savitzky-Golay least squares polynomials (Savitzky and Golay, 1964; Steiner et al., 1972) or cubic splines (Whitbeck, 1981). Fourier transform methods (Betty and Horlick, 1976; Horlick, 1972a; Kauppinen et al., 1981a) have an elegant simplicity in that they require weighting the in-verse Fourier transform of the spectrum by a polynomial function,

An(v) = FT[(2TT/6)M(6)] (11)

where An(v) is the Aith-order derivative of A{v), and Λ(δ) the inverse Fourier transform of A(v). This approach is particularly attractive for use with computers having array processors.

For samples in which orientation exists, it is sometimes possible to utilize polarized radiation in conjunction with variation of the sample orientation to resolve overlapped bands (Krishnan, 1978).

C. Band Shape Analysis

With extensive band overlap it becomes necessary to use band shape analysis procedures (Maddams, 1980; Pitha and Jones, 1966, 1967; Baker et al., 1978a,b; Chang and Shaw, 1977; Grans and Gill, 1980; Gillette et al., 1982a; Brown and Dennis, 1972; Marquardt, 1963; Hayakawa and Oka, 1981; Nomura et al., 1979; and DuPont curve resolver description), which require that the bands constituting a particular spectral region have some precise functional form. A variety of functions representing band shapes have been suggested, including pure Lorentzian and Gaussian forms, which may also include additional factors to account for band asymmetry. Inverse polynomial functions (Baker et al., 1978a,b) have also been investigated. Simple Lorentzian-Gaussian forms require three parameters to describe each band profile,

L^ = IP +'f- vmf (12)

14 P. C. Gillette, J. B. Lando, and J. L Koenig

G{y) = Im exp{-ln 2[(v - vm)IH]2} (13) where /m is the maximum intensity of peak, / / the half-width at half-height of the peak, and vm the frequency location of Im. The preceding functions can be integrated to yield the following equations for peak areas:

j L(v) a* = IJiTT (14a)

{ G(i7) df = /m//(7r/ln 2)1/2 (14b)

An example of this procedure using four Lorentzian peak shapes to re-solve the methylene rock-wag spectral region of poly(tetramethylene terephthalate) is depicted in Fig. 6. Accurate measurement of the inten-sity of the individual modes can be achieved only by the use of this procedure.

Theoretical considerations of band envelopes (Seshadri and Jones, 1963; Badger and Zumwalt, 1938; Young and Jones, 1971; DeGalan and Winefordner, 1968) have shown that three noninstrumental factors can influence band shape. Velocity changes of molecules in the vapor state give rise to Doppler broadening. Radiation damping arises from the fact that a vibrating dipole emits energy, which effectively decreases the vi-brational amplitude. Collision broadening, which is the most significant effect, results from the perturbation of energy levels when molecules

1000 975 950 925 900 Fig. 6. Band shape analysis of methylene rock-wag region of an annealed sample of

poly(tetramethylene terephthalate) resolved using Lorentzian band contours. Abscissa, cm-1; ordinate, A.


collide. Although Doppler broadening is a Gaussian effect, its magnitude is not significant under normal sampling conditions. Both radiation damp-ing and collision broadening result in Lorentzian perturbations, which suggests that profiles of liquid samples are best expressed as Lorentzians. The distribution of molecular conformations found in some samples (e.g., polymers) results in yet another form of band broadening. Experimentally (Young and Jones, 1971), it has been proposed that optimum results are obtained when a Gaussian perturbation term is included. Spectra col-lected with very large time constants on dispersive instruments (Baker et al., 1978b) have asymmetric peaks. As early as 1938 Wulf and Deming noted that their observed spectra could be fit very well to a Lorentzian-Gaussian sum function. They concluded, "However, it seems probable that the much greater freedom introduced by this procedure should make it possible to fit most symmetrical curves of this type within the limits of error of such experimental measurements and that would sacrifice some of the significance of such analyses."

Before the application of digital computers, band shape analysis re-quired the use of graphic or hand calculation (Badger and Zumwalt, 1938; Wulf and Deming, 1938; Brode, 1945; Sheppard et aL, 1941; Hagenbach and Percy, 1922) methods. The introduction of the DuPont 310 curve resolver and other electronic analog curve resolvers (French et al., 1954) greatly increased the speed of calculations, although the results remained subjective. In an early study (Vandenbelt and Henrich, 1953) of ideal band shapes, a wide range of symmetric peak profiles were synthe-sized to investigate resolvability and frequency shifts arising from peak overlap.

Unlike linear regression, in which one is able to obtain relatively simple expressions for parameters in terms of observed data, the functions em-ployed to represent the band profiles require the use of iterative, nonlin-ear least squares procedures. Typically, one provides the program with initial estimates of the parameters, which are then modified in an iterative procedure. In the simplest case each peak is characterized by three pa-rameters related to intensity, position within the spectrum, and width. The computed band profile is a linear combination of the individual band envelopes,

[Ac] = Σ Wmi,#/,*im) (15)

where [Ac] is the calculated spectrum, P,· the peak profile function, /mi the maximum intensity of peak i, Hi the half-width of peak /, and vmi the frequency of maximum intensity for peak /. The objective is to minimize the difference between the calculated spectrum [Ac] and the observed


spectrum [A0]. This amounts to minimizing the squared norm of the resid-ual spectrum,

r2 = ([Ac] - [A0])'([AC] - [A0]) (16)

where r1 is the sum of squares residual, [Ac] the calculated spectrum, and [A0] the observed spectrum.

For each iteration one computes the residual spectrum for the current values of the peak variables. The parameter values are changed slightly in an effort to produce a better match of the calculated and observed spec-tra. Calculation of the Jacobian matrix [/] reflects changes in the residuals as a function of changes in the peak parameters,

R(X' + AX) - R(X') J{X'AX) = — ^ — (17)

where AX represents the changes to be made to peak parameters, and R is the residual spectrum, [Ac] - [A0].

The modified parameter residual calculations provide the basis for com-puting improved estimates of the peak variables (e.g., Brown and Dennis, 1972):

[X"] = [Χ'] - ([/)'] + [JY[J])-l[JY[R] (18)

where X" represents the improved estimates parameters, [Xf] the esti-mates of parameters from prior iteration, and [Df ] the damping factors for this iteration. If the root-mean-squared deviation of the calculated and observed band profiles is within the experimental error or the peak param-eters do not change significantly on successive iterations, then the al-gorithm has converged. Proper selection and modification of damping factors and parameter steps are important to ensure both rapid conver-gence and the location of the global minimum of the residual.

Examination of Eq. (15) indicates that this function is the sum of a set of nonlinear functions. Unlike the half-width and peak position variables, the maximum-intensity terms in this equation are essentially linear param-eters. By using a method proposed by Lawton and Sylvestre (1971a), one can reduce the number of nonlinear parameters being refined by one-third. Because the maximum intensity of each band profile can be fac-tored to produce a linear term in the expression, the intensity parameters can be computed using conventional linear regression equations for each set of half-width and peak position parameters.

Pitha and Jones (1966) compared a variety of refinement procedures utilizing several band envelope functions including sum and product com-binations of Lorentzian and Gaussian profiles. In addition, they incorpo-


rated corrections for finite slit width encountered in dispersive instru-ments into the refinement procedure. Maddams (1980) published an excellent review of band shape analysis containing an extensive list of references. The temptation to resolve broad-band profiles into numerous individual peaks in the absence of experimental evidence for the presence of underlying peaks should be resisted. Vandeginste and DeGalan (1975) critically reviewed band shape analysis procedures using both synthe-sized and experimental spectra. Not surprisingly, the initial estimates of parameters influence the rate of convergence, and in some circumstances procedures fail to converge if the starting values are very much in error relative to the true parameters. The possibility that there are several different sets of parameters (Pitha and Jones, 1966) that express the ex-perimentally observed profile suggests that the values of individual pa-rameters must be viewed with circumspection. Whenever possible, as much of the spectrum should be used in the band-fitting procedure as is possible. The effect of using narrow regions of the spectrum (Pitha and Jones, 1967) is to change the baseline correction and leads to results in which the Gaussian contribution is increased if Lorentzian-Gaussian sum functions are used for the refinement.

D. Self-Deconvolution

An experimentally observed spectrum (French et al., 1954) results from the convolution of the instrument line shape function with the true spectrum,

Sm(y) = I R(P - F)St(v') df (19)

where Sm(v) is the measured spectrum, St(v) the true spectrum, and R(v) the instrument response function. The broadening introduced by this con-volution degrades the resolution. Hence, the deconvolution process can be viewed as the inverse of smoothing (Vandenbelt and Henrich, 1953). Several workers (French et al., 1954; Lawton and Sylvestre, 1971a; Van-denginste and DeGalan, 1975; Jansson et al., 1970; Wertheim, 1975; Hal-sey and Blass, 1977; Jansson et al., 1968; Jansson, 1970) have expanded on the early work of van Cittert (Jones et al., 1967a,b; Morrey, 1969; Szöke, 1972), who proposed an iterative procedure for "unfolding" this convolution product,

■M*) = Sin_x{v) + a \sm{v) - \ R(v - v,)Stn_l(vf) d, (20)

where a is the relaxation parameter.


The instrument response function R(v) can be approximated experi-mentally by measurements made on an isolated single line (Lawton and Sylvestre, 1971a; Khidir and Decius, 1962). By comparing the results of the deconvolution procedure applied to spectra measured on a dispersive instrument to Doppler-limited spectra obtained using a difference-fre-quency laser, Pliva et al. (1980) were able to investigate the reliability of the technique. Some of the results of their investigation are presented in Fig. 7. Deconvolution of the spectrum collected with a grating instrument by using a Gaussian instrument line shape is seen to result in a dramatic decrease in linewidth. The resulting deconvoluted spectrum compares very favorably with a spectrum obtained using a difference-frequency laser. Halsey and Blass (1977) proposed a method in which a function is derived by successive self-convolution of the response function, which eliminates the need for iteration.

More recently, a number of authors (Horlick, 1972a,b; Kauppinen et al., 1981a,b,c; Jones and Shimokoshi, 1983; Goldman and Alon, 1973) have exploited the properties of the Fourier transform by taking advan-

3031.0 3032.0

Fig. 7. (A) Doppler-limited spectrum of C6H6 (T = 300 K, P = 0.5 torr, path = 2 m) obtained with grating instrument; (B) same spectrum after deconvolution using a Gaussian apparatus function; (C) spectrum measured using a difference-frequency laser system. From Pliva et al. (1980).


tage of the fact that convolution and multiplication are FT pairs (Brigham, 1974). Equation (19) can be rewritten as

5m(8) = R(8)St(8) (21)

where 5m(6), R(d), and St(d) are inverse Fourier transforms of the func-tions. Hence, the deconvolution process can be expressed as a simple division followed by Fourier transformation:

St(6) = Sm(d)/R(d) (22) St(v) = FT[Sm(S)/R(S)] (23)

The extent to which the true spectrum can be recovered (Horlick, 1972b) is limited by the presence of noise. If the instrument slit width is greater than the linewidth being observed, then complete recovery of the true spectrum is fundamentally impossible (Wertheim, 1975; Horlick, 1972b). Excessive noise can be compensated for by introducing an apodization function to control the degree of line sharpening produced by the decon-volution process. A study of a wide range of apodization functions (Kaup-pinen et al., 1981c) revealed that Bessel, triangular, cosine, and sine2

apodization provided good S/N ratios while at the same time maintaining the resolution enhancement.

Another proposed method makes use of derivatives (Hardy and Young, 1949; den Harder and DeGalan, 1974), which enables the deconvolution to be done in real time if the spectrum is collected on a dispersive instru-ment. Although true deconvolution would require the calculation of an infinite number of derivatives, den Harder and DeGalan (1974) showed that good results can be obtained using only the first and second deriva-tives. (The inclusion of higher-order derivatives serves only to increase the noise level in the deconvoluted spectrum.)

Kauppinen et al. (1981b) extended the application of deconvolution to include the resolution of multicomponent band contours in condensed-phase spectra. Matching the response function to a single peak in a com-plex band profile (Kauppinen et al., 1981b) enables one to remove bands selectively in the overlapped spectrum.

E. Interpolation

There exist a number of procedures for interpolating points to enhance resolution artificially. Although such algorithms can certainly produce results that are pleasing to the eye, they should, in general, be avoided because no additional information is obtained from their application. The simplest method involves the use of fitting a polynomial to known points. The disadvantage of this technique is that discontinuities can exist in the


derivative of interpolated function at the end points. Spline functions do not suffer from this drawback, for by definition they require that both the slope and curvature be the same at the end points of each pair of cubics. Spline functions do, however, have a tendency to produce spurious results at points where the derivative changes sharply. Many graphics terminals have hardware that will fit cubic splines to data. A simple proce-dure with FT-IR instruments for interpolation in an entire spectrum is to add zeros to the end of interferograms before computing the Fourier transform (Griffiths, 1975). The results of zero filling are depicted in Fig. 8, in which the spectrum of polystyrene has been computed both with zero filling by a factor of 4 and without. (It is common practice to double the file size of an interferogram before computing the transform so that the number of points in the transformed spectrum will be the same as that in the original interferogram, because half of the points are "lost" in taking the Fourier transform.) Giancaspro and Comisarow (1983) com-bined zero filling with several interpolation functions in a study involving FT-IR spectra. They recognized two distinct cases depending on the val-ues of T (the acquisition time) and r (the relaxation time of the time domain signal). If T/r < 2, then optimum results were obtained with a parabolic interpolation function. For Τ/τ > 2, however, Lorentzian inter-polation is recommended. ,

F. Smoothing

Spectral smoothing (Savitzky and Golay, 1964; Bromba and Ziegler, 1981; Willson and Polo, 1981; Edwards, 1982; Ziegler, 1981; Schau, 1979; Whitbeck, 1981; Willson and Edwards, 1976) represents the inverse of interpolation in that it enhances the S/N ratio at the expense of resolution. This is achieved by convolution of the spectrum with a filter function. One method of smoothing that has been popular in the past involves the use of resistor-capacitor (RC) circuits. Such electronic smoothing has the unde-sirable property of introducing frequency shifts and asymmetric peak distortions. In 1964 Savitzky and Golay proposed the use of digital smoothing polynomials (DISPO) in an excellent paper that also includes FORTRAN subroutines. [A later work (Steiner et al., 1972) contains corrections for several of the tables found in Savitzky and Golay (1964).] Their approach is based on computing a polynomial of degree n through 2ra + 1 consecutive points, where n < 2m + 1. The DISPO approach involves computing the coefficients of a polynomial having the best mean square fit based on the points contained in a narrow window of the spec-trum. This polynomial can then be used to calculate an ' improved" value for the central point in the window. Although the procedure may seem


AJ 2000 1700

Fig. 8. Interpolation via zero filling. Abscissa, cm-1. Top: spectrum of polystyrene collected at resolution of 8 cm 1 and transformed with no zero filling. Bottom: same spec-trum but transformed after zero filling x8.

complex, the problem reduces to the use of a set of optimum convoluting integers to form a weighted average in the vicinity of each point in the original spectrum. On the basis of comparisons of theoretical band shapes using RC and DISPO filters, Ziegler (1981) concluded that a fourth-order DISPO is as much as two orders of magnitude better than a conventional RC filter. This technique has also been extended (Edwards, 1982) to two-dimensional data. Schau (1979) pointed out that in FT-IR smoothing can be achieved by apodization of the interferogram. A wide range of smooth-ing functions have been compared in a review article by Willson and Edwards (1976). Fourier transform methods (Betty and Horlick, 1976; Horlick, 1972a) have also been employed.

Although smoothing certainly produces results that are pleasing to the eye, its use should be avoided unless absolutely necessary, particularly when more advanced data processing techniques are to be applied to the spectrum.

G. Baseline Correction

It is critical that the background component be properly compensated for in advanced spectroscopic data manipulation procedures that require strict adherence to Beer's law. This is particularly true for samples run as KBr pellets or polymer films in which the particle size results in light scattering. For narrow regions of a spectrum, it is often sufficient to subtract a wedge from the original absorbance spectrum. When a broad


range of the spectrum is to be used, the background cannot be properly expressed in terms of a single linear baseline. One technique that has been utilized for x-ray diffraction data (Goehner, 1978) fits a series of linear baselines over the entire spectrum. A more attractive approach is to employ ultralow-frequency filtering (Atakan et al., 1980) of a Fourier transformed spectrum:

F = 0.5{1 + tanh[a(i; - v0]} (24) Unfortunately, this technique requires the somewhat subjective variation of several parameters, which could result in the loss of some spectral information. Statham (1977) proposed an algorithm that utilizes "top hat" digital filters to compensate for background effects encountered with x-ray fluorescence and γ-ray spectra.

It is often best, however, to include parameters to compensate for variations in baseline as variables to be refined in other procedures (e.g., band shape analysis or least squares curve fitting). With such approaches one effectively eliminates the often subjective decisions required to define a baseline.

IV. DATA PROCESSING ROUTINES FOR THE QUANTITATIVE ANALYSIS OF MIXTURES USING MULTIPLE SPECTRA

A. Spectral Stripping (Subtraction)

Quite often, one is confronted with the problem of analyzing the spec-trum of a mixture when only some of the components are known. In the case of a binary mixture the observed spectrum can be viewed as the sum of the two pure-component spectra: [M] = [P\] + [P2]. Rearrangement of this equation in terms of the known spectra [P\] and [M] permits one to obtain the spectrum of the unknown: [P2] = [M] - [P\], This analysis assumes that the concentration and path length of the known pure compo-nent are identical to those in the mixture. Early methods (Powell, 1956; Schnumann and Kendrick, 1954; Coates, 1978) for subtraction utilizing double-beam spectrometers were predicated on the tedious task of varying the cell path length of the known pure component. The digitization of absorbance spectra enables one to subtract the ir spectra of known com-ponents) of a mixture selectively to reveal underlying spectral features indicative of unknown pure compounds or interactions (Koenig, 1975; Strassburger and Smith, 1979; Gendreau and Griffiths, 1976; Hirschfeld, 1976f; Shurcliff and Steams, 1949).

Instrumental requirements have been summarized (Hirschfeld, 1976f;


Lynch and Brady, 1978), both in terms of S/N ratio and wavenumber reproducibility. Brown et al. (1982) have also studied the effect of fre-quency shifts arising from either the sample or the instrument in differ-ence spectra. Sample wedging (Hirschfeld, 1979b) can produce similar anomalies within difference spectra. The effects of apodization (Anderson and Griffiths, 1978; Griffiths, 1977) can produce artifacts, especially if it becomes necessary to substantially scale a spectrum. For weakly absorb-ing species, boxcar apodization appears to produce better compensation than triangular apodization (Anderson and Griffiths, 1978). Differences in the real part of the refractive index between samples can produce spuri-ous difference spectra (Allara, 1979). Jones et al. (Ribbegard and Jones, 1980; Goplen et al., 1980; Hawranek and Jones, 1976) have discussed practical methods for the evaluation of optical constants.

Typical interactive real-time graphics procedures require that the user select some region of the ir spectrum in which the known pure component has a band that is well resolved in the mixture. The computer then slowly varies the parameter by which the known spectrum is called, displaying a difference plot on a graphics terminal. When the band has been correctly compensated for (i.e., nulled), the user enters a command that stops the procedure to determine the proper scaling coefficient. For complicated mixtures it is not uncommon to repeat the procedure several times by removing each known pure component from the preceding difference spectrum until all the known compounds have been stripped from the mixture. Interactive procedures suffer from the fact that a subjective judgment must be made in determining each scaling coefficient. Conse-quently, results are not always reproducible, especially in instances in-volving multiple subtractions to reveal very weak underlying spectra. More recently, Gillette and Koenig (1984) proposed an iterative least squares procedure for the simultaneous optimization of the subtraction parameters of several pure-component spectra. This approach involves the use of least squares curve-fitting techniques and is discussed in Sec-tion IV,C.

B. Ratio Method

One of the earliest techniques for the extraction of pure-component spectra from mixture spectra was proposed by Hirschfeld (1976a) and later applied by Koenig et al., (1977). [Related approaches were described earlier (Shurcliff and Steams, 1949; Hirt et al., 1954) but were not de-signed to extract the entire pure-component spectrum.] The ir spectrum of a two-component mixture can be viewed as arising from a linear combi-nation of its constituent spectra,


[M,] = [ft] + [ft] (25) where [Mx] is the spectrum of mixture 1, and [ft] and [ft] are the spectra of the pure components. If one is able to prepare another mixture in which the concentrations of the pure components are different, then the corres-ponding mixture spectra may be expressed as

[M2] = *,[ft] + s2[P2] (26) where s\ and s2 are the scaling coefficients reflecting the different concen-trations of [P^ and [P2] in [M2].

Rearrangement of these equations leads to relationships defining the pure-component spectra in terms of the mixtures

[/Ί ] = — Γ Τ [M2] - - ^ - [Μ, ] (27a) Si S2 S\ — S2

[Pi] = —^— [M2] - —ii— [M,] (27b) S2 — S\ Si — S\

The scaling coefficients (s\) and ( 2) are derived by calculation of the "ratio spectrum" from the two mixtures:

roi _ [M2] _ sdPi] + s2[P2] [R] ~ \m " [Pii + [ft] (28)

In a spectral region where [P\] > [P2], [R] is approximately equal to (s\). Conversely, if [ft] > [ft], then [/?] is approximately equal to (s2). An implicit assumption is that each pure-component spectrum contains a characteristic peak that is not overlapped by peaks appearing in the other pure component(s). The value of the ratio spectrum in spectral regions where both mixtures have low absorbances can be misleading. By the introduction of relative concentrations into the preceding equations, the approach enables one to do quantitative analyses without external cali-bration (Diem and Krimm, 1981; Koenig and Kormos, 1979).

This procedure is illustrated in Figs. 9 and 10 for two mixture spectra synthesized from two Lorentzian peak profiles. Selection of the maximum and minimum points in the ratio spectrum (Fig. 9) serves to extract the scaling coefficients (s\) and (s2). Substitution of these values in Eqs. (27a) and (27b) results in the extraction of the two pure-component spectra (Fig. 10).

C. Least Squares

Quantitative analysis of mixtures in which the spectra of the chemical constituents are known is best performed by the use of linear least


Fig. 9. Application of the "ratio method" for pure-component extraction. Top: mix-ture 1; middle: mixture 2; bottom: ratio mixture 1/mixture 2.

squares procedures (Blackburn, 1965; Antoon et aL, 1977; Haaland and Easterling, 1980, 1982; Christian and Tucker, 1982; Willis et aL, 1970; James, 1981). The mathematical foundations of these techniques can be found in an excellent article by Blackburn (1965), which includes a de-tailed example that can be worked out on a hand calculator. When Beer's law is obeyed, a mixture spectrum [M] can be expressed as a linear

Fig. 10. Extracted pure components using ratios obtained from Fig. 9. (Top) Extracted pure component 1: M2[l/(2.41 - 0.42)] = M![0.42/(2.41 - 0.42)]. (Bottom) Extracted pure component 2: M2[l/(0.42 - 2.41)] = M,[2.41/(0.42 - 2.41)].


combination of pure-component spectra [P] as follows: [M] = [P][C] (29)

In the past Cloy et al., 1979; Nielsen and Smith, 1943) the scaling coeffi-cients [C] were computed by selecting the absorbances in each of the spectra at as many frequencies as were needed to determine the number of pure components. A set of linear equations could be set up and the matrix [C] derived (Barnes et al., 1943). The presence of random error in the spectra suggests that better results could be obtained by utilizing all the information within the spectra. This results in a set of linear equations in which the concentration matrix is overdetermined. To compute the "best" values of the concentrations, one seeks to minimize the sum of the squared differences between the observed mixture and that computed using the pure-component spectra:

r= Σ ("ii ~ Σ CjPij)1 (30) * j

Differentiating the residual (r) with respect to each of the concentrations and setting each of the resulting equations equal to zero permits one to derive a set of linear equations in which the concentrations are strictly determined:

2/?/i(m, - ci/?/i - · · · - cqPii)

: : ( 3 1 )

IpiqiWi ~ C\Piq - · · · - Cqpiq)

For the case in which all the absorbances are weighted equally, the solu-tion of Eq. (31) produces the least squares coefficients [C] , which are computed as

[C] = ([Ρ]'[Ρ])-ι[ΡΥΙΜ] (32) This approach was first applied to the analysis of spectroscopic data by

Sternberg et al. (1960) for a five-component mixture using uv data. Haaland and Easterling (1980, 1982) expanded on the earlier work of Antoon et al. (1977) utilizing FT-IR spectra. They proposed a number of variations in an effort to compensate for baseline differences among the pure-component spectra and suggested a different weighting scheme in addition to including a threshold parameter to select frequencies to be used for reducing the number of calculations. Optimum results are ob-tained when as much of the spectrum as possible is included in the analy-

dr

dC] = ο = Σ -


sis (Zscheile et ai, 1962). Rather than include specific terms to account for baseline variations, one may perform the regression analysis using derivative spectra (James, 1981) in which linear baseline differences are effectively eliminated. Leggett (1977) proposed a modified linear least squares procedure that restricts concentrations to nonnegative values.

A number of statistical tests (Hemmerle, 1967) can be readily com-puted. The multiple correlation coefficient is an overall figure of merit, with 1.0 representing perfect correlation:

R 2 — Σ (Mc - Mmf Σ (M0 - Mmf (33a)

Here, R2 is the multiple correlation coefficient squared, M0 the observed spectrum, Mc the calculated spectrum, and Mm the mean spectrum.

Fr = _m, 1) (1 - R2)/(n - q)

(33b)

Here, Fc is the F statistic with (q - 1) and (n - q) degrees of freedom, q the number of pure-component spectra, and n the number of points in the spectrum.

An example of the application of this procedure is depicted in Fig. 11. Least squares fitting of the spectrum of an antioxidant and pure butadiene rubber to a sample containing both components enables the amount of antioxidant in the sample to be determined. Comparison of the spectrum calculated using the least squares coefficients indicates virtually a perfect match to the mixture spectrum.

Fig. 1 1 . Least squares analysis of a polymer-antioxidant system. Abscissa, c m ' . Curve A, Bu-tadiene rubber containing 6.4 wt % CA-l antioxi-dant (solid line); best fit of pure butadiene rubber + pure antioxidant (dotted line). Curve B, Pure buta-diene rubber. Curve C, CA-l antioxidant. From Antoon et al. (1977).

UILAL

1800 600


Equation (32) assumes that the spectra for all the compounds in the mixture are known, which quite often is not the case. Nevertheless, the procedure for computing the scaling coefficients can provide valuable information if there are regions in the mixture spectrum in which absorb-ances arise primarily from the known compounds. For example, by using a region of the spectrum containing only a characteristic solvent band, one could obtain the scaling coefficient to subtract the solvent from a solute-solvent spectrum. By the use of an iterative algorithm (Gillette and Koenig, 1984), it is possible to identify such domains within the mixture spectrum in a completely automated fashion. To initiate the procedure, a least squares fit of the known compounds to the mixture is done utilizing the entire spectrum. Regions in which the calculated spectrum is consid-erably less than the observed spectrum are located and omitted from the next least squares cycle. This process continues until only regions of the mixture spectrum that can be well accounted for by the known spectra are used in the calculation. As the refinement progresses, the algorithm be-comes increasingly selective with respect to the regions of the spectra used in the calculations.

One obstacle in the approach is the determination of convergence crite-ria. It is unreasonable to continue iterating as long as the root mean square deviation improves, because the greater the number of points rejected, the better the fit. (In the worst case one would be using as many points as known spectra, which would lead to a perfect fit.) In general, one observes a dramatic decrease in the root mean square deviation dur-ing the first several cycles followed by a more gradual decrease. If the root mean square deviation does not improve by more than 10% between two successive iterations, then the process has converged. Practical ap-plication has demonstrated that scaling coefficients for the pure-compo-nent spectra are often underestimated during the early part of the refine-ment when a variable linear offset baseline term is included. Under these circumstances the residual spectrum contains band profiles similar to those observed in second-derivative spectra. If the number of minima increases from one iteration to the next, then the procedure has likely overcompensated for the pure components, and the results from the pre-ceding iteration represent the proper scaling coefficients.

A simple example of the technique is presented in Fig. 12, in which the spectrum of a known component (indicated by the single peak) is fitted to a mixture and subsequently subtracted to reveal the underlying unknown pure component. Successive iterations reject regions in which the fit is particularly poor. In this example the predicted scaling coefficient was less than 2% in error.

Unlike conventional subtraction procedures, which require that each


Λ Λ

MIXTURE / l / l

ITERATION 1 1 ~ ^ ^ / V v ^ ^ - — ^ _

ITERATION 2y/^~.-:\'.'·''''' ^ ^ - — ^ ^ J r~~"——~ __^^^ ^ * · ^ - ^ - ^ ]

[ITERATION 3 1

1 1 1 1 1 1

ACTUAL PURE COMPONENT

EXTRACTED PURE COMPONENT

1000 920 900

WAVENUMBERicm"1)

Fig. 12. Automated spectral subtraction, (a) Results of three iterations using modified linear squares curve fitting of a known pure component to a mixture. Points marked by dots signs not used in refinement, (b) Extracted pure component using scaling coefficients ob-tained from iteration 2.


known spectrum be sequentially subtracted from the mixture, this ap-proach permits the simultaneous optimization of subtraction scaling pa-rameters for all known compounds. The procedure utilizes all of the infor-mation present in regions of the spectrum in which the known pure-component spectra overlap and the unknown spectrum does not absorb. Furthermore, it circumvents the often subjective decisions that must be made in interactive subtraction procedures.

D. Factor Analysis

Situations in which one is attempting to analyze a series of mixtures without a knowledge of the number of pure components are the most complicated that one is likely to encounter, and also among the most common. Such problems are best addressed by a systematic analysis of the spectra in which one answers the following questions: (1) How many pure components are involved? (2) What are the pure components? (3) How much of each pure component is present in the mixtures?

The mathematical framework for these analyses can be found in factor analysis. This approach has been applied to a wide range of problems in which there is a linear relationship between the variables, including mass spectroscopy (Knorr and Futrell, 1979; Malinowski, 1978, 1982; Ritter et al., 1976a; Rozett and Petersen, 1975a,b, 1976) GC-MS (Sharaf and Ko-walski, 1982; Knorr et al., 1981), chromatography (Malinowski, 1982; MacNaughtan et al., 1972; Howery et al., 1974; Selzer and Howery, 1975; Kindsvater et al., 1974; Weiner and Howery, 1972; Weiner and Parcher, 1972, 1973; Howery, 1974; Weiner et al., 1972, 1974; McCloskeg and Hawkes, 1975), nuclear magnetic resonance (Malinowski, 1978, 1982; Weiner et al., 1970; Weiner and Malinowski, 1971), fluorescence (Warner et al., 1977; Weber, 1961; Knorr and Harris, 1981), polarography (How-ery, 1972), potentiometric measurements (Vadasdi, 1974) x-ray photo-electron spectroscopy (Gilbert et al., 1982), uv (Ohta, 1973; Edward and Wong, 1977), and ir (Antoon et al., 1979; McCue and Malinowski, 1981; Koenig and Tovar, 1981; Rasmussen et al., 1978; Chen and Gardner, 1983; Bulmer and Shurvell, 1973a,b, 1975; Shurvell et al., 1976; Shurvell and Bulmer, 1976; Shurvell and Dunham, 1978; Korppi-Tommola and Shurvell, 1979; Petelenz and Shurvell, 1980). Shurvell et al., 1976; Bulmer and Shurvell, 1973a,b, 1975; Shurvell and Bulmer, 1976; Shurvell and Dunham, 1978; Korppi-Tommola and Shurvell, 1979; Petelenz and Shur-vell, 1980) have utilized factor analysis in conjunction with band shape analysis to investigate a number of solution equilibria problems with ir data. Wernimont (1967) has made use of factor analysis to evaluate spec-trophotometer performance. For a more detailed description of chemical


applications of factor analysis the reader is referred to Malinowski and Howery's text (1980).

Even determining the number of pure components involved can be an arduous task if done by inspection due to the extensive band overlap. This step of the procedure can be viewed by ascertaining the minimum number of spectra that, when scaled and added together, will express any spec-trum in the series of mixtures within the bounds of the experimental error. More mixtures than pure components must be available. Early research-ers (Ainsworth, 1961, 1963; Katakis, 1965) performed rank analyses di-rectly on the mixture matrix. Rather than attempting to form conclusions based on isolated or narrow regions of the spectrum, a more desirable approach is to make use of all the measured absorbances for each spec-trum. For (m) mixture spectra, each containing (n) points, the initial prob-lem of calculating the number of pure components reduces to determining the rank of an (m) x (m) matrix (in general, the number of frequencies is much greater than the number of mixtures):

[C] = [MY[M] (34)

Here, [C] is the covariance matrix, and [M] the spectra of mixtures. It should be noted that several other forms of data "compression" exist

(Rozett and Petersen, 1975a). Under some circumstances it is desirable to normalize each spectrum by the square root of the sum of squared absor-bances to form a correlation matrix. The average value for each spectrum can also be subtracted to perform covariance or correlation about the mean. The later form of processing, however, is generally not applied to spectroscopic data, because it eliminates the physically meaningful refer-ence of zero absorbance units. Statistical weighting schemes (Cochran and Home, 1977) have been shown to be particularly useful if there is a strong frequency-SVAf ratio dependence.

One means of determining the rank of the covariance matrix is to solve the eigenvalue problem

[C][E] = [Ε][λ] (35)

where [E] is the eigenvector matrix, and [λ] the diagonal matrix of eigen-values. Simonds (1963) described an iterative method for obtaining the eigenvalues and eigenvectors of Eq. (35) that is well suited to computer implementation in a paper that also includes a simple numerical example. The eigenvectors form an orthonormal set of basis vectors, with the mag-nitude of each eigenvalue being related to the relative importance of its respective eigenvector in the solution. In the absence of error, the number of pure components is simply equal to the number of nonzero eigen-


values. This is rarely the situation in practice. The contribution of noise to the spectrum is generally small and as such results in eigenvalues of magnitudes that are considerably less than those arising from "real" spectral features.

One method of determining the number of pure components is to con-sider which eigenvalues are required to represent the data matrix within the bounds of experimental error. The residual standard deviation, or real error (Bulmer and Shurvell, 1973a; Kankare, 1970) provides an indication of how well the spectra can be represented using a particular set of eigen-values and eigenvectors by measuring the difference between the error-free pure-component spectra and original mixture spectra. The effect of dropping eigenvalues and eigenvectors from the solution can be directly measured through the root mean square error, or "extracted error." To compute the percent contribution of a particular eigenvalue in the overall solution (Rasmussen et ai, 1978), the eigenvalue is divided bythe sum of all the eigenvalues. By summing all the eigenvalues used in the solution and dividing by the sum of the eigenvalues, one can compute the cumula-tive percent variance. Chi-squared tests have also been suggested (Bulmer and Shurvell, 1973a; Hawkins, 1974; Hugus and El-Awady, 1971). Several authors (Jackson and Hearne, 1973; Anderson, 1963) have investigated the statistics governing the distribution of eigenvalues. A number of relationships based on an analysis of the eigenvalues to deter-mine the number of pure components have been proposed by Malinowski (1977a,b,c, 1978) (Table 1). If the error associated with each spectrum is known, then the interpretation of the results from these procedures is straightforward.

Unfortunately, the experimental error is very much a function of both the sample itself and the parameters used to measure the spectra. If there are only subtle differences between the mixture spectra, then the determi-nation of how many pure components are present can become subjective. One method of circumventing the problem of inexact knowledge of the experimental error is to compare factor analyses based on two sets of spectra measured by coadding a varying number of scans (Antoon et aL, 1979). The values of those eigenvalues corresponding to "real" pure com-ponents that result from carrying out factor analyses on these different scan sets will be approximately the same. The increased S/N ratio arising from the coaddition of more scans decreases the values of the "noise" eigenvalues relative to the eigenvalues associated with the spectra col-lected with a smaller number of scans. Hence, the real eigenvalues or eigenvectors can be deduced by a comparison of the eigenvalues obtained for the two data sets.

1 Infrared Spectral Data Processing

TABLE 1

Factor Analysis Error Functions

Γ i m Ί i/2 R E = —t ϊ Σ kJ

ln(m - p) - J " , J\

Real error

7 = P + I

IE = RE(p/ra)1/2 Imbedded error

IND

i/2 Extracted error m

RE Indicator function XE = R E ' M - p

(m - p) Variance

VAR = X / - / 2 λ,-

P . m Cumulative percent variance CumVAR = 2 kj/ Σ λ7

j=l ' 7=1

where m = number of spectra n = number of points per spectrum p = number of pure components \j = eigenvalues

The capacity of factor analysis to detect the correct number of pure components is dependent on an assumption that is often overlooked in the interpretation of results. A component can be distinguished only if its concentration in the series of mixtures varies relative to the other pure components. For example, consider the following reaction mechanism, which proceeds slowly enough that spectra can be measured during the course of the reaction:

A + B > C (36)

If A and B are present in stoichiometric amounts, then only two pure components (i.e., A 4- B and C) will be detected. Although both A and B have characteristic spectra, factor analysis can recognize only that the composite spectrum A + B is varying under these circumstances. Factor analysis determines the number of spectroscopically identifiable, linearly independent variables in the vector space formed by the mixture spectra.

For reasons that will become apparent, it is desirable to obtain a set of basis vectors in the spectral domain. This can be achieved by multiplying the original mixture matrix by the real eigenvector matrix,

[A] = [M][Ef] (37)


where [A] represents the abstract eigenspectra, and [Ef] the "real" eigen-vectors. Because the eigenspectra represent a basis for the mixtures, they contain all the spectral features necessary to express any of the mixtures, although it is not in a physically meaningful state.

Rearrangement of Eq. (37) leads to an expression of the mixture spectra in terms of the eigenspectra,

[Λί'] = [A][E']< (38)

where [Mf] represents the "improved" mixture spectra. The mixture spectra calculated with Eq. (38) are not identical to the starting spectra. Rather, these spectra actually have a higher S/N ratio than the original spectra, because some of the error has been removed by dropping the noise eigenvalues and eigenvectors from the reconstruction (Kankare, 1970; Gillette and Koenig, 1982). This procedure is illustrated in Fig. 13. By using only the two real eigenvectors to back-calculate 1 of the mix-tures in a series of 10 mixtures, the S/N ratio is seen to improve dramati-cally.

The identification of the pure components in the mixtures can take several forms, depending on the assumptions one is willing to make. Known spectra can be tested to determine if they are present in the mixtures. Under some circumstances the pure-component spectra can be extracted by the use of nonorthogonal rotations of the eigenspectra.

'Target testing" (Malinowski, 1978; Rozett and Petersen, 1975a; Wei-ner et al., 1970; McCue and Malinowski 1981; Malinowski and McCue, 1977; Roscoe and Hopke, 1982) provides a means of checking for the presence of individual known pure-component spectra in the mixtures. This procedure is especially valuable when used in conjunction with a spectral library (Gillette et al., 1982b). Unlike other library searching algorithms, target testing is not restricted to identifying unknowns con-taining only one component (i.e., a pure unknown). The basis of this technique represents something of an abstract converse of Beer's law. Just as a mixture can be expressed as a linear combination of its pure components, a pure component can be expressed as a linear combination of a series of mixtures. If one is able to reconstruct a particular pure-component spectrum using the eigenspectra within the margin of experi-mental error, then that pure component is present. One implementation of these concepts involves computing the sum of the squared differences between the least squares fit of the eigenspectra and each of the library spectra:

n = ([A][S] - [LM[A][S] - [L^) (39)

[S] = ([AY[A])-l[AY[Li] (40)


*$ψ* ^ l·

f\ RECONSTRUCTED SPECTRUM

Λ

I Hill11)1

:||MyV4

ORIGINAL SPECTRUM

|Λ# tH.»':

Λ ABSTRACT SPECTRUM 1 J X

ABSTRACT SPECTRUM 2

Ψ^^Ψ^ψΜ%^™ V » f t Fig. 13. Results of using two eigenvectors to reconstruct a spectrum from a series of 10

mixtures.

Here, r, is the residual of comparison between the /th library spectrum and eigenspectra, [L,·] the /th library spectrum, and [5] the least squares scal-ing coefficient matrix.

Before an attempt is made to extract the pure-component spectra, it is helpful to understand the significance of the eigenvectors. With the aid of simple matrix algebra, the physical meaning of the eigenvectors can read-ily be derived. The matrix spectra in Eq. (34) can be represented as a linear combination of pure-component spectra: [M] = [PILK]. Substitut-ing this relationship in Eq. (34) yields


[C] = ([P][K])'([P][K]) (41)

Taking advantage of the fact that the transpose of an orthonormal matrix is equal to its inverse, we can rearrange Eq. (35) to

[E]'[C][E] = [λ] (42)

Substituting the expression for [C] in Eq. (41) into the preceding equation,

[Ε]'([Ρ][Κ])'[Ρ][Κ])[Ε] = [λ] (43a)

([P][K][E])'([P][K][E]) = [λ] (43b)

[K][E] = [T] -> [K] = [T][EY (43c)

The concentrations of the pure components in the mixtures can be ob-tained by a linear transformation of the eigenvectors. In a sense, the eigenvectors are abstract representations of concentration. For a simple two-component case in which all mixtures have the same path length, a plot of the ordered pairs formed by the two real eigenvectors will lie on a straight line. The coordinates of the points lying on this line represent the scaling coefficients by which to multiply the eigenspectra to produce mix-ture spectra of all possible concentrations of the two pure components for a particular path length. Only a small segment of this line, however, has physical meaning. Scaling coefficients that reflect negative concentrations of pure components also lie on the line. Hence, the problem is to identify the two points on the line that represent the scaling coefficients that will form the pure-component spectra. Combinations of eigenspectra resulting in spectra having negative absorbances clearly have no physical meaning,

e\an + e2ai2 ^ 0 (44)

where β], e2 are the elements of an ordered pair lying on the concentration line. This can be rearranged to yield

an/ai2 > ~e2lex (45)

Examination of the eigenspectra ratio spectrum can be used to obtain ratios that impose boundary conditions on the concentration line. If it is assumed that each pure-component spectrum has a characteristic peak (i.e., a peak not overlapped by peaks in the other pure-component spec-trum), then the points at the intersection of the boundary lines and the concentration lines represent the scaling coefficients that will produce the pure-component spectra from the eigenspectra.

The aforementioned extraction procedure requires some modification for use with spectra collected at different path lengths. One solution to this problem is to normalize each spectrum by dividing each point by the


square root of the sum of the squared absorbances (i.e., factor analysis using a correlation matrix as opposed to a covariance matrix). For the two-component case the eigenvector pairs will lie on a parabola. The lines computed from Eq. (45) can then be used to obtain the scaling coefficients for the calculation of the pure-component spectra. Relative concentra-tions of the pure components in the mixtures can then be calculated.

A number of authors (Knorr and Futrell, 1979; Malinowski, 1982; Sharaf and Kowalski, 1982; Knorr et al., 1981; MacNaughton et al., 1972; Gilbert et al., 1982; Ohta, 1973; Lawton and Sylvestre, 1971b; Gillette et al., 1983; Martens, 1979) have dealt with the problem of extracting pure components using algorithms similar to that just described. Although other procedures have been developed for rotating eigenvectors (e.g., varimax and quartimax (Rozett and Petersen, 1975a), these approaches can produce physically meaningless results in the form of negative ab= sorbances or concentrations. From a spectroscopic standpoint the objec-tive of varimax is not valid, because the pure-component spectra are not necessarily orthogonal. Swain et al. (1979) have discussed some of the problems involved in eigenvector rotations.

A knowledge of reaction or kinetics mechanisms can be utilized to eliminate the characteristic peak restriction imposed by the aforemen-tioned procedure (Kankare, 1970; Sylvestre et al., 1974) and at the same time compute rate constants. The solution for the pure-component spec-tra under these circumstances generally necessitates the application of nonlinear least squares refinement procedures, although in some in-stances (Sylvestre et al., 1974) the number of parameters may be reduced by combining both linear and nonlinear least squares optimization.

To illustrate the underlying principles of factor analysis the four spectra depicted in Fig. 14 were synthesized by scaling the spectra of two Lorentzian peak profiles and adding random noise with a standard devia-tion of 1 unit. All of the various statistical tests (Fig. 15) indicate two pure components for this series of mixtures. The real-error function is in very close agreement with the known standard deviation of the data for a value of two pure components. Similarly, the indicator function possesses a sharp minimum for two components. Calculation of the abstract eigen-spectra yields the two spectra in Fig. 16, which contain all of the spectral features necessary to represent any of the mixtures. A plot of the four ordered pairs represented by the two primary eigenvectors produces the linear relationship seen in Fig. 16. By examining the spectrum of the product of the two eigenspectra and evaluating the ratio of the eigenspec-tra at the extrema, one can compute the two boundary lines in Fig. 17. The coordinates at each of the points of intersection of the abstract con-centration line and bound lines serve as the scaling coefficients by which


Fig. 14. Simulated spectra of four mixtures generated by adding two Lorentzian peak shapes in the ratios (A) 0.2:0.8, (B) 0.4:0.6, (C) 0.6:0.4, and (D) 0.8:0.2 and adding random noise with a standard deviation of 1 unit.

to multiply the eigenspectra to obtain the pure-component spectra in Fig. 18. A flow chart of the steps involved in factor analysis is presented in Fig. 19.

E. Cross Correlation

The quantitative measurement of small quantities of a material in a complex mixture represents one of the most difficult problems. Cross correlation (Mann et ai, 1982; Horlick, 1973; Hieftje, 1972; Davies, 1970) provides one means of addressing it. The correlation function is defined (Brigham, 1974) as

Z{t) = j Al(r)A2(t + r) dT (46)

where Z(t) is the cross-correlation function, and A\(t), A2{t) are the spec-tra being correlated. In its simplest form, cross correlation can be used to

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Fig. 18. Extracted pure components using coordinates defined by intersection of line in Fig. 17 with the boundary.

detect the presence of a compound in a noisy baseline. This involves cross correlating a high S/N ratio spectrum (e.g., as collected with a longer path length or higher concentration) of the compound with the poor S/N spectrum. A statistically significant nonzero value of the cross-corre-

I Input Spectra of Mixtures I

' I — ' I Compute Covariance/Correlation Matrix I

I I Solve for Eigenvalues/Eigenvectors I

' I — " *

I Determine # of Pure Components I

I Compute Abstract Eigenepectra l _

. ' I '

Reconstruct Improved Mixture Spectra

I Extract Pure-Component Spectra I I Target Test for Pure Spectra (Library Search)

Compute Concentrations in Mixtures

Fig. 19. Overview of factor analysis.

IL JUw. y ■ «. U' - JL-v — . X*-*r*

u ■

J\J^_ \ I 3437 3451 3465 3479 3493

A

A U \ 1 ^ ^ A L \ A Λ!\ΑΧΛΛΛ JIAJLJUAKTLAAJ

c Ji JIAJIIL,Jl AAJVAA/U JUIXJU - t z*0 +t

Fig. 20 . Top: emission spectra of (A) cobalt, (B) nickel, and (C) iron. Bottom: cross correlation of cobalt spectrum with (A) cobalt, (B) nickel, and (C) iron. From Horlick (1973). Copyright 1973 American Chemical Society.


lation function at (/) = 0 indicates the presence of a component. For nonzero values of (t), the cross-correlation function provides information related to relative peak positions in the two spectra. Hence, one is taking advantage of the information present in one spectrum to enhance artifi-cially that of a second spectrum. Although there are methods for directly recording cross-correlation spectra (Davies, 1970) using modified interfer-ometers, under most circumstances it is easiest to carry out the calcula-tions in Eq. (46).

An illustration of cross correlation is presented in Fig. 20, in which the emission spectrum of cobalt has been cross-correlated with the spectra of cobalt, nickel, and iron. Examination of the cross-correlation spectra reveals a strong peak at τ = 0 for the cobalt/cobalt pattern, as one would anticipate. The cobalt/nickel and cobalt/iron cross-correlation spectra, however, indicate that the spectra have very little in common, as shown by the relatively small values of the function at r = 0.

V. Automated Identification-Interpretation

No discussion of spectral data processing techniques would be com-plete without some mention of computer-based spectral interpretation-identification procedures. The laborious task of spectral identification has been greatly eased by the use of computer-based library searching rou-tines. Two general classes of algorithms have been developed: procedures that seek to make an exact identification of an unknown by direct compar-ison with spectra of known compounds, and more general approaches striving to identify functional groups in the unknown.

A wide range of encoding schemes have been proposed (Clerc and Zupan, 1977) to represent spectra in a digital form suitable for library searching and interpretation. The ideal storage format retains the mini-mum amount of information necessary (Pape and Kunath, 1979; Schaarschmidt, 1979) for correct identification of unknown compounds in a form permitting rapid numerical comparison. Although the optimum instrumental parameters are a function of the sample, practical consider-ations require standardization in sampling. Specific computer characteris-tics such as word size, amount of core memory, and types of mass storage devices must also be taken into account. The problem of converting large spectral atlases to a computer-compatible format is best solved by the use of graphics digitizers (Delaney and Uden, 1978). Buechi et al. (1978) discussed the compilation of spectral libraries in terms of five operations: selection, digitization, completion, formatting, and verification. Early re-searchers (Delaney and Uden, 1979a; Liddell and Jurs, 1973; Grotch, 1975; Ritter et al., 1976b) faced with limited computer resources often


compressed spectra into a "binary" format (i.e., at each frequency there was or was not a peak). This approach is particularly well suited for search systems based on logical comparison of spectra (Kwiatkowski and Riepe, 1982a,b). Other methods (Shaps and Sprouse, 1981; Tomellini et ai, 1981) include encoding each peak in three numbers related to its position, intensity, and width. Statistical compression (Lyte and Brazie, 1970) approaches have been developed to maximize the information con-tent of data representation. Ideally, it would be desirable to store abso-lute-intensity information. Practical considerations necessitate the reduc-tion of library spectra to relative intensities, by either scaling all peaks relative to the most intense peak in the spectrum or dividing by the square root of the sum of the squared absorbances. One of the first encoding schemes (Kuentzel, 1951) was based on 80-column Hollerith punch cards with searching done on a mechanical card sorter. The ASTM ir band index (Fisk and Milne, 1979; Sebesta and Johnson, 1972; Tanabe et al., 1979; Woodruff et al, 1975a; Woodruff and Munk, 1977c; Anderson and Covert, 1967; Erley, 1968) represents the largest collection, with approxi-mately 145,000 spectra. In an effort to provide some standardization, the Coblentz Society (Griffiths et al., 1979) developed guidelines specifying the storage format for vapor-phase spectra. These specifications include date regarding spectrometer operation, sampling methods, computer stor-age format, and chemical information.

Due to the inherent complexity of ir spectra, definitive identification of unknown samples requires direct comparison with previously collected spectra. Most algorithms are designed to "step through" entire libraries in a sequential manner. These brute force methods have become practical only in recent years with the advent of completely digitized spectral li-braries and fast, low-cost computers combined with mass storage de-vices. Much of the work of developing library-based spectral identifica-tion programs arose from the need to interpret the large number of spectra generated by GC-FT-IR experiments. A basic algorithm computes some measure of similarity between the unknown spectrum and each library spectrum, forming a list of "best hits" as the procedure progresses, which is printed on completion of the search. Erickson (1981) investigated four measures of similarity,

Σ l·*,· - Li\ (47a)

[Σ (Ai - Li)2\ (47b)

Σ \(Ai - A,·-,) - (Li - L/-0I (47c)

Σ [|(A, - Ai-,) - (Li - L,-,)!2]"2 (47d)


where A; is the ith point in an unknown spectrum, and L,· the /th point in the library spectrum. All of these functions provide some measure of similarity in the form of distance. Comparison of methods (47a) and (47b) indicates that method (47b) is biased toward the more pronounced differ-ences between spectra and as such will be less sensitive to random noise. The derivative analogs of functions (47a) and (47b) are (47c) and (47d), respectively.

Although sequential searches can provide excellent results, it is ineffi-cient to search an entire spectral atlas to identify an unknown compound. Rather, one would prefer to investigate a subset in the form of a class of known compounds, thereby greatly reducing the amount of time required for the identification. By reformatting the ASTM file in an "inverted" format, which permits selective searching of library spectra based on the strongest absorptions in the unknown spectrum, Lytle (1970) was able to reduce the search time to less than 1% of a sequential search for the case in which seven characteristics were specified. A disadvantage of this approach, however, is that there is no tolerance for errors. To achieve optimum results, selective searches offering the greatest input flexibility generally require the use of random-access file structure, which was not available for use on many computers until the early 1970s. (Unlike con-ventional sequential files, random-access files enable one to read any record in a file without having to read all of the preceding records.) A number of search systems permitting the user to restrict the portion of the data base to that which is actually searched have been proposed.

Another technique involves selection by the computer of the portion of the library to search. This approach assumes some order in the library. Azarraga et al. (1981) proposed ordering a library relative to one of the spectra in the library. Unfortunately, the success of the technique is biased toward identifying compounds similar to the spectrum used to order the library. More recently, Zupan et al. (Penca et al., 1977; Zupan, 1980, 1982) and Delaney (1981) developed methods that do not bias the ordering toward a single spectrum by constructing hierarchical decision trees. Central to the hierarchical decision tree approach is the ability to form groups (clusters) of similar spectra. Each cluster is characterized by an average spectrum, which reflects the dominant spectral features in that group. By comparison of the unknown with the average cluster spectra, one can limit searching to a specific group of spectra. In practice, this procedure is carried a step farther: additional composite spectra can be computed that reflect the more general features of groups of other com-posite spectra to form a treelike structure. One problem with this ap-proach lies in the large number of calculations required to identify ςςsimi-lar" spectra using conventional clustering algorithms. For small libraries it is feasible to compute the similarity between all pairs of spectra. Given


the relatively large number of spectra in many libraries, this approach is not plausible.

Although exact compound identification is obviously most desirable, such algorithms are limited by the libraries they search; that is, an un-known compound not in the library cannot be identified (Kiatkowski and Riepe, 1982a). Consequently, a number of researchers have developed algorithms designed to interpret spectra rather than provide exact identifi-cation. There are several approaches for automated functional-group identification. Although these procedures cannot provide an exact struc-tural formula, they are very useful when a spectral library is not at hand or an unknown compound is not contained in a library (Woodruff et aL, 1975b). Functional-group-identification algorithms are unlike library searches, which typically require many calculations and large mass stor-age devices that must be accessed very quickly. Complex "decision trees" have been developed (Woodruff and Munk, 1977a,b; Visser and van der Maas, 1980a; Leupold et aL, 1980; Woodruff and Smith, 1980; Rusmussen and Isenhour, 1979) that contain detailed information about known group frequencies. The operation of such algorithms typically re-quires that the ir spectrum be reduced to a list of peaks and then checked.

Procedures that attempt to analyze for the presence of specific func-tional groups in interferograms have also been developed (Wiebolt et aL, 1980). By taking the Fourier transform of a spectrum containing peaks characteristic of a particular class of compounds, one can compute repre-sentative synthetic interferograms. Comparison of the unknown and group-specific interferograms is then done by the use of zero-displace-ment correlation, which is calculated by first aligning the center bursts of the two interferograms and then forming the dot product. Zero-displace-ment correlation using GC-FT-IR data (Wiebolt et aL, 1980) can be ap-plied to the rapid detection of specific groups as a function of elution time. The technique has been most successful in identifying functionalities that contain characteristic peaks well isolated from other classes of com-pounds.

A great deal of work utilizing pattern recognition has also been done. [A brief overview of pattern recognition applications in analytical chemistry has been presented by Varmuza (1980).] Most of these algorithms fall into the supervised learning category; that is, the program attempts to derive rules to classify sets of known spectra. These rules serve as the basis for interpreting unknown spectra. The absorbances of spectra at each fre-quency can be viewed as coordinates of a point in hyperspace. Linear discriminant functions (Woodruffs aL, 1974, 1975c; Delaney and Uden, 1979b; Comerford et aL, 1977; Preuss and Jurs, 1974; Lowry et aL, 1975) represent hyperplanes that partition points (i.e., spectra) belonging to one


class from those not in the class. [An extra dimension is often added to each point (Liddell and Jurs, 1973; Preuss and Jurs, 1974) to ensure that the hyperplane(s) will pass through the origin.]

The identification of mixture spectra presents special problems, partic-ularly if there is a large concentration differential of the pure components in the mixture. Several approaches have been proposed (Kwiatowski and Riepe, 1982a; Isenhour, 1973) but do not appear to have gained wide-spread acceptance. For the case in which a series of mixtures is at hand, the factor analysis eigenspectra least squares fit already discussed repre-sents the best alternative.

All of the procedures discussed thus far have been developed because there is no way to transform spectral information directly into definitive chemical structures. At present, the simpler problem of computing an ir spectrum from structural information has not been solved to the extent that would permit the unambiguous interpretation of an unknown spec-trum. Search systems combining spectroscopic techniques (Visser and van der Maas, 1980a,b; Leupold et al., 1980; Comerford et al., 1977; Zupan et al, \911', 1979, 1980; Zupan, 1978; Gribov and Elyashberg, 1977; Gribov et al., 1977; Kwiatkowski and Riepe, 1982c) such as ir, Raman, uv, mass, and nuclear magnetic resonance have the greatest po-tential for the identification of unknown compounds.

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2 FOURIER TRANSFORM INFRARED VIBRATIONAL CIRCULAR DICHROISM

Prasad L. Polavarapu

Department of Chemistry Vanderbilt University Nashville, Tennessee

Introduction Principles of Circular Dichroism Measurements Circular Dichroism Measurements Using Michelson Interferometry A. Interferograms in Calibration

Experiments B. Interferograms of Optically Active

Samples Vibrational Circular Dichroism Spectra A. Baseline Determination B. Data Collection Considerations Current Status Concluding Remarks References

61

62

67

68

75 78 80 85 89 94 95

I. INTRODUCTION

The difference in absorption of left versus right circularly polarized incident light by a given sample is referred to as circular dichroism (CD). The applications of CD measurements to three-dimensional structural elucidation in chemical systems are well known. The most popular CD measurements are those carried out in the visible spectral region, probing the electronic transitions of chiral molecules. Although similar measure-ments in the infrared (ir) spectral region probing vibrational transitions were conceived in the early part of this century, only recently has it become possible to make reliable measurements. The CD in vibrational transitions, known as vibrational circular dichroism (VCD), is quite small, and its magnitudes are typically one part in 105 units of absorbance. Despite its small magnitude, VCD has been successfully measured (Holzwarth et al., 1974) and also confirmed (Nafie et al., 1976) using dispersive ir spectrometers. The advantages of Fourier transform interfer-

61 FOURIER TRANSFORM Copyright © 1985 by Academic Press, Inc. INFRARED SPECTROSCOPY, VOL. 4 All rights of reproduction in any form reserved.

ISBN 0-12-254104-9

62 Prasad L. Polavarapu

ometry (FT-IR) over conventional dispersive ir spectroscopy are well known, and these advantages are now being realized in VCD measure-ments as well. The first VCD measurement using an FT-IR spectrometer (Nafie et ai, 1979) was reported in 1979, and a review (Nafie and Vidrine, 1982) covering the initial developments appeared in Volume 3 of this treatise. In the present chapter, the focus is centered on the important details of FT-IR-VCD that were not addressed before. The fundamental expressions of FT-IR-VCD derived from first principles, the nature of interferograms in a CD experiment, the procedures for estimating the baseline in measured spectra, and data collection considerations to en-hance signal quality are presented. Most of the ideas and material pre-sented here evolved from the VCD research in our own laboratory begin-ning in 1981. In the next section we briefly recapitulate the basic principles of CD measurements, and based on these principles, the dis-cussions in the subsequent sections are developed.

II. PRINCIPLES OF CIRCULAR DICHROISM MEASUREMENTS

The most central component of state of the art CD measurements (Os-borne et al., 1973; Chabay and Holzwarth, 1975; Stephens and Clark, 1979) is a photoelastic modulator (PEM) (Billardon and Badoz, 1966; Kemp, 1969), which provides rapidly modulated states of different polar-izations for the incident light. The fundamental concept involved in the operation of a PEM is that a periodic stress applied to an isotropic crystal causes a synchronous variation of the difference in the refractive indexes along two mutually perpendicular axes of the crystal. One of these two axes coincides with the axis along which the stress is applied. When the polarization direction of the linearly polarized incident light is at 45° to the optical axes of the crystal, the periodic variation in the birefringence of the crystal introduces a time-dependent phase lag into the electric vector components. The phase variation in time t follows the relation

δ, = δ^ sin 2TTiumt (1)

where com is the frequency of stress modulation, and δ^ the maximum phase shift introduced for wavelength λ, or wavenumber v{. When the maximum stress applied corresponds to a phase shift of δ^ = π/2, then the radiation of wavenumber vt is said to be circularly polarized. Alterna-tively, one of the two electric vectors incident on and parallel to the optical axes of the PEM is said to have undergone a 90° phase shift or quarter-wave retardation relative to the other. For a given maximum stress on the PEM, one quarter-wave retardation is achievable at only one

2 Vibrational Circular Dichroism 63

wavenumber, which is represented by vq. For other wavenumbers at the same PEM setting, the maximum phase shift becomes

s>o _ cjO 'Vi π Vi π kq

^ - 8 ^ = 2 ^ = ü (2)

As sin 2no)mt goes through +1,0 , and - 1 , δ, for wavenumber vq goes through +7T/2, 0, and — π/2, which means that the vq component goes through right circularly polarized, linearly polarized, and left circularly polarized states, respectively. For the intermediate values of sin 2na)mt, corresponding intermediate polarization states are achieved. Thus, only for a minor portion of the sin 2πωΐηί cycle is the wavenumber component vq circularly polarized (to be precise, only at sin 2πωίηί = ±1), and for the remaining portion the polarization states are those of unwanted intermedi-ate nature. Because of the presence of these various polarization states, it is important to understand the nature of the signal expected at the detec-tor. For this purpose and also in order to derive FT-IR-VCD expressions from first principles, which has not been done so far, two types of experi-mental arrangements will be discussed. In one arrangement, we consider a monochromatic light of wavenumber v{ passing through a linear polar-izer, a PEM, and an optically active sample, in that order, and finally reaching a suitable detector. In the second arrangement, the optically active sample is replaced by a birefringent plate and analyzer. The former arrangement will be referred to as sample setup, and for reasons that will become clear shortly, the latter arrangement will be referred to as calibra-tion setup. Regardless of the variation in the experimental arrangement, we shall consider the z axis as the direction of light propagation and the optical axes of the PEM to coincide with the x and y axes. Also, the polarization direction of the linear polarizer will be considered to be at 45° to the x and y axes.

The electric vector of monochromatic light of wavenumber ^ , after passing through the first polarizer, is given as [£°(^)/V2](ii + v), where u and v are the unit vectors parallel to the x and y axes, respectively, and E\vi) is the amplitude of initial electric vector. As the PEM introduces a time-dependent relative phase lag δ, into one of the electric vector compo-nents, the resulting electric vector, after passing through the PEM, be-comes

E(vd = [£0(^)/V2](u + \ei8t) (3)

If the right and left circularly polarized vectors are denoted [E\vi)l V2] x (u + iv) and [£°(^)/V2](u - iv), respectively, it can be seen that Eq. (3) is equivalent to


E(yd = [E°(vi)/VS][(\ - iîs0(u + iv) + (1 + i£?i80(u - iv)] (4)

When this electric vector passes through an optically active sample, the right and left circularly polarized components are absorbed to different extents, and these absorbances are denoted by AR(^) and AL(vi), respec-tively. The resulting electric vector, then, is

E(vi) = [E%Vi)/VS][(\ - iei80(u + iv)^-2-303AR^)/2

+ (1 + 1**0(11 - [y)e-imA^12] (5)

The intensity at the detector is calculated as the product of this electric vector with its complex conjugate. The voltage output of a linearly re-sponding detector can be represented by the intensity falling on it, which in the present case is

I(vi) = [I°(i>i)/2][(e-aK + e-<*L) + (eaK - e~a^) sin δ,] (6)

where /°(i>/) is the initial intensity of the wavenumber component vx;, aL = 2.303AL(P"/), a n d «R = 2.303AR(z7/). Because δ, is time variant, sin δ, can be expressed in Fourier series (Potter, 1978) as

00

sin δ, = sin(ö^. sin 2πωΠϊί) = 2 ^ Λ«-ι(δ^) s'm[(2n - l)27rcom/] (7)

where n is an integer and Λ(δ^) are Bessels functions. From Eqs. (6) and (7) it can be seen that the detector would notice a time-independent signal, known as the dc signal, and time-varying signals at frequency com, 3com, etc. The signal at the fundamental frequency com, generally known as the ac signal, can be isolated by passing the detector signal through a lock-in amplifier tuned to com. Similarly, the dc signal can be isolated by eliminat-ing the signals at frequencies o>m, 3com, etc., using appropriate electronic filters. Then the ratio of the signal demodulated at com to the dc signal becomes

T f e = 2y'(8&> U K + e J G-; (8)

where G\ and Gf are the gains introduced by the electronics of the lock-in amplifier and filters, respectively. Multiplying the numerator and denomi-nator of Eq. (8) by ^ « R ^ L ^ and noting that ( ^ - e~^)/(e^ + e~P) = tanh β and that for small values of β, tanh β — β, one obtains

LJviVhcî) = 2Jl(d%){\A5[ALî) - AR(^)]}(G,/Gf) (9)

If vt is equal to Fq, then δ° = π/2 and J,(TT/2) - 0.57 (Potter, 1978). It is not possible to know a priori the wavenumber vq precisely, however, and therefore 7ι(δ^) is not known. So the value of Ji^.)G\/Gf should be


determined from a calibration spectrum to obtain the CD of a sample from Eq. (9).

In the calibration arrangement, the optically active sample is replaced by a birefringent plate and an analyzer together. We shall consider four different ways these two components can be oriented with respect to the preceding PEM and first polarizer. First, we assume that the fast axis of the birefringent plate is parallel to that of the PEM and that the direction of polarization of the analyzer is parallel to that of the first polarizer. Earlier, it was mentioned that the electric vector of the light after passing through the PEM would be [£°(^)/V2](u + \exbt). The birefringent plate introduces further phase lag b%, which is static in nature. The final electric vector, after passing through the analyzer, becomes

Effi·) = [E°(vd/2](u + vei(8 '+^)(u + v) (10)

and the intensity would be

I(vi) = [/°(^)/2](l + cos δ, cos δξ. - sin δ, sin δξ.) (11)

Using Eq. (7) and a similar expression for cos δ,,

cos 6/ = cos(8% sin 2πωηί) = 70(δ°.) + 2 Σ Λ«(δ°) cos 4ηπωηί (12)

The expression analogous to Eq. (9) becomes

LmW = -27,(62.) sin 6g. G, /dcfö) " 1 + 70(δ£.) cos δ? Gf ( 1 3 )

If the polarization direction of the analyzer is perpendicular to that of the first polarizer, whereas the fast axis of the birefringent plate is parallel to that of the PEM, then (u + v) in Eq. (10) becomes (u - v), and the expressions analogous to Eqs. (11) and (13) become

I (pi) = [/°(Ϊ7.)/2](1 - cos 6, cos δ" + sin δ, sin δ?) (14)

LJvi) = 27, (δ°.) sin δξ. G, /dcfö) 1 - 70(δ°-.) cos δ? Gf

In addition to the polarization axis of the analyzer being perpendicular to that of the first polarizer, if the fast axis of the birefringent plate is also perpendicular to that of the PEM, then the ei(s'+8i7) and (u + v) terms in Eq. (10) become ei(8t~8^ and (u — v), respectively, and the expressions analogous to Eqs. (11) and (13) become

I(vi) = [7°(ir.)/2](l - cos δ, cos δ? - sin δ, sin δ?) (16)

Lm(vi) -27,(δ£.) sin6? G, /dcfo) 1 - 70(δ^.) cos δ? Gf

(17)

66 Prasad L Polavarapu

In a fourth arrangement, the polarization direction of the analyzer is parallel to that of the first polarizer, but the fast axis of the birefringent plate is perpendicular to that of the PEM. Then the expressions analogous to Eqs. (11) and (13) become

I(vi) = [I°(v.)l2](l + cos δ, cos δ? + sin δ, sin δ?) (18)

/ » m ( p = 2y,(6g.) sin δξ G, Ucivi) 1 + Μδ%) cos δ* Gf

Note that Eqs. (13) and (19) are equal but of opposite sign, as are Eqs. (15) and (17). Also, Eqs. (13) and (17), like Eqs. (15) and (19), are equal to each other with a nonzero magnitude of ±2J\(8i7)G\/Gf at cos δ^ = 0 and sin δ : = ± 1. This is applicable when

δ ? = # ? = ( 2 « + 1 ) ? (20)

where v^ is the wavenumber for which the birefringent plate introduces one quarter-wave retardation and n is an integer. Furthermore, Eqs. (13), (15), (17), and (19) are equal to each other, with zero magnitude, when sin δ . = 0. This condition is met when

δ - = — -z- = ηπ (21) Vn 2

In practical terms, if the maximum stress setting on the PEM corres-ponds to one quarter-wave retardation for the wavenumber Vq and the

Fig. 1. Calibration curves for circular dichroism measurements determined with the optical arrangements represented by Eqs. (13), (15), (17), and (19). These curves are ob-tained on an FT-IR spectrometer employing a BaF2 polarizer, ZnSe modulator, CdSe bire-fringent plate, BaF2 analyzer, and optical filter transmitting in the region 1650-600 cm1.


light components of various wavenumbers are investigated using the cali-bration arrangement, then one would obtain four experimental curves represented by Eqs. (13), (15), (17), and (19), as shown in Fig. 1. The nonzero crossings of these curves provide the values of ±2Ji(dli)G\/Gi. These values can be interpolated to the desired wavenumber and used in Eq. (9) to determine the CD of a given sample.

III. CIRCULAR DICHROISM MEASUREMENTS USING MICHELSON INTERFEROMETRY

For those familiar with the principles of the Michelson interferometer (Griffiths, 1975; Martin, 1980) and of CD measurements presented in the previous section, the combination of these two is rather straightforward. In a Michelson interferometer, the incident light is split into two compo-nents by a beam splitter oriented at 45° to the direction of propagation of incident light. One component is transmitted in the direction of propa-gation, whereas the other component is reflected at 90° to this direction. These components are brought back to the beam splitter by a fixed mirror in the path of one component and a movable mirror in the path of the second component. If the mirrors in the two arms of the Michelson inter-ferometer are not at equal distance from the beam splitter, the two split components will have traveled different distances before returning to the beam splitter. For a monochromatic light of wavenumber v-x and source intensity /?(ΪΛ), if the electric vector in one arm has traversed a distance X more in coming back to the beam splitter than the electric vector in the second arm of the interferometer, then the two electric vectors will have acquired a phase difference of ΙττΧν^. Then the intensity of light coming out of an ideal interferometer would be lllivi) c o s 2 ^ ^ ) , which is equiv-alent to [7s(^)/2](l + cos lirXvi). For a polychromatic incident light, the intensity recorded by the detector as a function of the path difference in the two arms of interferometer would be

I(X) = |o" ^ 4 p (1 + cos 2πΧϊ,) dv (22)

Usually, a portion of this signal that is independent of X is known as the dc signal and is eliminated by appropriate electronic filters. The remaining part that varies with X is known as the interferogram, and its shape and properties in a normal transmission experiment are well known. Similar aspects pertaining to the interferograms in CD experiments are presented in detail here. For measuring CD by means of a Michelson interferometer, the sample setup would correspond to placing a linear polarizer, a PEM, and an optically active sample, in that order, in the path of the light


coming out of the interferometer and going toward the detector. Similarly, the calibration setup would correspond to placing a polarizer, a PEM, a birefringent plate, and an analyzer, in that order, in the same place. The interferograms in the calibration setup and sample setup are discussed separately.

A. Interferograms in Calibration Experiments

Let us consider the experimental arrangement in which the fast axis of the birefringent plate is parallel to that of the PEM and the polarization direction of the analyzer is perpendicular to that of the first polarizer. From Eqs. (7), (12), (14), and (22), the intensity at the detector, as a function of the path difference in the interferometer, can be seen to con-sist of four distinct parts,

I(X) = /dc + /, + h + h (23)

where

/* = £ ^ Ρ [1 - Λ<.) cos δ?] </* (24)

/, = £ !%p. [1 - J0(8l) cos δ?] cos 2πΧν, dv (25)

Too J^( — "\ f °°

h = Jo -^ψ- [Σ-^2ηφ cos δ? cos Αηττω^

+ 2J2n-\(8%) sin δ? sinL(2« - l)27ra>m/]} dv (26)

= f- / φ ) Γ ^ _2j2n(8?,) cos δ? cos 4n7ra>mi Jo 4 l~,

+ 2J2n-\(8%) sin δ* sinl(2« - l)27rcüm/]j cos ΙττΧν-, dv

(27)

The intensity represented by Eq. (24) is purely a dc term and is eliminated during signal processing. The intensities represented by Eqs. (25) and (26) are the signals varying independently due to the mirror movement in the interferometer and polarization modulation by the PEM, respectively. The intensity represented by Eq. (27) contains interference from these two modulations. It may be noted that com is usually in the range 25-100 kHz, depending on the design of the PEM, and the interferometer fre-quencies 2Vv~i can be in the range 10,000-100 Hz, depending on the mirror velocity V in the interferometer.


When the detector signal [Eq. (23)] is passed through the electronic filters that transmit only the interferometer frequencies and block the dc signal as well as na)m signals [Eqs. (24), (26), and (27)], one obtains a interferogram It(X) that is similar to those obtained in a normal transmis-sion experiment, where

h(X) = |o°° I-^~ [1 - Λ(δ°-·) cos δ?] Gf(vd cos 2nXvt dv (28)

Similarly, if the detector signal is passed through a lock-in amplifier with a minimal time constant and tuned to o>m, then the output of the lock-in amplifier becomes

/, = £ I-J^~ [2/,(δ^.) sin δ?](1 + cos 2nXvdG\(vd dv (29)

Note that Eq. (29) is similar in form to Eq. (22) and that the output of the lock-in amplifier contains a dc signal superimposed on the interferogram signal. The presence of this dc signal in the output of the lock-in amplifier was not realized earlier (Nafie and Diem, 1979; Nafie and Vidrine, 1982). The dc part can be eliminated by passing the lock-in output through the same electronic filters that are used in obtaining Eq. (28). The resulting interferogram, denoted Ιωη(Χ) to indicate the interferogram signal demod-ulated at a)m, becomes

W * ) = [ ^ [ 2 7 , ( δ > ί η δ?] cos(2nXvdGx(vdGf(vd dv (30)

Sometimes it is necessary to use another electronic filter ahead of the lock-in amplifier to eliminate the contributions from Eqs. (24) and (25), which might overload the lock-in amplifier. In Eqs. (28)-(30), the gain factors G\(v~i) and Gf(v~i) are wavenumber dependent. Because the inter-ferometer frequencies 2Wvx and the modulation frequency com give rise to the beat frequencies com + IVvi and ωηι - IVvi [see Eq. (27)], it is neces-sary to set the lock-in to a Q value that is adequate for passing the interferometer frequencies centered around com without severe attenua-tion. Any signal attenuation resulting from the inherent properties of the lock-in amplifier, including its time constant, can be considered to be embedded in the G\(v~i) term. The lock-in amplifier may also introduce wavenumber-dependent phase factors into the cos(27rA^) term in Eq. (30). These phase differences will be accounted for in the Fourier trans-formation of the interferograms (as discussed later). For this reason and also to avoid complicated forms for the equations, the phase differences introduced by the lock-in are not explicitly shown in the equations.

It is most interesting to analyze the interferograms It(X) and hm(X) to understand their shape and also to ascertain whether any information can


be gained from the interferograms themselves. Before doing so, it is nec-essary to comment on the integration limits in Eqs. (28) and (30). For a given maximum stress on the PEM, only one wavenumber component, i7q, achieves a circularly polarized state, and other wavenumber compo-nents, Vi9 follow Eq. (2). Becase the light output of the Michelson interfer-ometer contains several wavenumber components, some of these would acquire a maximum phase retardation of π, π/4, etc. These retardations are undesirable for CD measurements, and therefore there is no need to detect the wavenumber components other than those close to vq. In our experiments in the mid-ir region we usually use a filter that has 5% cut-on at 1635 crrr1 and set the PEM so that vq is at —1200 cm- 1 . For this reason, the integration limits for Eqs. (28)-(30) will be determined by the allowed bandwidth in a CD experiment.

First, we consider the ωΙΤ1 interferogram the shape of which is deter-mined mainly by the trignometric functions in Eq. (30). This is because, within the band with of a CD experiment δ£. is chosen to be around π/2, which makes J\{b%) remain positive. Thus, consider the integral

ILJX) = |0"maX sin δ? cos lirXvi dv (31)

where vmdX is the maximum wavenumber component detected. Using Eq. (20), this integral can be seen to be equivalent to

/' (ΥΛ = - n s [ C O S ( X B / 4 + X)2nvmAX - 1 cos(XB/4 - Χ)2πνΠΆΧ - 11 l*J*) U.3 [ (λΒ/4 + Χ)2ττ (λ Β /4-Ζ)2τΓ J

(32)

where λΒ is the wavelength for which the birefringent plate introduces the first quarter-wave retardation. For positive path difference X, the contri-bution of the second term of Eq. (32) is large, whereas that of the first term is negligible. Similarly, for negative path difference, the contribution from the first term of Eq. (32) is large, whereas that of the second term is negligible. The plot of Eq. (32) as a function of path difference X is shown in Fig. 2, for λΒ = 200 μπι and vm.äX = 1600 cm"1. The horizontal axis represents the data points, where each data point corresponds to a sam-pling interval of 0.6328 /xm. The most interesting point here is that, unlike normal transmission interferograms, the com interferogram will not have maximum intensity at the zero path difference (ZPD) point, that is, at X = 0. Instead, there will be two intensity maxima (one with a positive value and the other with a negative value), one on each side of the point X = λΒ/ 4 = Xc. There will be two similar intensity maxima on the negative path difference side also, one on each side of the point —X = λΒ/4 = —Xc. The distance AXC between the positive and negative intensity maxima on ei-

2 Vibrational Circular Dichroism

2.4η

I.6H

71

>-<o z UJ I -z

0.8-

O.OH

-0.8-

- I .6-

-2.4-

-ΛΛΛ/WVyu

-320 -240 -I60 - 1 — 80

~ i — I60 240 320 -80 0

DATA POINT Fig. 2 . Theoretical o)m interferogram represented by Eqs. (31) and (32), with λΒ = 200

μ,πι and vmax = 1600 c m 1 . The sampling interval for the data points on the horizontal axis is taken to be 0.6328 μ,πι.

ther side of Xc can be estimated as follows. Because only one of the two terms in Eq. (32) is important at a time, let us consider the second term of Eq. (32) for positive path difference in the interferometer. This term equals zero for values (λΒ/4 - X)(27rvmax) = ±2ηπ, where n = 0,1,2, etc. That is, at X = λΒ/4 ± n/vmax, the second term of Eq. (32) is zero. Hence, the distance AZC between the positive and negative intensity maxima would be l/Vmax. If the integration of Eq. (31) is carried out from vm[n, instead of zero, then AZC will be Ι/Δν, where Δ

V ~ ^max ^rnin IS t h e bandwidth of a CD experiment, and vmin the lowest wavenumber compo-nent detected. These theoretical predictions can be verified from the ex-perimental interferogram shown in Fig. 3, which was obtained with a ZnSe modulator, two BaF2 polarizers, a CdSe birefringent plate, an opti-cal filter with 5% cut-on at 1635 cm-1, and sampling intervals of 0.6328 μ,πι. The value of λΒ for the CdSe birefringent plate employed here was not known previously and has been estimated from the calibration curves in Fig. 1. Earlier, it was mentioned [see Eq. (21)] that the four curves in Fig. 1 meet at zero crossings for values of λΒ/λ/ = 2n. The values of λ/ at any two such crossings are sufficient to determine the value of λΒ from Eq. (21). From these crossings, we find the λΒ of the CdSe plate, used in obtaining Figs. 1 and 3, to be 154 /xm. Thus, Xc = λΒ/4 corresponds to the


773

0.25

<

0.00

O > -0 .25

6 0 0 800 675 700 725 DATA POINT

Fig. 3. Experimental interferograms obtained in a calibration experiment employing an optical filter transmitting in the region 1650-600 cm1. The data points are sampled at 0.6328-μπι intervals, (a) An o)m interferogram corresponding to Eq. (30). Data point 712 corresponds to the ZPD point, and data point 773 to the point Xc, as discussed in the text, (b) Transmission interferogram corresponding to Eq. (28). Data point 706 corresponds to the ZPD point, and the modulations in the region represented by the double-headed arrow are due to the second term in Eq. (28).

sixty-first data point from ZPD, at a sampling interval of 0.6328 μπι. This is in perfect agreement with the actual value found for Xc experimentally (Fig. 3). In fact, this observation may well be used to determine the birefringence of optical plates from the cum interferogram itself. The verifi-cation of AXC is slightly difficult because the value of AXC is influenced by the presence of /?(^)/ι(δ^.) terms in the actual interferograms [Eq. (30)].

Similar analysis can be undertaken for the normal transmission interfer-ogram It(X) given by Eq. (28). This interferogram can be seen to be a resulting sum of two parts. The first term in Eq. (28) is simply a transmis-sion interferogram of the instrument and is the dominant contributer to h{X). The second term in Eq. (28) can be analyzed as in Eq. (30), but here the term 70(δ^) is a rapidly varying function with wavenumber. In fact, within the bandwidth of a CD experiment that is around δ?. = π/2, JoiSp.) can have both positive and negative values and therefore cannot be omit-ted. Fortunately, the contribution from this second term is small com-pared with that of the first term, and hence It(X) will look like an instru-mental transmission interferogram with a well-defined intensity maximum at ZPD. The modulations caused by the second term will be apparent near Xc, where the contribution from the first term becomes small. An experi-mental interferogram is shown in Fig. 3, where these points can be veri-fied as well.

To obtain the desired calibration curve analogous to that represented by Eq. (15), the interferograms It(X) and ΙωΛΧ) have to be Fourier trans-


formed and the ratio of the resulting spectra must be taken. The phase corrections required for the Fourier transformation of It(X) can be ob-tained by the usual procedures employed (Mertz, 1967; Bertie, 1980; Chase, 1982) for one-sided interferograms, because the ZPD is associated with a well-defined intensity maximum. The phase corrections in Fourier transformation of/Wm(Z), however, require special care because, as men-tioned earlier, the intensity maxima occur near Xc (see Figs. 2 and 3) and not at ZPD. The commercial FT-IR spectrometers provide an option in which the software automatically searches for the intensity maximum and associates this with ZPD. Clearly, this option gives erroneous results here. In another option, one could supply the ZPD information and have the phase corrections calculated using the supplied ZPD information. There are two ways to locate the ZPD point in com interferograms. In an earlier discussion we showed how to identify the point Xc. From Eqs. (30) and (32), it can be seen that the ZPD will be midway btween +XC and —Xc, so one can calculate the data points corresponding to +XC and —Xc

and obtain the ZPD point as the average of these data points. In a second procedure, because the interferogram is expected to be symmetric with respect to ZPD, one could locate the ZPD point by inspecting the symme-try in the interferogram.

In principle, it should be possible to obtain satisfactory phase correc-tions for one-sided interferograms, once the ZPD point has been identi-fied. In the case of ojm interferograms, however, two precautions are required even at this stage. To circumvent the problem of side lobes in the Fourier transformed spectra, resulting from the finite mirror movement in interferometers, it is customary to apodize the interferogram. From the shape of the ωπι interferogram, it can be seen that any apodization func-tion that dampens the interferogram in the wings would actually nullify the intensity maxima found near Xc, and this effect would lead to im-proper phase correction. Either a boxcar apodization or an inverse trian-gular apodization (Griffiths, 1975) would be more appropriate for a)m inter-ferograms. The second precaution pertains to the use of a left ramp function employed (Anderson and Mattson, 1979) to minimize phase er-rors resulting from a small error in the ZPD point. Again, the left ramp function would nullify the intensity maxima near -Xc and lead to an improper phase correction. Thus, one should choose a proper apodization function and avoid a left ramp function for Fourier transforming, the a)m

interferograms. Alternatively, one could transfer (Nafie et aL, 1979) the phase correction calculated for the transmission interferogram It(X) to Ιωτη(Χ) with the ZPD point determined in the manner described. This procedure is quite satisfactory for obtaining calibration curves (but not sample CD, as described later). When these precautions are taken, the


calibration curve analogous to that represented by Eq. (15) is obtained as Iol LJX) cos lirXvi dX 2J\(h%) sin h%

G\{vi) (33) So1 h{X) cos lirXvi dX 1 - J0(8% cos δ£.

The procedure for obtaining the other three calibration curves, analo-gous to those represented by Eqs. (13), (17), and (19), is identical to the one presented thus far. To illustrate the effects of phase errors on calibra-tion curves, these curves obtained with different options are shown in Fig. 4. The curves shown in Fig. 4a are obtained by letting the software associate the intensity maximum near Xc with ZPD and using the Happ-Genzel apodizaion. The curves in Fig. 4b are obtained by supplying the ZPD (determined as described earlier) and using the Happ-Genzel apodi-zation. The curves shown earlier in Fig. 1 are obtained by transferring the phase correction calculated for It(X) to Ιωπϊ(Χ). It is evident that the cali-bration curves in Fig. 4 contain severe phase errors, whereas those in Fig. 1 are as expected by theory.

0.55H

- i o.cH E

3

-0,55

-1.10 1675 1555 1435 1315 9 5 5 835 715 595 1195 1075

WAVENUMBER Fig. 4. Calibration curves obtained with improper phase corrections, (a) Curves ob-

tained when the maximum intensity near Xc in the o>m interferogram is associated with the ZPD point and Happ-Genzel apodization is used, (b) Curves obtained when the correct ZPD point and Happ-Genzel apodization are used.


Finally, it should be mentioned that the ZPD point in transmission interferograms would differ from that in a>m interferograms (see Fig. 3). This is because to obtain /Wm(Z), the detector signal is first demodulated by a lock-in amplifier that introduces a time constant effect. For a mirror velocity of 0.5 cm/sec and a sampling interval of 0.6328 μιη, the time elapsed between successive data points would be approximately 63 /xsec. If the time constant of the lock-in amplifier is comparable to this value, the data recorded for the a>m interferogram would appear to be shifted relative to that of the transmission interferogram. The effect of this time constant is discussed in Section IV.B.

B. Interferograms of Optically Active Samples

From Eqs. (6), (7), and (22) the total intensity at the detector in the sample setup, as a function of mirror movement, would be

KX) = /dc + h + h + h (23) where _ 0

Γ Jo

f Jo

o 4

4

»w I°s(Fi)

4

x

(e"aR + e~aL) cos lirXvi dv

(e-otR _ e~aL)

Jo

J E 272π-ι(δ"-) sin[(2« - l)2irWmi]} dv

(e~aR — e~ai)

] Σ 272/ι-ι(δρ·) sin[(2« - l)27rcom/] cos 2πΧν\ dv

Following the arguments advanced for the interferograms in the calibra-tion experiment, the normal transmission interferogram and a)m interfero-gram can be extracted from Eq. (23) as

It(X) = £ " " [/?fö)/4](e-"R + e-«L)(cos 2nXvdGf(vd dv (34)

IaJX) = / 0W Ulivdime-«* - e-°L)

x (cos l^XvMJ^G^vdG^m dv (35)


where aR and aL are as defined following Eq. (6). Denoting AL(VJ) - AR(^/) as ΔΛ(^/), we see that Eq. (35) is equivalent to

Lm(X) = £ ™ ^ p (*_aR + e-aL)(cos 27T^){tanh[1.15AA(i7/)]}

x [IHS^GWGfö)] dv (36)

Besides the difference in gain factors, the transmission interferogram [Eq. (34)] and ωτη interferogram [Eq. (36)] differ only in the presence of J\(d%) and tanh[1.15AA(i^)} terms in the latter. Within the bandwith of a CD experiment, J\(8%) is maintained positive, but AA(j7,) can have both posi-tive and negative values. Hence although It{X) would resemble the routine transmission interferogram of a sample with the maximum intensity asso-ciated with the ZPD, the com interferogram is expected to be different. In a way, the shape of the com interferogram of the sample can be expected to be closer to the shape ofthat in a calibration experiment, because both the AA(F,-) contribution to the former and the sin δ | contribution to the latter are bisignate. The major difference is that, in contrast to sin δ | , AA(P/) is not a periodically and predictably varying function. As a consequence, it does not appear to be possible to predict where the maximum intensity point would be in the o>m interferogram of a sample. To gain some under-standing we have simulated interferograms from the theoretical values (Polavarapu and Michalska, 1984) of AA(F,·), and one such interferogram for (5>(-)-epoxypropane is shown in Fig. 5. Here, the J\(8%) term is expressed in an analytical form (Potter, 1978), and the source intensity and gain factors are assumed to have unit values. From the simulated interferograms, we find that the maximum intensity is not associated with the ZPD as expected, but is a few data points away. The intensity around the ZPD, however, is not as low as that found in the com interferogram of the calibration experiment (see Figs. 2 and 3). In practice, the a)m interfer-ograms of samples are quite different from the one expected from Eq. (36) and appear to be similar to the transmission interferograms (Fig. 6). This can be explained by noting that the AA(F/)/A(F,·) values for vibrational transitions are of the order of 10-4 to 10"5 and that the dichroism in the optical components and detector window can easily exceed this value. Then an instrument-dependent bias should be added to AAfö) in Eq. (36), which can make the resulting values monosignate and the resulting inter-ferograms similar to the normal transmission interferograms.

The experimental a>m interferograms are invariably similar to the nor-mal transmission interferograms, except for one difference. Because the com interferogram is processed by a lock-in amplifier, the time constant of the lock-in amplifier makes the o>m interferogram appear to be shifted


I 2η

8-J

>■ \-CO

77

- 4 -

- 8 H

-12- — I — - 8 0

—r— 80 -320 - 2 4 0 -160 - 8 0 0 80 160 2 4 0 320

DATA POINT

Fig. 5. Theoretical com interferogram, corresponding to Eq. (36), of (5)-(-)-epoxypro-pane. The source intensity and gain factors due to processing electronics are assumed to have unit values. Only the spectral region 1600-600 c m 1 is considered in the computation.

354

< u CO

o >

0.31

0 . 0 0 4

-0.31 300 375 4 0 0 325 350

DATA POINT Fig. 6. Experimental interferograms of an optically active sample recorded with an

optical filter transmitting in the region 1650-600 c m 1 and a sampling interval of 1.2656 μ,πι. The ZPD points are indicated by the arrows, (a) An a>m interferogram corresponding to Eq. (36); (b) a transmission interferogram corresponding to Eq. (34).


relative to the normal transmission interferogram. Then the ZPD point will be shifted accordingly (see Fig. 6). The relative shift of the ZPD point can be easily determined for the interferograms in the calibration experi-ment as discussed earlier. The same amount of relative shift is applicable to the interferograms of the sample under the same instrumental condi-tions. In our experiments we provide the ZPD point determined in this way and let the software Fourier transform the G>m interferogram using this ZPD point. For transmission interferograms It(X), the software searches for the maximum intensity, associates it with the ZPD point, and carries out the Fourier transformation, which is the same procedure ap-plied in normal transmission experiments. The CD of the sample is then obtained from Eqs. (34) and (36) for one-sided interferograms as

It* 7"m(*) COS 27rXi7i dXl^ l ^ COS 27rXF' dX

= 2Ji(6^.)Gi(^) tanh[1.15AA(*;■)] -271(δ^1(^)[1.15ΔΑ(ΖΛ)] (37)

The value of2J\(8%)G\(vi) is obtained from the nonzero crossings of the calibration curves (see Fig. 1).

In the early VCD measurements (Nahe et ai, 1979, 1981; Nafie and Vidrine, 1979, 1982; Lipp et al., 1982a,b) it was thought that the phase correction calculated for either the transmission interferogram of the sam-ple or the interferogram of a stressed ZnSe plate had to be transferred to Fourier transform the a>m interferograms. At that time the similarity of experimental com interferograms with the transmission interferograms of the sample was not realized, and the phase transfer procedure was usually employed. The procedure of Fourier transforming the ωπχ interferograms of the sample by supplying the ZPD information, as suggested by (Po-lavarapu 1984a) is now routinely employed, and the phase-transfer proce-dure is considered unnecessary (Lipp and Nafie, 1984).

IV. VIBRATIONAL CIRCULAR DICHROISM SPECTRA

Because CD is a difference quantity, both positive and negative inten-sity features are expected in the VCD spectra obtained from Eq. (37). In practice, the bias introduced by the birefringence and dichroism in the optical materials of the instrument superimposes all the VCD features on a monosignate background. The racemate (a mixture of 50% R and 50% S · enantiomers) of a sample will exhibit no VCD, and hence the raw VCD spectrum of racemate obtained from Eq. (37) should contain just the


monosignate background. If the raw VCD of racemate is subtracted from that of an enantiomer, then the monosignate background will be sub-tracted out and the real VCD features of the enantiomer will result. To verify that this subtraction procedure gives genuine VCD features, it is necessary to verify that the VCD features of the enantiomers will be of equal magnitude and opposite in sign. Furthermore, the sum of the raw VCD spectra of the enantiomers should be equal to twice the raw VCD spectrum of the racemate. The difference between the racemate raw VCD spectrum and one-half of the sum of the raw VCD spectra of the enantio-mers reflects the amount of noise present in the VCD spectral measure-ment. The verification of the genuineness of the measured VCD and the noise level, in this manner, requires three different measurements (one on each enantiomer and one on the racemate). Although this is time-consum-ing, it is important to make such verifications, because the nature of the artifacts in VCD spectra is not yet completely understood and is very much dependent on the instrumental components. The raw VCD spectra for the enantiomers and the racemate of limonene oxide are shown in Fig. 7. The VCD spectra of the enantiomers obtained after subtracting the raw VCD spectrum of the racemate are shown in Fig. 8 along with the noise

1365 T

965 915 Π 1 1 1 1 Γ

1315 1265 1215 1165 1115 1065 1015 WAVENUMBER

Fig. 7. FT-IR-VCD (above) and absorption spectra (below) for limonene oxide (neat liquid). The labels (+) and ( - ) identify the raw VCD spectra of the enantiomers that exhibit positive and negative optical rotations, respectively. The trace between the traces of enan-tiomers represents the raw VCD of the racemic mixture.


Δ Α = 3 χ Ι Ο "

965 915 1165 WAVENUMBER

Fig. 8. FT-IR-VCD (above) and absorption spectra (below) for limonene oxide (neat liquid). The raw VCD spectrum of the racemic mixture is subtracted from that of each enantiomer. The labels (+) and (-) identify the VCD spectra of the enantiomers, as in Fig. 7. The trace that is nearly horizontal and runs through the crossings of the spectra of enantio-mers represents the noise level in these measurements and is obtained as one-half of the sum of the raw VCD of enantiomers minus the raw VCD of the racemic mixture.

spectrum. The excellent mirror-image quality seen for the VCD spectra of the enantiomers, with very low noise level, can be regarded as a valida-tion of the subtraction procedure employed.

If both enantiomers are available, the racemate can be made by mixing equal proportions of the enantiomers, and the three measurements pro-vide the best reliability test for the measured VCD. For most organic compounds, only one enantiomer is generally available, and this poses a major challenge for determining the reliable baseline from the VCD mea-surements on a single enantiomer. We have undertaken extensive investi-gations on this topic (Polavarapu et al.} 1984) employing different ideas, and these are summarized in the following section.

A. Baseline Determination

The artifacts in VCD spectra can arise from several sources, and in our instrument we find the main source to be the detector window. Even if different detectors contain windows of the same material, the stress factor and therefore the birefringence in the window can be quite different,


unless special precautions are taken by the manufacturer. Thus, the eval-uation of artifacts can vary from one detector to another. So far, we have employed three different detectors for measuring VCD in different re-gions, and the details of artifacts have been different in each case. Thus, the aim of this section is to give a broader picture for determining the baseline in VCD spectra of a single enantiomer.

Let us assume that the detector window introduces an additional di-chroism and that the ΔΑ(^/) in Eq. (36) is a sum of two contributions, one due to the sample ΔΑ8(Ϊ7,·) and one due to the detector AAdfö). If the optically active sample is placed ahead of the PEM and the first polarizer instead of after the PEM, then the ΔΑ(Ρ,) term in Eq. (36) will be equiva-lent to AAd(pi), whereas the overall absorbance will be maintained as before. This sequence of components, that is, sample, polarizer, and PEM, in the optical train is referred to as the background VCD setup. The VCD obtained from Eq. (37) in the background setup would be 2Ji{d®)G\(vj) tanh[1.15AAd(i7,)], whereas that obtained in the sample setup would be 2Jx(8%)Gi(vi) tanh[1.15AAs(i7,) + 1.15AAd(i7,)]. Provided that AAd(i7,·) is small, AAS(^,) can be extracted from the difference be-tween the VCD obtained in these two measurements. In order for this method to be generally applicable, one should first verify that this back-ground subtraction procedure gives mirror-image VCD features for the enantiomers. In Fig. 9 the VCD spectra for the enantiomers of a-pinene, obtained by subtracting the background VCD spectrum from the raw VCD spectrum, are shown. All the major VCD bands are seen to have opposite signs for the enantiomers. In this way of measuring VCD, some amount of caution should be exercised. In particular, it should be noted that for some samples the raw VCD spectrum obtained in the sample setup may resemble the absorption spectrum. In such cases, the VCD spectrum obtained in the background setup may not represent a true baseline. As a result, when the background VCD spectrum is subtracted from the raw VCD spectrum of the sample, the resulting VCD bands would have predominantly the same sign and look like the absorption bands.

The spectra recorded with one detector in our laboratory showed only weak absorption dependence (see Figs. 7 and 9), and the efforts to derive the baseline from the background VCD spectra turned out to be success-ful most of the time. Employing a different detector resulted in a large amount of bias in the raw VCD spectra of samples, and the subtraction of the background VCD spectra did not provide true VCD. The large bias seen in the raw VCD spectra, however, is invariant to the rotation of the polarizer ahead of the PEM. When this polarizer is rotated by 90°, the left and right circularities are interchanged, and hence the measured CD also changes sign. In other words, the VCD spectra of the sample measured


0.63

LU O

< m cr o CO m <

0.42

0.2M

0.00 1350 1300 1250 1200 1150 1100 1050 1000 950

WAVENUMBER Fig. 9. FT-IR-VCD (above) and absorption spectra (below) for α-pinene (neat liquid).

The background VCD is subtracted from the raw VCD of each enantiomer. The labels (+) and ( - ) identify the spectra of enantiomers that exhibit positive and negative optical rota-tions, respectively.

with the polarizer oriented +45° and -45° to the fast axis of the PEM have identical bias but oppositely signed CD. Then subtraction of these two VCD spectra will eliminate the bias and provide the CD with twice the magnitude. Here also, caution should be exercised, because any birefrin-gence present in the optical components after the sample will also change sign [see Eqs. (11) and (14)] with rotation of the polarizer from +45° to -45°. As a result, the VCD extracted in this way can still contain some artifacts, and this is in fact evidenced in the spectra in Fig. 10. Here, the VCD spectra for each enantiomer of α-pinene are obtained as one-half of the difference between the raw VCD spectra obtained with the polarizer at +45° and -45° to the fast axis of the PEM. The mirror-image quality for the VCD of enantiomers is satisfactory for all major VCD bands. The VCD corresponding to the absorption band at 1265 cm 1 , however, was of the same sign for both enantiomers. This is clearly an artifact, as already mentioned. These artifacts are estimated by measuring the background VCD spectra with the polarizer oriented at +45° and -45°, as before. When the difference between these two background spectra was taken, the resulting spectrum did not contain bias as before, but the artifact signals remained with twice the magnitude. These subtracted background


1350 1285 1220 1155 1090 1025 960 895 830 765 WAVENUMBER

Fig. 10. FT-IR-VCD (above) and absorption spectra (below) for α-pinene (neat liq-uid). The VCD spectra of each enantiomer, represented by the labels (+) and (-), are extracted as one-half of the difference between the raw VCD obtained with the linear polarizer ahead of the PEM, at +45° and -45° to the fast axis of the PEM. The topmost traces represent the background VCD and are obtained similarly, with the sample placed before the linear polarizer.

spectra for each enantiomer of α-pinene are also shown in Fig. 10. Note that the artifact signal at 1265 cm-1 is clearly seen in the background result. For obtaining the artifact-free VCD, the difference in background VCD should be subtracted again from the aforementioned difference in sample VCD. This procedure, therefore, requires four different measure-ments for a given enantiomer.

A third approach that we have formulated follows from the equations derived for calibration curves. The birefringent plate and the analyzer can be regarded together as a sample with VCD varying between ±2J\(8%) [see Eq. (33)]. Similarly, the optically active sample and the subsequent components in the optical train can also be regarded together. If the sample and subsequent optical components exhibit not only CD, but also circular birefringence (CB) and linear dichroism (LD), then the intensity at the detector given by Eq. (23) will contain an additional term, /(CB,LD) cos δ, (Jensen, 1983; Jensen et aL, 1978), where/(CB,LD) is a


function of the circular birefringence and linear dichroism. Then Eqs. (23) and (37) become

l(X) = Tmax - ^ p i [(e-«R + e-«L) + (e-«R - e~a^) sin δ,

+ /(CB,LD) cos δ,](1 + cos 2πΧν() dv (38)

Jof1 Lm(X) cos ITTXVI dX _ 2Jl(8%)Gl(pi)(e-aR - e~"L)

Jp It(X) cos 2πΧν, dX ~ (e~aK + e~aL) + y0(6g,.)/(CB,LD) If the second term in the denominator on the right-hand side of Eq. (39) is significant, then this equation does not provide true VCD, and the result-ing raw VCD spectra can have complicated shapes. The contribution of the J0(8^.)/(CB,LD) term can be eliminated by noting that

Jo' LJX) cos lirXvi dX

Ji» h{X) cos IrrXvi dX - [Μδ^/ΙΜδ^Ο^)] Jp I2ojJX) cos 2πΧϊ7{ dX

-271(80,,)G1(i7/)[1.15AA(i7/)] (40)

The signal hMm(X) in the denominator on the left-hand side of this equa-tion can be obtained in the same way as Lm(X) by tuning the lock-in amplifier to 2<am frequency. The values of Jo(&%) and Λ(δ(^) required in Eq. (40) can be determined from the calibration curves (Fig. 1) as follows. The wavenumber at which J\(d%) is maximum corresponds to 8% = 1.832. The wavenumber at which one quarter-wave retardation is introduced by the PEM is 0.86 times lower than the wavenumber corresponding to the maxi-mum J\ value. The 8% value at different wavenumbers can then be ob-tained from Eq. (2). Once 8% is known, Jo(8%) can be obtained from the standard tables. Since J\(8%) is known from the nonzero crossings of the calibration curves (Fig. 1), J2(8^) can be obtained from Jo(8%) and J\(8%) using the recursion formula (Potter, 1978). The value of Jo(8%)/ [2J2(dv)G\(vi)] calculated in this way varied in the range 0.1 x 10~2 to 3.3 x 10~2 for the region 1600-600 cm"1 in our instrument. Because the Fourier transforms of I{(X) and I2i0m(X) are of similar magnitude (see Fig. 11), the contribution of the second term in the denominator on the left-hand side of Eq. (40) is less than 3.5% and is therefore ignored in our measurements.

Lipp and Nafie (1984) suggested a different procedure for determining the baseline. They reported that the absorption dependence in their mea-surements varied only slightly with the concentration of (3/?)-(+)-methyl-cyclohexanone in CC14 solution. They suggested that twice the difference in raw VCD spectra of 0.22 and 0.11 M solutions nullifies the artifacts and provides true VCD. A polarization scrambling technique was also ad-


i i i 1 1 1 1 1 1 1

1625 1540 1455 1370 1285 1200 1115 1030 945 860 WAVENUMBER

Fig. 11. Comparison of the spectrum demodulated at 2com frequency with the transmis-sion spectrum for (3/?)-(+)-methylcyclohexanone. Both spectra are plotted on the same scale.

vanced (Lipp et al.f 1982a) to nullify the artifacts in FT-IR-VCD mea-surements, but no real measurements have been carried out.

It should be reiterated that the level and nature of artifacts vary from one instrument to another. Therefore, it is necessary to investigate these procedures on an individual basis.

B. Data Collection Considerations

In order for the lock-in amplifier to demodulate the polarization modu-lated signal effectively, the PEM frequency should be well separated from the interferometer frequencies. For spectral measurements in the region 7000-400 cm-1, the highest interferometer frequency in the modern fast-scanning interferometers would generally be less than 10 kHz. Then it is necessary to have the PEM frequency much above this frequency, prefer-ably in the range 25-100 kHz. This results in a constraint that the detector employed for CD measurements should be sufficiently responsive to this high-frequency modulation, which restricts the choice of detectors to the fast photoconductive or photovoltaic detectors. The detectors that we have successfully employed are the HgCdTe detectors (D* = 2 x 1010 and 0.5 x 1010, designated and supplied as type A and B by Nicolet Instru-ments Corporation) for VCD measurements in the region 2000-600 cm-1. For VCD measurements in the region 4000-2000 cm-1, an InSb detector (D* = 1011) has been employed (Nafie et al., 1979). A proper choice for the velocity of the moving mirror in the interferometer depends on the detector employed. This value is generally suggested by the manufactur-ers of FT-IR spectrometers but may require a slight modification, as described latter, for VCD measurements.


Because VCD magnitudes are very small, the noise level in com interfer-ograms is expected, and also observed, to be much higher than that in normal transmission interferograms. To alleviate this problem, several thousand com interferograms must be averaged. It is wise to distribute these interferograms into different computer files so that, if an instrumen-tal drift or malfunction occurs during the data collection, only the appro-priate files can be rejected rather than the complete set of data. In our experiments, the data collection is done alternately for <om and normal transmission interferograms. About 16 transmission interferograms are coadded into one file, and 250-1000 com interferograms are coadded into another file. Several such files, usually 6-10 each, are accumulated and compared for consistency. To achieve a resolution of 4 cm-1 in the Fourier transformed spectra, the acquisition time for these interferograms would be approximately 1-2 hr for each sample.

According to the sampling theorem (Griffiths, 1975), in order to recon-struct a wave of wavelength λ, the data should be sampled at an interval of λ/2. Commercial FT-IR spectrometers implement this requirement by utilizing an He/Ne laser interferometer as reference. If the data are col-lected at every zero crossing in the output of this laser interferometer, then the spectrum in 0 to (l/λι) cm-1, where λι is the lasing wavelength in the reference interferometer, can be measured accurately. In VCD exper-iments, optical filtering is required due to the nature of the polarization modulation, as explained in Section III.A. Such filtering reduces the bandwidth of the spectral region studied, and hence a sampling interval longer than the shortest sampling interval can be employed for VCD measurements in the region 2000-600 cm 1 . This results in a reduced number of data points to be collected for a given resolution and hence less computer time for Fourier transformation of the interferograms. Also, the computer memory needed to store the Fourier transformed spectra would be less. The main point, however, is the enhancement in VCD signal quality when a proper data sampling interval is chosen. If the data are collected at every fourth zero crossing in the output of the He/Ne laser interferometer, then the maximum wavenumber that can be correctly sampled is (1/4λι) cm-1, and this sampling interval is adequate for VCD measurements with an optical filter transmitting in the region 2000-600 cm-1. In fact, there are two decisive advantages in choosing this sampling interval for measuring VCD below 2000 cm-1. For the data collection at every fourth zero crossing in the output of the He/Ne laser interferome-ter, the time elapsed between two successive data collection points would be (2\i/2V) seconds, where V is the mirror velocity in the Michelson interferometer. For V = 0.5 cm/sec, which is close to the suggested value for most common HgCdTe detectors, this would correspond to 127 sec.


Because the minimum time constant that can be achieved on most lock-in amplifiers is of the order of 100 /xsec, there would be just enough time for the lock-in amplifier to process the com interferogram signal in between the two sampling points. However, if the data were collected at the shortest sampling interval (at every zero crossing in the output of the He/Ne laser interferometer), then the time elapsed between successive data collection points would be (Xi/4V), which for a mirror velocity of 0.5 cm/sec is 32 )Ltsec. Here, the lock-in amplifier would not have enough time to process the signal between two successive data points in com interferograms, and this would reduce the quality of the o>m interferograms. A decrease in mirror velocity by four times would eliminate this reduction in quality, but this choice is not advisable for the reasons already cited, as well as for reasons given later, unless the lowered velocity is the optimal value for the detector employed. The second advantage of choosing the longest permissible sampling interval results from the increased accuracy of the phase correction. It is common practice to take about 200 data points around the ZPD point to derive the phase correction. If the data were collected at every zero crossing in the output of the He/Ne laser interfer-ometer, then several of these 200 data points would actually provide the phase information for spectral frequencies in the region 4000-2000 cm-1. The bandwidth limit for mid-ir VCD measurements, however, requires these spectral frequencies to be filtered out. Then only a few of the data points (approximately one-fourth) among the chosen 200 data points would provide the phase information in the desired 2000 to 600 cm-1

region. Phase correction derived from such a small number of data points is less accurate. If one chose to sample the data at every fourth zero crossing in the output the He/Ne laser interferometer, then most of the 200 data points around the ZPD point would provide the phase informa-tion in the desired 2000 to 600 cm-1 region, and hence the derived phase correction would be more accurate.

For VCD measurements in the region 4000-2000 cm-1, it is preferable to sample the data at every second zero crossing in the output of the He/ Ne laser interferometer. Here, it may prove advantageous to decrease the mirror velocity so that the time elapsed between successive data points exceeds the minimum time constant of the lock-in amplifier.

Choosing a proper sampling interval and mirror velocity for the mea-surements in a chosen bandwidth of the CD experiment in the manner already described can eliminate the oversampling noise, which in fact is evidenced in our VCD measurements in the region 1650-600 cm-1. The increase in VCD signal quality with increasing sampling interval is dem-onstrated in Figs. 12 and 13. The raw VCD spectra for (+)-a-pinene that were measured with three different sampling intervals are displayed in

0.8- ,

1350 1300 1250 1200 1150 1100 1050 1000 950 900 WAVENUMBER

Fig. 12. FT-IR-VCD spectra of (+)-a-pinene (neat liquid) obtained with sampling intervals of (a) 0.3164 urn, (b) 0.6328 urn, (c) 1.2656 urn. An optical filter transmitting in 1650-600 c m 1 is employed in all these measurements. Each spectrum is obtained from 1000 interferograms.

1350 1300 1250 1200 900 1150 MOO 1050 1000 950 WAVENUMBER

Fig. 13 . FT-IR-VCD spectra of (+)-a-pinene obtained with a sampling interval of (a) 0.6328 urn, (b) 1.2656 urn. Each spectrum is obtained as one-half of the difference between the raw VCD of the enantiomers. The spectrum at the 0.6328-um sampling interval is obtained from 5000 interferograms, whereas that at the 1.2656-um sampling interval is obtained from 2000 interferograms. An optical filter transmitting between 1650 and 600 cm ' is employed in both measurements.


Fig. 12. Here, the detail of spectral features has clearly improved with an increase in the sampling interval, and the effect of time constant of the lock-in amplifier is very nicely demonstrated. The VCD spectra corrected for the baseline, by taking one-half of the difference between the VCD of the enantiomers, are shown as a function of sampling interval in Fig. 13. Here the S/N ratio is clearly seen to have improved with an increase in the sampling interval. This observation can be appreciated more if it is noted that the total number of interferograms collected at a longer sam-pling interval is only 2000, whereas that at a shorter sampling interval is 5000.

These results indicate that VCD signal quality can be improved by the choice of a proper sampling interval that is appropriate for the bandwidth in a VCD experiment. It should be noted that increasing the sampling interval indiscriminately can result in folding of the spectral frequencies and in spurious effects.

V. CURRENT STATUS

The FT-IR-VCD measurements can be divided into three regions, based mainly on the sensitivities of the detectors available: (1) 4000-2000 cm"1 region, employing an InSb detector (D* = 1011); (2) 2000-800 cm"1

region, employing an HgCdTe detector (D* = 2 x 1010); (3) 800-400 cm"1

region, employing either an HgCdTe detector (D* = 0.5 x 1010) or a copper-doped germanium detector (D* — 1010). Although VCD measure-ments in the region 4000-2000 cm - 1 are most actively carried out on dispersive spectrometers (Stephens and Clark, 1979), only a few reports have been published on FT-IR-VCD measurements in this region. Owing to the inherent limitations of the dispersive technique for the long-wave-length region and to the definite advantages of FT-IR spectroscopy, FT-IR is the preferred approach to VCD measurements in the region 2000-400 cm- 1 . The VCD spectra shown in the previous sections are presented only in the region 1650-800 cm- 1 due to the transmission properties of an optical filter employed in those measurements. The FT-IR-VCD mea-surements in the region 2000-1650 cm - 1 can be carried out just as easily as they can be carried out in the region 1650-800 cm- 1 . As an example, the VCD in the C = 0 stretching vibration of dimethyl tartrate obtained with an optical filter transmitting in the region 2500-1200 cm"1 is dis-played in Fig. 14. The VCD measurements in the region 800-400 cirr1

require some special attention. First, the HgCdTe detectors used in this range have lower/)* than the detectors used in the region 2000-800 cm- 1. Therefore, it is necessary to use other detectors, such as copper-doped germanium detectors (£>* — 2 x 1010), which require cooling with liquid

90 Prasad L Polavarapu

n 1 r 1800 1775 1750 1725

WAVENUMBER

1700

Fig. 14 . FT-IR-VCD (above) and absorption spectra (below) of dimethyl tartrate (0.01 M in CC14) in the C = 0 stretching region at 8 cm ' resolution. An optical filter transmitting in the re-gion 2500-1200 cm ' is employed in this measurement. The labels D and L repre-sent the VCD bands due to the D and L enantiomers, respectively.

helium. Other points of concern are related to the lower intensity of the light sources at longer wavelengths, the lower transmission of linear po-larizers (usually KRS-5), and, most importantly, the transmission of the PEM constructed with a ZnSe optical element. The transmission of ZnSe drops rather sharply from 700 cm- 1, and beyond 600 cm- 1 the transmis-sion is so low that VCD measurements are not feasible. A better perspec-tive of these practical drawbacks can be obtained from Fig. 15, where the transmission properties of the aforementioned components are displayed. A suitable PEM that permits good transmission toward the far ir has not been developed to date. Owing to this technological limitation, the present VCD measurements can be carried out, at best, down to 600 c m H

(Polavarapu, 1984a,b). A VCD spectrum measured in the region 850-600 cm- 1, employing a KRS-5 polarizer, ZnSe modulator, and HgCdTe detec-tor φ * = 0.5 x 1010), is shown in Fig. 16. Although a suitable PEM for VCD measurements toward the far ir is currently not available, VCD measurements should, in principle, be possible with a polarizing interfer-ometer (Martin, 1980). A theoretical analysis of this polarizing interferom-eter for CD measurements is presented in the literature (Dignam and Baker, 1981), but no experiments have been reported to date.

The usual solvent problems inherent in ir spectroscopy are more severe for VCD measurements. This is because, if some solvent bands and the bands due to the sample appear at the same positions, then only a portion of the absorption recorded will be due to the sample. In VCD measure-ments it is preferable to keep the absorption at any position of interest


9 0 0 850 — i — 750 650 600 800 750 700

WAVENUMBER Fig. 15 . Transmission spectra of optical materials used in VCD measurements. The

optical filter used in these measurements has 5% cut-on at 11 μ-m. Light throughput: (A) ZnSe modulator, (B) ZnSe + filter, (C), ZnSe + filter + KRS-5 polarizer.

below 1.0, due to S/N considerations. If some or most of this absorption is due to the solvent, then the effective amount of light absorbed to sample the VCD is less, and therefore the S/N ratio becomes poor. In other words, the presence of solvent absorption in the region of interest is highly unfavorable, especially because the magnitude of VCD is quite small. Most optically active biological compounds are best studied in H20 and D20 solvents. Because these solvents have strong ir absorption

Fig. 16 . FT-IR-VCD and absorption spectra of </-3-bromocamphor (0.4 M in cyclohexane) in the region 850-650 cm- 1. (a) Background VCD, (b) sample VCD. These spectra were obtained using the procedure adapted for obtaining the spec-tra in Fig. 10.

0.75-!

0.53H < ω cr o <n ω < 0 . 3 M

0.09

ΔΑ = 3χ Ι0"

850 8 0 0 750 700

WAVENUMBER 650


bands, it is necessary to evaluate the feasibility of VCD measurements in these solvents. The strong absorption band of D20 centered around 1200 cm-1 precludes VCD measurements in the region 1300-1100 cm 1 . Al-though some background absorption still exists on either side of this region, VCD measurements are feasible if the sample concentration is chosen to be high and the path length is kept minimal. The region blocked by D20, that is, 1300-1100 cm-1, will have to be studied in another sol-vent. The solent DMSO-d6 is particularly suitable for this region, because it begins to absorb strongly from 1100 cm-1. The VCD spectra of tartaric acid in DMSO-i/6 and D20 solvents are displayed in Figs. 17 and 18, respectively, to illustrate these points. It is also possible to use H20 sol-vent to measure VCD in the region 1200-900 cm-1, provided that the sample concentration is sufficiently high to limit the path length close to 15 /im. Infrared absorption spectra of excellent quality have been re-ported for aqueous sugars (Back et al., 1984) under these conditions. In the C—H stretching frequency region, VCD measurements with D20 solvent are possible even with dispersive instruments (Diem et al., 1978).

D Ll\ DJ

J0T L \ \ \

\\

1.05-1

ÜJ

^ 0.70^ < CD <£ O

£ 0.35H GD

u.uu-j 1 | 1 1 1 1 ( 1625 1540 1455 1370 1285 1200 1115 1030

WAVENUMBER Fig. 17. FT-IR-VCD (above) and absorption spectra (below) of tartaric acid (0.5 M in

DMSO-i4). The labels D and L represent the VCD bands due to the D and L enantiomers, respectively. The trace that runs through the crossings of the D and L bands is the raw VCD spectrum of the racemic mixture.


For liquid samples that have high vapor pressures and for those that do not denature at elevated temperatures, the solvent problem can be avoided by undertaking the VCD measurements in the vapor phase (Po-lavarapu and Michalska, 1983). A VCD spectrum of (5)-(-)-epoxypro-pane measured in the vapor phase is displayed in Fig. 19. For the weaker absorption bands, the VCD was reported earlier employing higher sample pressure (Polavarapu and Michalska, 1983), and all basic features can be seen to be reproducible here, even though the absorption is much weaker in the present case. The VCD spectra in the vapor phase contain contribu-tions from the rotational-vibrational transitions, and these partially re-solved contributions generally give the appearance of noise. The spec-trum shown in Fig. 19 is obtained at a resolution of 4 cm-1, and measurements at higher resolution might be possible.

Ί 1 1 1 1 1 1 1 I 1625 1540 1455 1370 1285 1200 1115 1030 945 860

WAVENUMBER

Fig. 18 . FT-IR-VCD (above) and absorption spectra (below) of tartaric acid (1.5 M in D20). The raw VCD spectrum of the racemic mixture is subtracted from those of the enantiomers. The topmost trace represents the noise level and is obtained as in Fig. 8. The labels D and L represent the VCD bands due to the D and L enantiomers, respectively. The concentration and path length employed are 1.5 M and 30 μπι, respectively.


<3

O Z < ω δ en CD

< 0.05 1200 1140 1080 8 4 0

T 1020 960 900

WAVENUMBER Fig. 19. FT-IR-VCD (above) and absorption spectra (below) of (£)-(-)-epoxypropane

in the vapor phase. The raw VCD spectrum of the racemic mixture is subtracted from that of the enantiomer. The topmost spectrum represents the level of reproducibility in the VCD spectrum.

VI. CONCLUDING REMARKS

The status of FT-IR-VCD measurements has changed considerably in a short period of time. The theoretical and practical aspects of FT-IR-VCD measurements are now better understood. The phase correction in Fourier transforming the ajm interferograms of optically active samples, which was considered problematic until recently, is obtained in a simple manner. Although the first FT-IR-VCD measurements were reported in limited vibrational regions, the current technology permits the measure-ments to be made from anywhere in the near-ir region, all the way down to 600 cm 1 . Furthermore, vapor-phase FT-IR-VCD measurements have been shown to be feasible, which opens a new branch of investigations dealing with the effect of vibrational-rotational interactions on VCD in-tensities (Polavarapu, 1983). FT-IR-VCD measurements in aqueous sol-vents are also possible in limited spectral regions. This possibility is ap-pealing for VCD investigations of optically active biological molecules. Proper attention to the possible appearance of artifacts and the proce-dures for identifying and eliminating artifacts, however, is required for VCD measurements in general.


There are two other areas in which FT-IR-VCD measurements are expected to become popular. First, for molecules isolated in inert matri-ces at cryogenic temperatures, the vibrational bands are much narrower than those for molecules in the liquid phase. The narrow line shapes avoid strong overlap among neighboring bands and are particularly advanta-geous for VCD measurements. This is because the reduced overlap among bisignate VCD bands results in increased individual magnitudes and therefore in an improved S/N ratio (Schlosser et al., 1982). For this reason, FT-IR-VCD measurements on matrix-isolated molecules are ex-pected to become routine. Second, VCD measurements on samples in magnetic fields (Keiderling, 1981) are also expected to be carried out on FT-IR spectrometers, although some modifications to accommodate a high-field magnet around the sample are required.

The extension of FT-IR-VCD measurements to the far-ir region can be considered to be a major goal. In this regard, the concept of a polarizing interferometer is appealing, particularly for VCD measurements in the region 600 to —3 cm-1 (Dignam and Baker, 1981).

ACKNOWLEDGMENT

This work was supported by grants from NIH (GM29375) and Vanderbilt University.

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5644. Dignam, M. J., and Baker, M. D. (1981). Appl. Spectrosc. 35, 181. Griffiths, P. R. (1975). "Chemical Infrared Fourier Transform Spectroscopy." Wiley, New

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496. Lipp, E. D., Zimba, C. G., and Nafie, L. A. (1982b). Chem. Phys. Lett. 90, 1.


Martin, A. E. (1980). In "Vibrational Spectra and Structure" (J. R. Durig, ed.), Vol. 8, p. 1. Am. Elsevier, New York.

Mertz, L. (1967). Infrared Phys. 7, 17. Nafie, L. A., and Diem, M. (1979). Appl. Spectrosc. 33, 130. Nafie, L. A., and Vidrine, D. W. (1979). Proc. Soc. Photo-Opt. Inst. Eng. 191, 56. Nafie, L. A., and Vidrine, D. W. (1982). In "Fourier Transform Infrared Spectroscopy"

(J. R. Ferraro and L. J. Basile, eds.), Vol. 3, p. 83. Academic Press, New York. Nafie, L. A., Keiderling, T. A., and Stephens, P. J. (1976). J. Am. Chem. Soc. 98, 2715. Nafie, L. A., Diem, M., and Vidrine, D. W. (1979). J. Am. Chem. Soc. 101, 496. Nafie, L. A., Lipp, E. D., and Zimba, C. G. (1981). Proc. Soc. Photo-Opt. Inst. Eng. 289,

457. Osborne, G. A., Cheng, J. C , and Stephens, P. J. (1973). Rev. Sei. Instrum. 44, 10. Polavarapu, P. L. (1983). Bull. Am. Phys. Soc. [2] 28, 1343. Polavarapu, P. L. (1984a). Appl. Spectrosc. 38, 26. Polavarapu, P. L. (1984b). In "Vibrational Spectra and Structure" (J. R. Durig, ed.), Vol.

13, p. 103. Am. Elsevier, New York. Polavarapu, P. L., and Michalska, D. F. (1984). Mol. Phys. 52, 1225. Polavarapu, P. L., and Michalska, D. F. (1983). J. Am. Chem. Soc. 105, 6190. Polavarapu, P. L., and Michalska, D. F. , and Back, D. B. (1984). Appl. Spectrosc. 38, 438. Potter, M. C. (1978). "Mathematical Methods in the Physical Sciences." Prentice-Hall,

Englewood Cliffs, New Jersey. Schlosser, D. W., Devlin, F., Jalkanen, K., and Stephens, P. J. (1982). Chem. Phys. Lett.

88, 286. Stephens, P. J., and Clark, R. (1979). In "Optical Activity and Chiral Discrimination" (S. F.

Mason, ed.), p. 263. Reidel Publ., Dordrecht, Netherlands.

3 ADVANCES IN CAPILLARY GAS CHROMATOGRAPHY-FOURIER

— TRANSFORM INTERFEROMETRY

K. Krishnan

Digilab Division Bio-Rad Laboratories Cambridge, Massachusetts

Introduction The GC-FT-IR System A. Hardware B. Software Applications A. Sensitivity B. Oils and Perfumes C. Pesticides and Environmental Pollutants D. Other Applications Use of Combined GC-FT-IR and GC-MS Data Alternative Approaches to GC-IR Summary References

97 98 98

104 115 115 117 127 138

138 142 143 143

I. INTRODUCTION

In recent years the study of gas Chromatographie fractions by Fourier transform interferometry (GC-FT-IR) has emerged as one of the major applications of FT-IR. The status of GC-FT-IR has been reviewed by Griffiths (1975, 1978), Erickson (1979), and Krishnan and Ferraro (1982). All of these earlier contributions reviewed GC-FT-IR work done with packed GC columns. The first commercial capillary GC-FT-IR systems were introduced in 1981, bringing the technique closer to gas chromatog-raphy-mass spectroscopy (GC-MS) in sensitivity and software sophisti-cation. In this chapter the hardware and software requirements and appli-cations of capillary GC-FT-IR are reviewed. While this chapter was being prepared, an excellent review of the status of current capillary GC-FT-IR instrumentation was published by Griffiths et al. (1983). The reader


ISBN 0-12-254104-9

98 K. Krishnan

is referred to that article for the specifics of different commercial capillary GC-FT-IR systems.

This chapter attempts to cover the capillary GC-FT-IR literature through the end of 1983. Most of the spectra in Section III were recorded in the author's laboratory (with the invaluable assistance of Mr. Stephen L. Hill) using the Digilab capillary GC-FT-IR system. The author would like to emphasize that spectra of similar quality could no doubt be ob-tained using any commercial GC-FT-IR system available in the market-place.

II. THE GC-FT-IR SYSTEM

A. Hardware

Any GC-FT-IR system consists of a GC-IR accessory interfaced to the FT-IR system (Fig. 1). The effluent from the GC column is directed by means of suitable heated transfer lines to a heated GC-IR gas cell or light pipe. The light pipe is typically made of a stainless steel, glass, or quartz tube that is coated with gold on the inside and enclosed by infrared (ir) -transmitting windows. The ir beam from the FT-IR instrument is focused on one end of the light pipe, and the transmitted radiation detected by a high-sensitivity ir detector. The GC effluent coming out of the light pipe can be directed toward a conventional GC detector (flame ionization, thermal conductivity, etc.). Thus, in addition to recording the GC-FT-IR data, one would also obtain a gas chromatogram of the sample. An alter-native way of obtaining the gas chromatogram consists of placing an effluent splitter between the GC column and the light pipe, sending a small portion of the effluent directly to the GC detector. The latter approach may be less desirable, however, in that the recorded gas chromatogram may not reflect any degradation of the GC performance suffered in the light pipe due to volume mismatch, cold spots, etc.

Once the GC-IR accessory is in place, FT-IR data collection can be started simultaneously with the injection of a sample into the Chromato-graph. Interferograms can be collected fast enough to keep up with the GC resolution (typically, at least one interferogram per second for capil-lary columns). Infrared reconstructed chromatograms can be produced either during or after the data collection. At the end of a typical GC-IR data acquisition sequence, one would have the gas chromatogram, ir spectral data, and reconstructed chromatograms for producing the ir spectra of the Chromatographie peaks of interest. The final step of the GC-IR analysis sequence is to perform a search of the spectral data against standard ir vapor-phase libraries.

3 Advances in Capillary GC-FT-IR 99

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Fig. 1. Block diagram of a typical capillary GC-FT-IR system. From Borman (1982).

In the following sections, the hardware and software requirements of capillary GC-FT-IR systems are discussed in detail.

1. The GC-IR Light Pipe

As mentioned earlier, the light pipe is glass or stainless steel tubing, the inside walls of which are coated with gold. The procedure for producing high-quality gold coating has been described by Azarraga (1980). The Pyrex or quartz tubing to be gold coated is thoroughly cleaned and dried under a nitrogen stream. The tube is then mounted vertically, and a uni-form capillary film of Hanovia Liquid Bright Gold (Hanovia Corp.) is deposited on its inner wall. The gold film is then allowed to dry, and the tube is baked at temperatures between 450 and 550°C. A high flow rate of nitrogen or air is maintained through the tubing during the firing process to prevent any deformation of the gold film. At the end of the baking

100 K. Krishnan

process, the light pipe is put through another cleaning and drying cycle. With careful practice, it is possible to produce gold coatings that exhibit very high reflectivity in the ir region and resist peeling and cracking. Figure 2 shows schematically a typical GC-IR light pipe, including the transfer lines and the ir-transmitting windows. As can be seen from the figure, when the ir beam is focused on one end of the light pipe, it under-goes multiple reflections from the gold-coated walls, effectively increas-ing the path length.

For the best GC-IR performance, the volume of the light pipe should be matched to the volumes of the typical GC fractions under study. Light-pipe design has been discussed in detail by Griffiths (1977) and Erickson (1979). For optimum performance, the volume VLp of the light pipe should be equal to VV2, the half-volume of the GC fraction (i.e., the volume of the carrier gas between the half-height points). If VLP is larger than Vm, more than one GC fraction may be passing through the light pipe during the ir measurements, leading to the degradation of the GC resolution. Make-up gas will have to be added to the inlet of the light pipe to regain the GC resolution. The GC fractions would be diluted, however, and the GC-IR detection limit raised. If VL? is smaller than Vm, only a part of each GC fraction will be traversing the light pipe during the ir measure-ments, once again leading to increased GC-IR detection limits or lower sensitivity.

Given that a light pipe has been designed to match the volume of the GC fraction optimally, one can produce light pipes having various length/ diameter ratios. For instance, the commercially available light pipes for packed-column GC-FT-IR studies have volumes of ~2 ml. The two most

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popular light pipes, however, have diameter/length radios of around 2.4/ 600 mm (Digilab) and 3/400 mm (Nicolet), respectively. In principle, the longer the light pipe, the greater is the ir path length and the higher the sensitivity, provided that the light pipe has reasonable transmission char-acteristics. Capillary GC fractions have much lower volumes, and the capillary light pipes of similar volumes will have to be used. If the light pipes designed for packed-column work were to be used for capillary GC-IR studies, a considerable amount of make-up gas would have to be introduced. Capillary GC-IR light-pipe design also has to take into ac-count other considerations, such as the diameter and length of the capil-lary GC columns and the temperature programming used in the GC exper-iment. Depending on the column and experimental characteristics just described, the volumes of typical GC fractions may range from 50 to 300 μ,Ι. Considering all these factors, one approach to designing a light pipe for capillary GC-IR is to match the light-pipe volume to the volume of a low-temperature eluent from a column of given dimensions. There will be a slight loss of GC-IR sensitivity for the high-temperature eluents, and a small amount of make-up gas will have to be used when columns of fairly different dimensions are to be used (Kuehl, 1981).

A test of the light-pipe optimization for capillary GC-IR experiments is to record the gas chromatogram with the GC column connected directly to the GC detector and to compare this with the chromatogram recorded with the light pipe in place between the column and the GC detector. Figure 3 shows such results employing a 0.5 mm x 20 m capillary column and a light pipe designed specifically for the dimensions of this column (Kuehl, 1981). One can see little or no degradation of the GC resolution due to the light pipe.

The complete GC-IR light-pipe assembly includes the light pipe, trans-fer lines, and temperature-control circuitry. Most commercial capillary GC-IR systems employ light pipes that can be heated to ~320°C. The GC-IR systems are optimized for work with specific column dimensions (for instance, the Digilab system is optimized for use with 0.32-mm-i.d. capil-lary columns with no need for make-up gas) or to allow the facility to introduce a considerable amount of make-up gas between the column and the light pipe (McClure, 1983).

2. The FT-IR Instrument

The ir transmission of most GC-FT-IR light-pipe assemblies is —10%. To offset this loss of optical throughput, high-sensitivity MCT (mercury cadmium telluride) ir detectors are used in the GC-FT-IR systems. De-pending on their actual stoichiometry, different MCT detectors exhibit

102 K. Krishnan

I'll u u

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Fig. 3 x 20 m detector. detector.

Flame ionization chromatograms of a solvent mixture recorded using a 0.5 mm WCOT glass column. Top: column connected directly to the flame ionization Bottom: column connected to the GC-IR light pipe and then to the flame ionization

different spectral responses and sensitivities. The MCT detectors most commonly used in FT-IR instruments can be classified as wide range or narrow range. The wide-range MCT detector has a sensitivity about four times greater than the room-temperature DTGS (deuterated triglycine sulfate) pyroelectric detector and can respond over the entire mid-ir range (4000-400 cm-1)· The narrow-range MCT detector is about four times as sensitive as the wide-range one, but at the cost of reduced spectral cover-age (4000-700 cm-1, typically). Figure 4 (Griffiths et al, 1983) shows the relative sensitivities of different MCT detectors. For GC-FT-IR applica-tions, the narrow-range MCT detector is most often used, and the GC-IR system is so optimized as to produce the largest possible signal from the detector without exceeding the dynamic range of the analog-to-digital (A/ D) converter used in the FT-IR system with the light pipe kept at room temperature. The transmission of the light pipe is lowered at elevated temperatures, perhaps due to a change in the reflectivity of the gold coating.

3 Advances in Capillary GC-FT-IR

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3. GC-IR Data Collection

Most fractions separated by a medium-bore capillary column (0.32 mm i.d.) have half-widths ranging from 1 to 5 sec. Thus, the FT-IR system should be capable of collecting data at least once every second in order to keep pace with the GC resolution. Furthermore, as shown in Fig. 5 (Grif-fiths et al., 1983), the sensitivity of the MCT detector increases with increasing modulation frequency, reaching a plateau at a limiting fre-quency. Because the modulation frequencies generated by the Michelson interferometer increase linearly with the velocity of the moving mirror, higher scanning speeds can be employed in the GC-FT-IR systems. The velocity of the moving mirror is so adjusted as to produce modulation frequencies in the plateau region shown in Fig. 5. Under these conditions,

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Griffiths et al. (1983). Copyright 1983 American Chemical Society.

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104 K. Krishnan

the signal-to-noise (S/N) ratio in the ir spectrum produced from a single scan of the interferometer will be optimum. At higher scan speed, the duty cycle efficiency will decrease with a corresponding reduction in the S/N of a single scan (Griffiths, 1983; Griffiths et aL, 1983).

As already indicated, GC-IR data will have to be collected at least once every second to keep abreast of the GC resolution. In the FT-IR system, data collection times increase and the S/N ratios per scan decrease with increasing spectral resolution employed in the data collection. In view of these factors, spectral resolution of only 8 (or 4) cm-1 is usually employed in most GC-IR data collection. However, as will be seen in later sections, data so produced will be of sufficiently high quality and contain enough spectral information for identifying the various GC fractions.

Typical real-life GC samples may contain tens, if not hundreds, of components, and the Chromatographie separation may take anywhere from 30 min to 2 hr. An enormous amount of data may be collected, and GC-FT-IR systems usually incorporate large data storage capacities. Win-chester magnetic disk drives with data storage capacities ranging from 24 to more than 300 megabytes are commonly used in such systems.

B. Software

1. Data Collection

Once the GC-FT-IR experimental parameters, such as scan speed, spectral resolution, and time resolution (frequency of data collection) have been determined, there are two alternative approaches to the actual acquisition of GC-FT-IR data. The first approach is simply to collect the data continuously during the entire GC run. The second approach is to use "thresholding." In the earliest commercial GC-FT-IR system (that of Digilab) in the early 1970s, there was a hardware interface between the gas Chromatograph and the FT-IR instrument. When the GC detector sensed a GC peak above a preselected threshold, the FT-IR system col-lected data from this time until the GC signal fell below this threshold. This hardware threshold was replaced by a software threshold derived from low-resolution ir functional-group reconstructed chromatograms in the later commercial systems, for instance, the "chemigram" approach in the Nicolet instruments (Coffey et al., 1978). The thresholding method was devised primarily to overcome the limited data storage capacities of the earlier FT-IR systems and worked effectively with packed-column gas chromatographs where the GC fractions were few and temporally well separated. This approach suffered from the drawback that GC-IR data could not be collected over the GC fractions not detected by the GC


detector (the hardware approach) or the GC fractions producing ir absor-bances outside the spectral regions used for creating the functional-group reconstructed chromatograms. In capillary GC-IR work, particularly when samples contain a large number of closely eluting components, there is effectively no difference between the thresholding and non-thresholding approaches of data collection. All current commercial GC-FT-IR systems do offer the facility for employing software thresholding. Regardless of thresholding, during the GC-FT-IR data collection process a number of individual interferograms or sets of interferograms will have been collected and stored in the data system. At the end of data collec-tion, the interferograms (or sets) spanning each of the GC eluents of interest can be coadded, Fourier transformed, and ratioed against a previ-ously collected reference spectrum to obtain the GC-FT-IR spectra of interest.

2. Reconstructed Chromatograms

The process of coadding interferograms to produce GC-IR spectra is greatly aided by the use of ir reconstructed chromatograms. These chro-matograms can be reconstructed from spectra (functional-group chro-matogram, FGC) or from interferograms (Gram-Schmidt chromatogram, GSC). The FGCs are created by monitoring all of the collected GC-IR data and monitoring the absorbances over specified ir spectral regions as a function of time. This procedure is shown schematically in Fig. 6. During data collection, the FGCs can be created by Fourier transforming 256 or 512 points of each collected interferogram in real time and using the low-resolution spectral data thus obtained. In GC-FT-IR systems incorporat-ing an array processor, the actual 8 cm-1 spectral data can be used to produce the real-time FGCs (Krishnan et al., 1981).

The FGCs are very useful in capillary GC-IR data reduction because they can be used to identify easily the GC fractions that belong to a particular chemical class (esters, alcohols, etc.) in a complex mixture. As already pointed out, however, FGCs may not be very useful for thresholding during data collection. In general, the spectral regions from which the FGCs are to be created will have to be specified before the start of data collection, necessitating knowledge of the chemistry of the GC fractions over which data are to be collected. If data were to be collected over all the fractions eluting from the Chromatograph, one could, in prin-ciple, specify the whole mid-ir range (4000-700 cm-1 in the case of most GC-FT-IR systems) as one of the spectral regions to be used in creating the FGCs. As the spectral range for creating the FGC increases, however, so does the noise, and the SIN ratio of the resulting FGC may be very

106 K. Krishnan

CHROMATOGRAM 1 CHROMATOGRAM 2 POINT POINT

Fig. 6. Schematic representation of functional-group chromatogram reconstruction: integration method.

poor. The GSC eliminates this problem, allowing reconstructed chro-matograms to be created that are sensitive to absorbance over the entire mid-ir spectral range.

The GSC is created directly from the interferograms following the pro-cedure first outlined by deHaseth and Isenhour (1977) and described by Hanna et al. (1979a). This vector orthogonalization procedure, shown schematically in Fig. 7, makes use of the fact that every point of an interferogram contains contributions from the entire spectrum. Any n points of an interferogram can be represented as a multidimensional vector in a vector hyperspace. A reference subspace can be created by collecting a number of interferograms with only the carrier gas flowing through the GC-IR light pipe and producing the corresponding vectors and orthogona-lizing them. When a GC fraction is passing through the light pipe, a basis vector can be created from the corresponding interferogram. The Euclid-


References Sample

Fig. 7. Schematic representation of Gram-Schmidt chromatogram reconstruction. Value = |I - P| = (I I - I IB, - I B2 - )12.

ean distance of this (sample) vector from the reference subspace will depend on the absorbances (and hence the concentrations) of the sample in the light pipe. Under favorable conditions (Beer's law holding true, the gas chromatogram being sampled exactly reproducibly, etc.), the magni-tude of the GSC will be expected to be linear with the sample concen-tration.

In actual practice, the GSC can be created simultaneously with [in FT-IR systems incorporating an array processor (Krishnan et al., 1980)], or

108 K. Krishnan

more commonly at the end of, the GC-FT-IR data collection. The points of the interferograms used in the vector orthogonalization procedure can be chosen away from the center bursts (the point of zero retardation) of the interferograms (deHaseth and Isenhour, 1977) or around the center bursts (White et aL, 1981). Regardless of which of these methods is used to create the GSC, it seems clear that the S/N ratio of the GSC is higher than that of the FGC (Griffiths et al, 1983). That the GSC clearly follows the sample absorbance can be seen from Fig. 8 (Simonoff et al., 1981). This figure shows the GSC of isobutyl methacrylate from the GC-FT-IR data collected using a packed column in the gas Chromatograph. A scan set of three interferograms was collected and coadded every 750 msec, and 14 such sets could be collected over the time when the isobutyl methacrylate was passing through the light pipe. The figure shows the GSC profile for this elution, and the carbonyl stretching band of isobutyl

187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 Scan sets

Fig. 8. Plot showing the Gram-Schmidt chromatogram peak and the C = 0 stretching band from isobutyl methacrylate.


methacrylate from each scan set. One can see the excellent sensitivity of the GSC to the changing sample absorbance. If no thresholding were used and the GSC were created over the entire GC elution time of a complex mixture, the GSC would represent the ir detector output for the gas Chro-matograph. Thus, in principle, the GSC would be equivalent to the total-ion reconstruction in a GC-MS experiment. More importantly, only those components of a complex mixture separated by the GC that can be seen in the GSC would be likely to produce ir spectra. Thus, the GSC would greatly facilitate the data reduction procedure in the GC-FT-IR experi-ment. For instance, Fig. 9 shows a comparison between the flame ioniza-tion detector (FID) chromatogram and the GSC of a perfume mixture. A

Gram-Schmidt

Scan sets 1 174 348 499 638 777 914 1030 1146 1262 1368 1468 1567 1666 1753 1840 i 1 1 1 1 1 1 1 1 1 1 1 1 1 I '

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Fig. 9. Comparison between Gram-Schmidt (bottom) and flame ionization (top) chro-matograms of a perfume mixture.

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0.32 mm x 40 m SE-54 WCOT fused silica column was used in the experiment, and data were collected without thresholding for 40 min. One can see that most of the peaks in the FID chromatogram appear in the GSC. The relative intensities of the peaks are different in the two chro-matograms because polar and nonpolar compounds absorb differently in the ir region.

In capillary GC-FT-IR the sensitivity of the GSC can be reduced by poor spectrometer purge and by system instabilities. If the FT-IR system is poorly purged, the absorbances due to water vapor and C02 might be much higher than the absorbance due to a particular GC fraction. Because the GSC will be sensitive to water vapor and C02 absorbances as well, poor purge will have the effect of raising the baseline in the GSC and even obscuring the GSC peaks due to weakly ir-absorbing compounds. Mois-ture contained in the sample under study and bleed from poorly condi-tioned capillary columns will have similar effects on the GSC. The situa-tion can be remedied by choosing a region of the chromatogram containing the poor purge or column bleed for creating the reference subspace to be used in the GSC. Figure 10 illustrates the efficiency of such a procedure. The GSC shown is that of a solvent mixture separated by a 0.32 mm x 40 m SE-30 WCOT fused silica column. In the lower figure, the FT-IR instrument purge was stopped as soon as the reference interferograms were collected before the injection of the sample into the gas Chromatograph. As more and more water vapor and C02 enter the system, the baseline of the GSC increases, reaching a plateau when the water vapor and C02 concentrations have reached a steady state. The upper figure shows the GSC recreated using references from this steady-state region, marked by the arrows. One can now clearly see peaks in the GSC (e.g., at 6.4 and 10.5 min) that could not be seen in the lower figure.

Infrared reconstructed chromatograms can be used for a very rapid analysis of the chemical classes of a complex mixture. Figure 11 shows the GSC and five different FGCs of a coal extract. The GSC, recorded over 40 min, contains —175 peaks. The FGC created over the aliphatic C—H stretching region (2840-3000 cm-1) clearly shows hydrocarbons of different carbon numbers eluting at periodic intervals. The FGC over the carbonyl stretching region (1680-1800 cm1) indicates the esters, ketones, aldehydes, and lactones in the coal extract.

Fig. 10. Gram-Schmidt chromatograms of a solvent mixture recorded with poor in-strument purge. Bottom: using references collected before the injection of the sample. Top: using references from the marked area of poor purge. Note the appearance of peaks in the top chromatogram that were totally masked by poor purge in the bottom chromatogram.

112 K. Krishnan

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Fig. 11. Gram-Schmidt and functional-group chromatograms of a liquefied coal ex-tract. Window regions used in the creation of various functional-group chromatograms (cm-·): A, 822-961; B, 1682-1802; C, 961-1099; D, 2839-3001; E, 1161-1300.


In a series of papers, Malissa (1982a,b, 1983) evaluated the characteris-tics of the FT-IR instrument as a GC detector. In the earlier experiments, he used a packed column in the gas Chromatograph and a light pipe 40 cm x 2.4 mm. For various injections of samples, such as salicylaldehyde, methyl salicylate, 2-chlorophenol, phenol, and 1,1,1-trichloroethane, he found that the response factor R (computed from the area of the peaks in the GSC) was not linear with the sample amounts. When a 0.32-mm capillary column and a 7 cm x 2 mm light pipe were used, Malissa (1983) found that the GSC did indeed have a linear response range and that quantitative determinations could be made in this range using the FT-IR detector. Malissa also demonstrated that a photoionization detector placed between the GC column and the GC-IR light pipe exhibited a much larger linear response range and could be used for quantitative measure-ments.

3. Spectral Search

A typical capillary GC-FT-IR experiment may produce tens of ir spec-tra, and search software routines are available for identifying these spec-tra. The most commonly used GC-FT-IR spectral search routines are based on the GIFTS software developed by Azarraga and Hanna (1979). The search routines are also described by Hanna et al. (1979b) and use a vapor-phase library for compound identification. The recorded FT-IR va-por-phase spectrum is first normalized so that the strongest band in the spectrum has unit absorbance. In the so-called de-resolved format, the data (recorded at 8 or 4 cm-1 spectral resolution) are digitized every 8 cm"1 between 3250 and 2600 cm"1 and between 2100 and 690 cm"1. Be-cause all the entries in the spectral libraries are coded in this fashion, any unknown spectrum to be searched is first reduced to the library format. The reduced spectrum is then compared with every entry in the library and a hit quality index (HQI) is produced at the difference between the unknown and the library spectra:

HQI = Σ (Si ~ Li)2

i

Here, 5/ is the absorbance of the unknown spectrum at frequency /, and L, the corresponding value from the library spectrum; the summation is carried out over the frequency range of interest. For a perfect match, HQI will be zero. This search routine works very well with GC-FT-IR data in spite of the low resolution used and the omission of significant regions of the mid-ir spectrum such as the hydroxyl stretching region. Search results can be printed out, and most search routines also allow a visual compari-

114 K. Krishnan

_J__JIA

1 1 1 1 3500 3000 2570 2000

Wavenumber Fig. 12. Plot of a typical GC-FT-IR spectral search results. A, Entry 01985 946-02-1,

1-butanol, 4-chloro-, benzoate, HQI = 0.14; B, entry 00126, 120-50-3, benzoic acid, isobutyl ester, HQI = 0.12; C, entry 00799, 2315-68-6, benzoic acid, propyl ester, HQI = 0.11; D, entry 02981, 94-46-2, benzoic acid, isopentyl ester, HQI = 0.10; E, entry 01716, 136-60-7, benzoic acid, butyl ester, HQI = 0.06. Spectra copyright Sadtler Research Laboratories, Division of Bio-Rad Laboratories, Inc.

4000 I

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son of the unknown spectrum and the various identifications. Figure 12 shows such a comparison, with the unknown spectrum at the bottom and the identified library spectra above it. From bottom to top, the HQI in-creases for the library spectra, indicating decreased probability of that positive identifications will be made. In general, positive identifications can be made when the spectrum being searched is in the library. A vapor-phase library (VAP-IR) containing around 8000 spectra is available from Sadtler Research Laboratories, Philadelphia. As can be seen from Fig. 12, the identifications all belong to compounds of a similar chemical class, and thus even if the library did not contain the spectrum being searched, the search routine could yield its chemical functionality.

Most commercial GC-FT-IR systems incorporate the sophisticated hardware and powerful computing systems needed to fulfill most of the capillary GC-IR requirements outlined here. As pointed out by Griffiths (1983) some commercial systems have very high GC-IR sensitivities and are capable of real-time GC-IR data collection. Such systems can simulta-neously collect the interferograms, create the GSC or FGCs, Fourier transform the interferograms, and produce and display the absorbance spectra and the reconstructed chromatograms, all in 750 msec. Only the spectral search must be done at the end of the data collection.

III. APPLICATIONS

A. Sensitivity

Before the development of capillary systems, it was possible to obtain the ir spectra of GC fractions routinely in the range 200-500 ng using packed columns in the Chromatograph. The best reported sensitivity using packed-column GC-IR was 50 ng of isobutyl methacrylate (Krishnan et al., 1979). The capillary columns produce narrow, concentrated Chro-matographie fractions. This effect can be seen in Fig. 13, which shows the GSC of the same solvent mixture obtained using a packed (2 mm i.d.), a wide-bore (0.8 mm i.d.), and a medium-bore column (0.5 mm i.d.). The use of capillary columns should thus result in increased GC-FT-IR sensitiv-ity, and, indeed, the GC-FT-IR spectrum of 3 ng of isobutyl methacrylate has been reported (Fig. 14; Kuehl, 1981). Good-quality capillary spectra of 40 ng of ethyl acetate and dioxane (Fig. 15) were reported by Smith et al. (1983). It is now possible to obtain the capillary GC-IR spectra of materials routinely in the range 5-50 ng, polar materials such as esters having the lower detection limit.

Beadle et al. (1983) reported on the sensitivity of the GSC and search routines at these low detection limits. The sample used in this study was a

116 K. Krishnan

A f

c >* IIUW u kJ Time (min)

Fig. 13. Gram-Schmidt chromatograms of a solvent mixture recorded using a g-in. (A), 0.8-mm (B), and 0.5-mm (C) column.


0.1000-4

0.0400 4-

-0.0200 3000 2500 1500 1000 850 2000

Wavenumber 3 ng of isobutyl methacrylate

Fig. 14. Capillary GC-FT-IR spectrum of 3 ng of isobutyl methacrylate. From Kuehl (1981).

synthetic mixture containing compounds of different chemical classes. Figure 16 shows the FID chromatogram of this mixture (0.32 mm x 40 m, SE-30 WCOT fused silica column) and the GSCs for injection levels cor-responding to 20 and 40 ng per compound. As would be expected, more peaks are to be seen in the GSC corresponding to 40 ng. Figure 17 shows the 20-ng spectrum of 1,3,5-trichlorobenzene along with the spectral search results showing the proper identifications. Figures 18 and 19 show the spectra and search results for 40 and 20 ng of anisole, respectively. As can be seen, the 20-ng anisole spectrum is quite noisy, and yet the search identification is correct.

B. Oils and Perfumes

Most natural products such as oils, perfumes, coals, and petroleum distillates contain many isomeric compounds. Because most of these iso-

118 K. Krishnan

1.01 (a)

ΓΊ]

0.98 4000 3500 3000 2500 2000 1500 1000

Wavenumber

Fig. 15. Capillary GC-FT-IR spectra of 40 ng (a) ethyl acetate, (b) dioxane. From Smith et al. (1983).

mers exhibit distinctly different ir spectra, the GC-FT-IR methodology lends itself to the study of these products. The packed-column GC-FT-IR technique has been used with great effectiveness in the study of perfumes and oils (Jacobs et al., 1980; Namba, 1982, 1983). Figure 20 shows, for instance, the GSC and FCGs of jasmine oil separated by a 2% silicone (OV-17) -packed column (Namba, 1982). Most of the major constituents of jasmine oil can be easily identified, as indicated in the figure. Figure 21 shows two of these components: linalool and c/s-jasmine lactone. Mc-Clure (1983) studied jasmine oil using a 0.32 mm x 50 m methyl silicone column. The light pipe was 2.7 mm i.d. x 40 cm long, and make-up gas was used to preserve the Chromatographie resolution. Excellent spectra of such components as linalool and benzyl acetate and detailed analyses of jasmine oil have also been reported.

HÜJ

Gram-Schmidt

LU ΑΜλ.

y j ϊ HW**«r^J

1 177 353 529 671 1.00 3.50 6.00 8.50 11.00 13.50

Gram-Schmidt

UMAUMWUv<**^W

Scan sets Minutes

177 353 529 705 8.50 11Ό0 1.00 3.50 6.00

Fig. 16. Flame ionization and Gram-Schmidt chromatograms of a solvent mixture. A, Flame ionization chromatogram; B, Gram-Schmidt chromatogram, 20 ng per fraction; C, Gram-Schmidt chromatogram, 40 ng per fraction.

3000 2000 1000

r ~r HvAM*

4000 3500 3000 1500 1000 500 2500 2000 Wavenumber

Fig. 17. Spectrum of 20 ng of 1,3,5-trichlorobenzene (left) and the search results (right). A, Entry 02672 18708-70-8, benzene, 2-nitro-l,3,5-trichloro-, HQI = 0.81; B, entry 02561 601-88-7, benzene, l,9-dichloro-2-nitro, HQI = 0.75; C, entry 04488 2645-22-9, al-drithiol-4, HQI = 0.74; D, entry 02199 108-70-8, benzene, 1,9,5-trichloro-, HQI = 0.55. Spectra copyright Sadder Research Laboratories, Division of Bio-Rad Laboratories, Inc.


2000 1000

rf0^ ^JKMJ 4000 3500 3000 2500 2000 1500 1000 500

Wavenumber Fig. 18. Spectra and search results for 40 ng anisoie. A, Entry 00019 109-79-1, ben-

zene, ethoxy-, HQI = 0.79; B, entry 06941 1562-94-8, azoxybenzene, 4,4-propyldimethoxy-, HQI = 0.71; C, entry 04277 7095-08-2, acetonitrile, O-methoxyphenyl, HQI = 0.63; D, entry 01850 100-66-8, anisoie, HQI = 0.62. Spectra copyright Sadtler Research Laboratories, Division of Bio-Rad Laboratories, Inc.

122 K. Krishnan

}ty*(% VW||

3000 2000 1000

_ /v

ΑΑΛ^

4000 3500 3000 2500 2000 1500 1000 500

Waven umber

Fig. 19. Spectra and search results for 20 ng anisole. The search result is correct, albeit with high HQI. A, Entry 09000 1126-78-0, ether, butyl phenyl, HQI =1.10; B, entry 04277 7085-09-2, acetonitrile, 0-methoxyphenyl, HQI =1.10; C, entry 06941 1562-94-9, azoxyben-zene, 4,4-propyldimethoxy-, HQI = 1.09; D, entry 01650 100-66-8, anisole, HQI = 1.09. Spectra copyright Sadtler Research Laboratories, Division of Bio-Rad Laboratories, Inc.


»Ό-Η

^ * * A ^ L ^ ^ V*W-/***♦*

255 509 930 0.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00

Time (min)

Fig. 20. Gram-Schmidt and functional-group chromatograms of jasmine oil. From Namba (1982). The major constituents are identified.

Wilkins et al. (1982) reported the GC-FT-IR spectra of peppermint oil separated by a wide-bore (0.44 mm i.d. x 35 m) glass Carbowax 20m SCOT capillary column. Using the GC-IR data and the complementary GC-MS data, the authors were able to identify most of the previously known constituents of this oil.

Sun et al. (1983) studied the degradation of essential oils by capillary GC-FT-IR. They recorded the spectra of pure limonene and aged li-monene and were able to identify some of the degradation products in the aged specimen.

Kuehl and Olson (1981) reported on the capillary GC-FT-IR analysis of rose oil and geranium oil. Figures 22 and 23 show the GSCs of these two oils respectively. Whereas good-quality ir spectra could be obtained for

124 K. Krishnan

π 1 1 r 3800 3450 3100 2749 2399 2049 1699 1348 998 648

Wavenumber

Fig. 21. GC-FT-IR spectra of linalool (a) and ds-jasmine lactone (b) from jasmine oil.

3 Advances in Capillary GC-FT-IR

3.0

125

Gram-Schmidt

kifUl UAMJWLW '»1*Λ*Λ/ U^Jl/W^J UM^V^AMAJ Ι^νΛΛ^-νΛ^*/

oJ

Scan sets

Minutes 184

— ] — 356 502 647 769 891 999

2.00 4.50 7.00 9.50 12.00 14.50 17.00 19.50 Fig. 22 . Gram-Schmidt chromatogram of rose oil recorded using a 0.5 mm x 20 m

OV-1 WCOT glass column.

all the peaks present in the GSCs, positive identifications of the minor fractions could not be made due to the absence of a sufficiently large vapor-phase library. Krishnan et al. (1983) also studied the capillary GC-FT-IR spectra of sandal wood oil. The natural oil, produced by Karnataka Soaps and Detergents Ltd., Bangalore, India, was separated using a 0.32 mm i.d. x 40 SE-30 WCOT column. From the chemical analysis of this

126 K. Krishnan

5.0 -i

Gram-Schmidt

IL.MJW1LWJ U

Scan sets Minutes

1 184 344 490 625 747 864 — i —

968 1071

2.00 4.50 7.00 9.50 12.00 14.50 17.00 19.50 22.00 Fig. 23. Gram-Schmidt chromatogram of geranium oil recorded using the column of

Fig. 22.

oil, it is known that the major constituents are a- and /3-sandalol and the corresponding aldehydes. The GSC and FGCs (monitoring the regions 3030-3100 c m 1 and 1700-1740 cm1) in the elution range of the major constituents of the oil are shown in Fig. 24. The FGCs show that the two strongest peaks in the GCS are due to overlapping constituents, not fully resolved by gas chromatography. These can be separated using the FT-IR data by creating the GC-IR spectra corresponding to the elution ranges


MlAr ΐΛ^ΛΛΛ^Λννΐ

k/uAwvv^ 13 14

Time (min) Fig. 24 . Gram-Schmidt (A, B) and functional-group chromatograms (C) of sandal-

wood oil; (A) 1720-1700 c m 1 , (B) 3080-3060 cm '.

indicated by the FGCs. The spectra for the peak eluting at 12.49 min are shown in Fig. 25. One can clearly see the changes in the carbonyl stretch-ing region in these spectra. By mutual absorbance subtraction of these two spectra, one can obtain the pure ir spectra of these overlapping con-stituents, which can be identified as ß-sandalol and the corresponding aldehyde (Fig. 26). Thus, under certain conditions, the GSC and the FGCs can be used effectively to enhance GC resolution.

C. Pesticides and Environmental Pollutants

Another major application of the GC-FT-IR technique is in the study of pesticides and environmental pollutants. Kalisinksy (1981) and Gurka et al. (1982) published results in this area of GC-IR application using packed columns. Griffiths et al. (1983) obtained the capillary GC-FT-IR spec-trum of an environmental sample and attempted to identify the constitu-ents of the sample using complementary GC-MS data. Of course, the

(b)

Kw^yvw^^w v*w*.

0 4000 3500 3000 2500 2000

Wavenumber 1500 1000 750

Fig. 25. GC-FT-IR spectra of the leading (a) and tailing (b) edges of the peak at 12.49 min in sandalwood oil.

128

1.5

(a)

o p W W * * ^ i ^ΝΑΛ^Τ·*-

0.6000

« 0.3000

(b)

4000 3500 3000 2500 2000 Wavenumber

1500 1000 750

Fig. 26. Pure spectra of ß-sandalol (a) and the corresponding aldehyde (b) obtained from mutual subtraction of the spectra of Fig. 25.

129

130 K. Krishnan

entire field of GC-FT-IR and capillary GC-FT-IR was made possible by the pioneering work on waste water and other environmental pollutants at the Environmental Research Laboratory of the U.S. Environmental Pro-tection Agency in Athens, Georgia, by Azarraga and co-workers (1981). Krishnan et al. (1983) studied the capillary GC-FT-IR spectra of a num-ber of pesticides. A 0.32 mm x 40 m SE-54 WCOT fused silica column was used, and the GC-IR interface in these studies was an all-glass sys-tem. The GC-IR spectra of a number of pesticides of known purity, dis-solved in benzene, were obtained and compared with the corresponding

3000 2500 2000 1500 1000 750 Wavenumber

Fig. 27 . Comparison between the GC-FT-IR (bottom, 8 c m 1 ) and diffuse reflectance (top, 4 c m 1 ) spectra of DDT.


diffuse reflectance spectra to verify that these pesticides did not undergo thermal decomposition during the Chromatographie separation. Figures 27 and 28 show such a comparison for two pesticides: DDT [ 1,1,1-trichloro-2,2-bis(/?-chlorophenyl)ethane] and aldrin (1,2,3,4,10,10-hexachloro-1,4,4,5,8,8-hexahydro-l^-^«i/o-^jco^^-dimethanenaphthalene), respec-tively. One can see the close similarity between the vapor-phase (GC-IR) and the condensed-phase (diffuse reflectance) spectra of these two pesti-cides. DDT and aldrin were more than 99% pure and contained but a single isomer. Other pesticides, however, such as BHC (1,2,3,4,5,6-hexa-chlorocyclohexane), were a mixture of isomers. The FID gas chromato-gram of BHC, which is a mixture of four isomers (α, β, γ, and δ), is shown in Fig. 29. One can see the peaks due to the four isomers marked in the

3000 2500 2000 1500 1000 750 Wavenumber

Fig. 28. GC-FT-IR (bottom, 8 cm1) and diffuse reflectance (top, 4 cm1) spectra of aldrin.

132 K. Krishnan

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CD Λ

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< I -

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figure, as well as a number of minor impurity peaks. Figures 30 and 31 show the GC-FT-IR spectra of these fractions, 1 to 4, which can be identified as being due to the α, β, γ, and δ isomers, respectively. Figures 30 and 31 show that each isomer yields a distinctly different ir spectrum, illustrating the usefulness of GC-FT-IR in the study of isomeric com-pounds. Most GC-IR software systems allow the creation of user-created libraries. By the use of such a library consisting of the pure spectra of a number of pesticides, positive identification could be made from a com-plex mixture containing all the pesticides. This is illustrated in Fig. 32, which shows the FID chromatogram of the pesticide mixture with all the GC fractions positively identified.

A number of arochlors have also been analyzed by means of the capil-lary GC-FT-IR technique (Sadtler Research Laboratories, 1982). Figure 33 shows the FID chromatogram of one of them, arochlor 1232. Three of


3000 2500 2000 1500 1000 750 Wavenumber

Fig. 30 . Capillary GC-FT-IR spectra of (a) a- and (b) /3-BHC. These correspond to peaks 1 and 2 in Fig. 29.

the GC-IR spectra obtained from this mixture, including biphenyl and 4-chlorobiphenyl, are shown in Fig. 34.

Malissa (1984) studied the capillary GC-FT-IR spectra of a number of chlorophenols. Using a spectral library created using standard chlorophe-nols, he showed the presence of two tetrachlorophenols and a penta-

134 K. Krishnan

3000 2500 2000 1500 1000 750

Waven umber

Fig. 31. Capillary GC-FT-IR spectra of (a) y- and (b) δ-BHC. These correspond to peaks 3 and 4 in Fig. 29.

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3200 2800 2400 2000 1600 1200 800 700

Wavenumber

Fig. 34. Capillary GC-FT-IR spectra of three arochlor 1232 fractions. Peak 1 (bi-phenyl): maximum absorbance, 0.017; minimum absorbance, -0.002. Peak 2: maximum absorbance, 0.024; minimum absorbance, -0.003. Peak 3 (4-chlorobiphenyl): maximum absorbance, 0.020; minimum absorbance, 0.0 (Sadtler Research Laboratories, 1982).

137

138 K. Krishnan

chlorophenol in addition to 2,4,5-trichlorophenol in 2,4,5-T (commercial tetrachlorophenols).

D. Other Applications

The capillary GC-FT-IR technique has been applied to the study of crude oil, and the feasibility of applying the technique to the study of the pyrolysis products of polymers has been shown (Smith et al., 1983). The technique is now being used routinely in the petrochemical industry to study petroleum products and liquefied coal extracts, even though not much has been published in this area in view of the proprietary nature of these studies.

IV. USE OF COMBINED GC-FT-IR AND GC-MS DATA

As indicated earlier, the GC-FT-IR technique is capable of clearly distinguishing among isomeric compounds. In contrast, GC-MS data pro-viding information about molecular weights can clearly differentiate ho-mologous compounds such as n-alkanes of differing carbon numbers. At present, the sensitivities of even the best available capillary GC-FT-IR systems are 10 or 20 times lower than those of GC-MS systems. Be-cause the two techniques often offer complementary information, how-ever, the combined data from the two techniques can yield more informa-tion than either technique by itself. It is not surprising, then, that attempts have been underway to combine the two methodologies. Shafer et al. (1981), Crawford et al. (1982), Wilkins et al. (1981, 1982), and Wilkins (1983) coupled GC-IR and GC-MS systems so that it is possible to obtain the two sets of data from a single sample injection. Shafer et al. (1984) showed that more peaks in the capillary gas chromatogram of an indus-trial waste water residue can be positively identified by GC-IR than by GC-MS. Crawford et al. (1982) identified the structure of a siloxane by using the combined data. As mentioned earlier, Wilkins et al. (1982) used the data to analyze the chromatogram of peppermint oil.

Although not actually coupling the GC-FT-IR and the GC-MS sys-tems, Griffiths et al. (1983) and Chiu et al. (1983) reported data obtained using identical columns in separate GC-IR and GC-MS instruments. Grif-fiths et al. (1983) identified three GC fractions in an environment sample using combined data.

Chiu et al. (1984) studied a coal combustion product using the same 0.32 mm i.d. x 15 m DB-5 fused silica column on a Finnigan MAT SS-200 GC-MS system and a Digilab FTS-20 capillary GC-FT-IR system. Figure 35 shows the GC-MS total-ion plot of this sample. The GC-MS spec-trum of this sample had been studied in great detail by Chiu et al. (1983).

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140 K. Krishnan

1.8000

0.5500 1

-0.7000 3000 1500 1000 680 2500 2000

Wavenumber Fig. 36. GC-MS (top) and GC-FT-IR (bottom) spectra of peak 8 of Fig. 35. Copyright

1983 American Chemical Society.

Figure 36 shows the GC-MS and the GC-IR spectra of peak 8 in this chromatogram. The GC-MS spectrum, showing major ions at MIZ 116 and 115, was identified by the GC-MS data system as one of the following compounds: indene, phenylpropadiene, phenylpropyne, or isomers of methylphenylacetylene. The GC-IR spectrum was unequivocally identi-fied as being due to indene. Figure 37 shows the GC-MS and GC-IR spectra of peaks 36 and 44 in the chromatogram. As can be seen, the mass spectra are very similar; the ir spectra, however, are quite distinct. The GC-MS data indicated that these peaks could be due to 9-fluorenone, 1-phenalenone, or benzo[c]cinnoline. The GC-MS spectra show that both compounds lose 28 amu under electron ionization, consistent with the facile removal of a carbonyl moiety. The ir absorbance due to the car-bonyl stretching in the strained five-member ring (9-fluorenone) should be

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142 K. Krishnan

at a higher frequency than the one exhibited by 1-phenalenone. The GC-IR spectra of the two peaks could thus be used to identify positively peak 36 (as 9-fluorenone) and peak 46 (as 1-phenalenone). By using the two sets of data, Chiu et al. (1984) also positively identified a number of other peaks in this chromatogram.

V. ALTERNATIVE APPROACHES TO GC-IR

The GC-FT-IR technique described so far in this chapter can be called the traditional GC-IR method. Attempts have been underway to develop alternative GC-IR methodologies with improved sensitivities. Kuehl and Griffiths (1978) developed a dual-beam FT-IR method and applied the technique to the recording of GC-FT-IR spectra using packed (Gomez-Taylor and Griffiths, 1978) and capillary (Kuehl et al., 1980) columns. The dual-beam method attempts to overcome the limitation imposed on the A/ D conversion process in the FT-IR instrument by the strong center burst in the interferograms. Because the Michelson interferometer produces two interferograms that are separated in the phase by 180°, combining these two out-of-phase interferograms at the detector greatly diminishes the amplitude of the center burst and leads to improved digitization accu-racy. Using this technique, Kuehl et al. (1980) reported the GC-FT-IR spectrum of 5 ng of anisole.

A completely different approach to GC-IR studies has been proposed by Garrison et al. (1981), Reedy et al. (1979), and Bourne et al. (1979). Their technique is based on the principle of matrix-isolation spectros-copy. The fractions coming out of the GC column are codeposited with argon on a highly polished, rotatable metal surface at temperatures below 15 K. The deposition is so arranged that the uniform matrix con-taining a GC fraction is around 0.5 mm in diameter and 0.5 mm thick. During the GC run, the deposition surface is rotated so that different fractions occupy different spots. At the end of the GC run, the ir reflec-tance spectra of the various spots are recorded. Because the ir data are not collected on line, any desired spectral resolution and measurement time can be employed. Because most of the bands in the ir spectra will sharpen and perhaps show splitting, the method is capable of producing GC-IR spectra of high sensitivities. It requires, however, a liquid-helium-cooled or a closed-cycle cryostat, its associated vacuum fixtures, and the matrix deposition facilities. There is the possibility of degrading the GC resolution in a complex mixture containing closely separated eluents, and spectra cannot be recorded on line. Because the cryogenic-temperature spectra will be quite different from the low-resolution vapor-phase spec-tra contained in most spectra libraries, identification of the GC fractions


may be time-consuming. However, the technique may prove to be quite useful for the study of the GC-IR spectra of simple mixtures containing well-separated eluents in the low-nanogram range. In this connection, it is worth noting that Griffiths et al. (1983) predicted the feasibility of modify-ing the traditional capillary GC-FT-IR instrumentation by the use of very small area MCT detectors and light pipes optimized for use with these detectors to produce GC-FT-IR spectra in the subnanogram range.

VI. SUMMARY

The capillary GC-FT-IR technique is finding a wide range of applica-tions and can provide information that is sometimes unique, but more often complementary to that provided by the GC-MS methodology. Ad-vances in traditional and nontraditional GC-IR instrumentation may in-crease the sensitivity of the method as well as its areas of application. Attempts are underway to speed up improvements in the software capa-bilities of the technique, such as producing FGCs directly from interfero-grams (Hohne et al., 1981; Wiebolt et al., 1980) and "spectral search" performed directly on interferograms themselves (Azarraga et al., 1981).

REFERENCES

Azarraga, L. V. (1980). Appl. Spectrosc. 34, 224. Azarraga, L. V., and Hanna, D. A. (1979). "GIFTS, Athens ERL GC/FT-1R Software and

User's Guide." U.S.E.P.A./E.R.L., Athens, Georgia. Azarraga, L. V., Williams, R. R., and deHaseth, J. A. (1981). In "Proceedings of the

International Conference on FT-IR" (H. Sakai, ed.), p. 232. SPIE, Washington, D.C. Beadle, B. C , Krishnan, K., and Hill, S. L. (1983). Pap., Int. Conf. FT-IR, 1983. Borman, S. A. (1982). Anal. Chem. 54, 901A. Bourne, S., Reedy, G. T., and Cunningham, P. T. (1979) J. Chromatogr. Sei. 17, 460. Chiu, K. S., Walsh, P. M., Beer, J. M., and Biemann, K. (1983). In "Proceedings of the

Seventh International Symposium of Polynuclear Aromatic Hydrocarbons," p. 319. Bat-telle Press, Columbus, Ohio.

Chiu, K. S., Biemann, K., Krishnan, K., and Hill, S. L. (1984). Pap., 31st Annu. Conf. Mass Spectrom. Allied Top.

Coffey, P., Mattson, D. R., and Wright, J. C. (1978). Am. Lab. (Fairfield, Conn.) 10(5), 126. Crawford, R. W., Hirschfeld, T., Sanborn, R. H., and Wong, C. M. (1982). Anal. Chem. 54,

817. deHaseth, J. A., and Isenhour, T. L. (1977). Anal. Chem. 49, 1977. Erickson, M. D. (1979). Appl. Spectrosc. Rev. 15, 26. Garrison, A. A., Hembree, D. M., Jr., Yokley, R. A., Crocombe, R. A., Mamantov, G., and

Wehry, E. L. (1981). In "Proceedings of the International Conference on FT-IR Spec-troscopy" (H. Sakai, ed.), p. 150. SPIE, Washington, D.C.

Gomez-Taylor, M. M., and Griffiths, P. R. (1978). Anal. Chem. 50, 422.

144 K. Krishnan

Griffiths, P. R. (1975). "Chemical Infrared Fourier Transform Spectroscopy." Wiley, New York.

Griffiths, P. R. (1977). Appl. Spectrosc. 31, 284. Griffiths, P. R. (1978). In "Fourier Transform Infrared Spectroscopy" (J. R. Ferraro and

J. L. Basile, eds.), Vol. 1, p. 143. Academic Press, New York. Griffiths, P. R. (1983). In "Advances in Infrared and Raman Spectroscopy" (R. J. H. Clark

and R. E. Hester, eds.), p. 277. Wiley, New York. Griffiths, P. R., deHaseth, J. A., and Azarraga, L. V. (1983). Anal. Chem. 55, 1361A. Gurka, D. F. , Laska, P. R., and Titus, R. (1982). J. Chromatogr. Sei. 20, 145. Hanna, D. A., Marshall, J. C , and Isenhour, T. L. (1979a). J. Chromatogr. Sei. 17, 434. Hanna, D. A., Hanagac, G., Hohne, B. A., Small, G. W., Wiebolt, R. C , and Isenhour,

T. L. (1979b). J. Chromatogr. Sei. 17, 423. Hohne, B. A., Hanagac, G., Small, G. W., and Isenhour, T. L. (1981). J. Chromatogr. Sei.

19, 283. Jacobs, M., Ledig, W., and Waldradt, J. (1980). Digilab Users Conf., 1980. Kalisinsky, K. S. (1981). In "Proceedings of the International Conference on FT-IR Spec-

troscopy" (H. Sakai, ed.), p. 156. SPIE, Washington, D.C. Krishnan, K., and Ferraro, J. R. (1982). In "Fourier Transform Infrared Spectroscopy"

(J. R. Ferraro and L. J. Basile, eds.), Vol. 3, p. 188. Academic Press, New York. Krishnan, K., Curbelo, R., Chiha, P., and Noon, R. C. (1979). J. Chromatogr. Sei. 17, 413. Krishnan, K., Brow, R. H., Hill, S. L., Simonoff, S. C , Olson, M. L., and Kuehl, D. (1981).

Am. Lab. Mar. Krishnan, K., Hill, S. L., and Crocombe, R. A. (1983). Pap., Pittsburgh Conf. Anal. Chem.

Appl. Spectrosc., 1983 p. 610. Kuehl, D. (1981). In "Proceedings of the International Conference on FT-IR Spectroscopy"

(H. Sakai, ed.), p. 140. SPIE, Washington, D.C. Kuehl, D., and Griffiths, P. R. (1978). Anal. Chem. 50, 418. Kuehl, D., and Olson, M. L. (1981). Pap., Pittsburgh Conf. Anal. Chem. Appl. Spectrosc,

1981 p. 247. Kuehl, D., Kemeny, G. J., and Griffiths, P. R. (1980). Appl. Spectrosc. 34, 222. McClure, D. L. (1983). Am. Lab. (Fairfield, Conn.) 15(11), 43. Malissa, H., Jr. (1982a). Fresenius' Z. Anal. Chem. 311, 123. Malissa, H., Jr. (1982b). Fresenius' Z. Anal. Chem. 313, 116. Malissa, H., Jr. (1983). Fresenius' Z. Anal. Chem. 316, 699. Malissa, H., Jr. (1984). Fresenius' Z. Anal. Chem. (to be published). Namba (1982). Pap., 18th Annu. Meet., Appl. Spectrosc. Soc. Jpn. Namba (1983). Pap., 19th Annu. Meet., Appl. Spectrosc. Soc. Jpn. Reedy, G. T., Bourne, S., and Cunningham, P. T. (1979). Anal. Chem. 51, 1535. Sadtler Research Laboratories (1982). "The Infrared Spectra Handbook of Priority Pollut-

ants and Toxic Chemicals." Sadtler Res. Lab., Philadelphia, Pennsylvania. Shafer, K. H., Hayes, T. L., and Tabor, J. E. (1981). In "Proceedings of the International

Conference on FT-IR Spectroscopy" (H. Sakai, ed.), p. 160. SPIE, Washington, D.C. Shafer, K. H., Hayes, T. L., Brasch, J. W., and Jakobsen, R. J. (1984). Anal. Chem. 56,

237. Simonoff, S. C , Olson, M. L., Kuehl, D., and Turner, R. B. (1981). In "Proceedings of the

International Conference of FT-IR Spectroscopy" (H. Sakai, ed.), p. 135. SPIE, Wash-ington, D.C.

Smith, S. L., Garlock, S. E., and Adams, G. E. (1983). Appl. Spectrosc. 37, 192. Sun, J. N., Mohar, J. W., and Reed, D. C. (1983). Pap., Pittsburgh Conf. Anal. Chem. Appl.

Spectrosc, 1983 p. 611.


White, R. L., Griss, G., Brissey, G. M., and Wilkins, C. L. (1981). Anal. Chem. 53, 1778. Wiebolt, R. C., Hohne, B. A., and Isenhour, T. L. (1980). Appl. Spectrosc. 34, 7. Wilkins, C. L. (1983). Science 222, 291. Wilkins, C. L., Griss, G. N., Brissey, G. M., and Steiner, S. (1981). Anal. Chem. 5?, 113. Wilkins, C. L., Griss, G. N., Brissey, G. M., and Onyiriuka, E. C. (1982). Anal. Chem. 54,

2260.

APPLICATIONS OF SPECTRAL TECHNIQUES TO THERMAL ANALYSIS

A. G. Nerheim

Analytical Services Division Standard Oil Company (Indiana) Naperville, Illinois

I. Introduction 147 II. Application of Fourier Transform Infrared

Evolved-Gas Analysis 149 A. Adaptation of a Nicolet 7199 GC-FT-IR

Spectrometer to FT-IR-EGA 149 B. FT-IR-EGA and Thermal Gravimetric

Analysis of Amidization Reactions 150 C. Compressed Spectral Plot Patterns for

FT-IR-EGA 152 D. FT-IR-EGA and Thermal Gravimetric

Analysis of Fiberglass Fillers 156 III. Application of Infrared Spectral Data of

Residuals during Thermal Analysis 164 A. Adaptation of Equipment and Procedures 164 B. Infrared Analysis of Thermally Induced

Imidization 165 IV. Conclusion 167

References 167

I. INTRODUCTION

Thermal gravimetric analysis (TGA), a well-established analytical tech-nique, has been coupled with spectral analysis of thermally released com-ponents that cause a weight change in the original material. As the tem-perature of the sample is programmed up, the released components are swept through a heated transfer line and gas cell. While in the heated cell, the components are scanned continuously by a Fourier transform infrared (FT-IR) spectrometer. The collection of special data is essentially the same as that in the GC-FT-IR system, the key difference being that the components are not separated by a gas chromatography (GC) column. The coupled techniques provide a means of identifying unknown compo-nents released at the characteristic temperatures shown by TGA.


ISBN 0-12-254104-9

148 A. G. Nerheim

Roush and Huppler (1982) directly coupled the TGA apparatus with the FT-IR system. Other investigators have studied the thermal decomposi-tion of samples by a technique designated Fourier transform infrared evolved-gas analysis (FT-IR-EGA) without direct coupling to TGA. Liebman et al. (1976) formed volatile products with a model 120 Pyro-probe (Chemical Data Systems, Inc., Oxford, Pennsylvania) at a pro-grammed temperature rate of 5, 10, 20, or 40°C/min. The products were swept from the Pyroprobe into a heated light-pipe gas cell and scanned with a Digilab FTS-14 FT-IR spectrophotometer. Products were formed in either pyrolytic (nitrogen or helium) or combustive (air) atmospheres.

Lephardt and Fenner (1980) developed a more elegant approach in characterizing the pyrolysis and combustion of tobacco. A computer-controlled pyrolysis furnace gave the desired control for isothermal run-ning, temperature sampling, and a final hold time before shutdown. The evolved gases entered the heated transfer line and gas cell of a Digilab GC-IR accessory maintained at 180°C. The computer capability of the system was expanded by a two-directional transfer of data files between the Digilab FTS-14 used for this experiment and a Xerox Sigma-9 computer. The expanded system provided both spectral and temperature data. A variety of options was available for data processing and presentation.

The most useful option appears to be a plot of absorbance against sample temperature for selected components. The plots for the evolution of C02, H20, and other components, defined as evolution profiles, char-acterize the pyrolysis and combustion of complex samples with the FT-IR-EGA technique. Samples of tobaccos have been characterized by three different applications of the FT-IR-EGA system. In the first appli-cation, involving direct comparison by pyrolysis in an N2 stream, charac-teristic differences between bright and burley tobacco were shown by evolution profiles for C02, H20, NH3, and acetic acid. In another applica-tion the bright tobacco was extracted with H20, and the evolution curves for H20 clearly showed differences caused by the extraction. The final application involved combustion studies, which showed that evolution profiles depend on the oxygen content of the gas as well as the temper-ature.

Fenner and Lephart (1981) also applied evolution profiles to the exami-nation of the pyrolysis of Kraft pine lignin by the FT-IR-EGA tech-nique. Evolution profiles of formic acid, formaldehyde, S02, H20, C02, methanol, and phenols demonstrated the effectiveness of the technique in simultaneous monitoring of multiple vapor-phase species. Sulfoxide and/ or sulfone, which may be incorporated in the lignin structure during the Kraft pulping process, was deemed to be the probable origin of S02. The FT-IR-EGA technique is an effective approach to examining the evolu-tion of specific chemical products and/or structures.

4 Applications of Spectral Techniques to Thermal Analysis 149

Our method of achieving FT-IR-EGA capabilities is simplified by a direct adaptation of the GC-FT-IR system in which the use of the GC oven is retained. A stainless steel tube replacing the GC column holds the sample while the excellent thermal control of the GC oven achieves the desired temperature programming of the sample. Options for the presen-tation of spectral data range from the simple to the more sophisticated and guide the selection of the files of special interest for plotting and further study as well as presenting spectral changes as a function of temperature. For a simple analysis the thermal conductivity response may suffice for the selection of spectral files. For a complex analysis, special compressed plots enable one to look at hundreds of spectra at the same time, tell at a glance if any are different and if so which are. This is an effective guide to selecting spectra for plotting and for further study of evolved compo-nents.

Another function of thermal analysis is to provide structural informa-tion on the changes in components remaining in the heated zone. A modi-fied heated horizontal stage holding the sample is aligned in the sample beam. As the temperature is programmed up, scans of the changing struc-ture are obtained continuously. For the most detailed spectral information on thermally induced structural changes, the two techniques may be com-bined to give direct data on the components leaving and those remaining in the heated zone.

II. APPLICATION OF FOURIER TRANSFORM INFRARED EVOLVED-GAS ANALYSIS

A. Adaptation of a Nicolet 7199 GC-FT-IR Spectrometer to FT-IR-EGA

The adaptation of a Nicolet 7199 GC-FT-IR spectrometer to FT-IR-EGA consists of simply replacing the GC column with a 2-in. length of \-in. stainless steel tubing to hold the sample. The adaptation leaves one end of the sample tubing connected to the GC thermal conductivity cell and the other end connected to a length of iVin. stainless steel tubing bringing in the carrier gas. The sample tube is mounted vertically, with the helium carrier gas entering the bottom end. The entering helium car-rier gas flows up through the sample into the thermal conductivity cell and finally exits through the light pipe. Thus, the adaptation provides a config-uration to monitor the volatilized sample components by thermal conduc-tivity response and FT-IR spectra.

The adaptation requires no modification of the GC oven and tempera-ture controls. The temperature control provided for GC is excellent and is utilized to achieve FT-IR-EGA. Conditions are set to give the desired

150 A. G. Nerheim

starting temperature, temperature programming rate, maximum tempera-ture, time at maximum temperature, and shutdown time. Temperature conditions can be set closely enough to approximate TGA conditions to allow correlation and responses of the respective systems.

The experiment consists of a sequence of steps starting with the loading of a weighed sample and concluding with the removal and weighing of the residual sample. Loading of the sample consists of removing the sample tube, adding a weighed amount of sample, plugging each end of the sam-ple tube with glass wool, replacing the sample tube, and tightening the swagelock fittings to make the system leak free. The sample weight de-pends on the concentration of the components of interest, but 100 mg is commonly used. A carrier gas flow rate adjusted to 10 ml/min gives ade-quate response. After the enclosed sample is flushed with helium carrier gas for —15 min, the taking of data is started with the following switched on: GC temperature programmer, thermal conductivity recorder chart drive, and continuous FT-IR scanning with data storage. After shutdown of the temperature programmer and oven cooldown, data taking is termi-nated; residual sample is removed and reweighed to compare weight loss as determined by TGA.

Application of the FT-IR-EGA technique provides structural informa-tion on sample weight changes shown by TGA analysis. As an illustration, the application of the technique to three increasingly complex problems is described. The first problem yielded structural information on amidiza-tion in a direct approach guided by a correlation between TGA and ther-mal conductivity response. The second and third applications required the development of a more sensitive guide to structural changes during the TGA analysis of sizing agents on fiberglass.

B. FT-IR-EGA and Thermal Gravimatic Analysis of Amidization Reactions

Thermal gravimetric analysis studies on the amidization of aromatic acids gave the weight-loss curve and first-derivative curve shown in Fig. la. In order to identify the components, the FT-IR-EGA technique was applied and gave the thermal conductivity response shown in Fig. lb. Experimental conditions for the FT-IR-EGA technique were set to ap-proximate TGA conditions closely enough to allow correlation of the responses of the respective system. The temperature programming rate and starting temperature of the GC oven were set to those of the TGA analysis: 5°C/min and a starting temperature of 30°C. The weight of the sample, 130 mg, was —10 times greater than that of the sample weight used for TGA. The experiment was terminated after the GC oven reached 300°C and had been held there for 5 min. After the oven had cooled down,

4 Applications of Spectral Techniques to Thermal Analysis

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the residual sample was removed and re weighed to show a 12% weight loss. The loss agrees with 11.8% shown by the TGA weight-loss curve. The first derivative of the weight-loss curve represents changes in weight with maxima at different characteristic temperatures.

Maxima at different characteristic temperatures are also shown by the thermal conductivity response of the FT-IR-EGA system in Fig. lb. The close similarity of the shapes of the respective maxima in Fig. la,b, to-gether with the correspondence of their characteristic temperatures, in-dicates a good correlation between TGA and thermal conductivity. Ac-cording to this correlation the maxima shown by the thermal conductivity response is a viable monitor of components originating from the sample, as indicated by the corresponding maxima shown by the first derivative of the weight-loss curve.

With the aid of the thermal conductivity maxima, three spectra were selected to identify components corresponding to the maxima of the first

152 A. G. Nerheim

derivative of the TGA weight-loss curve. The first maximum represents H 2 0 ; the second and third maxima represent mixtures of H 2 0 and l-methyl-2-pyrrolidone, the solvent for the amidization reaction. The two mixtures contain the same amount of H 2 0 , but that represented by the second maximum contains more solvent than that represented by the third. The thermal conductivity response aids in the efficient selection of pertinent spectra by which to identify the components released at charac-teristic temperatures during TGA.

C. Compressed Spectral Plot Patterns for FT-IR-EGA

If all problems were as clear-cut and simple as that discussed in the preceding section, thermal conductivity response alone would be an ade-quate guide to selecting pertinent spectra. For more complex problems, in which several different components are represented by a single broad thermal conductivity maximum, another means of selecting the pertinent spectra is needed, an approach that will indicate which spectra will aid in the identification of the components as a function of evolution tempera-ture. The objective of the approach is to be able to view hundreds of spectra at the same time and tell at a glance whether they are different and which ones are different. The concept is that of a chemigram, a plot indicating changes in functional groups with temperature, but with a greater sensitivity to subtle changes in functional groups represented by hundreds of spectra.

To provide room for hundreds of spectra in a single presentation, the spectral region characteristic of specific functional groups is compressed (Fig. 2). A conventional plot of the region 1550-1300 cm- 1 shows the characteristic methyl and methylene bands of the evolved gas in the py-rolysis of polypropylene. The compressed plot is obtained simply by set-ting the plot routine to 0.0002 in./cm, which causes the methylene band to overlap that of methyl. The bands seem hidden in the compressed plot. Clearly, the compressed plot cannot indicate the characteristic methyl and methylene band positions provided by the conventional plot. An alternative way of looking at or characterizing bands that does not require precise wavenumbers is needed. Another means can be illustrated with stacked compressed plots of the methyl and methylene regions against temperature.

Figure 3a shows 200 compressed spectra plotted against temperature in the pyrolysis of polypropylene. Starting at 30°C, heating continued at a rate of 5°C/min and was terminated after the GC oven reached 400°C and had been held there for 15 min. The spectral region characteristic of the methyl group shown by the single conventional plot is compressed and plotted against the temperature at which the spectrum was obtained and

4 Applications of Spectral Techniques to Thermal Analysis

Conventional plot Compressed plot

153

1550 1450 1350 Wavenumber


Fig. 2. Spectrum of the characteristic methyl/methylene region presented in the form of a conventional and a compressed plot.

stored in a given destination file number. The GC oven temperature asso-ciated with a given destination file is based on calibration. Calibration shows that the plot of temperature displayed by the GC oven against the destination file number is linear. The linear relationship between tempera-ture and destination file number over the entire range of the thermal treatment indicates that a characteristic temperature may be assigned to any given spectrum. The presentation is marked off at 100°C intervals with a blank spectrum. Replacing the spectra at the 100°C intervals with a blank spectrum obtained at 30°C, where no components are evolved, gives the desired presentation.

The spectral region characteristic of the methylene group is com-pressed and plotted against temperature (Fig. 3b). The conventional plot is compressed and appears as a straight line in the plot against tempera-ture. The plot matches that of Fig. 3a in appearance. Figure 3c shows the spectral region extended to include both the methyl and methylene groups. Although the methyl/methylene plot against temperature in Fig. 3c is similar to those of Fig. 3a,b, a significant difference is shown. That difference is a variegated appearance characteristic of the relative methyl/ methylene intensities. This is a way of looking at the compressed stack plotted to gain information on hundreds of spectra at a single glance.

By the characteristic variegated appearance substituting for the charac-terization by band positions in conventional plots, compressed spectral plot patterns can provide the suggestions of change for hundreds of spec-tra in a single presentation. The approach is more efficient than viewing each of the hundreds of spectra individually and then deciding that the methyl/methylene character or structure remained essentially unchanged during the run from 100 to 400°C. Figure 3 suggests that one can look at

154 A. G. Nerheim

A - i 1 1 -i 1 1

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1250 30 100 200 300 400 400 Heat off Temperature, °C

Fig. 3. Characteristic spectral region of the pyrolysis products of isotactic polypro-pylene compressed and then plotted against temperature, (a) Characteristic methyl region; (b) characteristic methylene region; (c) combined methyl/methylene region.


i 1 1 1 — r 30 100 200 300 400 400 Heat off and cooling

Temperature, °C

Fig. 4. Evolution of H20 shown by plotting the compressed characteristic band at 3854 cmr1 against temperature, (a) Without correction for the shifting baseline; (b) with correc-tion for the shifting baseline.

hundeds of spectra at the same time and tell at a glance whether they are different and which ones they are.

An illustration less ideal than that of the methyl/methylene characteri-zation is shown for the evolution of H20 from a sizing agent extracted from a fiberglass filler in Fig. 4a. The isolated H20 vapor band at 3853.8 cm-1 is shown in conventional and compressed plots against the tempera-ture at which it evolved. The conventional plots show the smaller band to be higher on the absorbance scale than the larger one because of a shift in the baseline. The compessed plot against temperature shows considerable distortion caused by the shifting baseline. The shifts give little information and are confusing. For each band the baseline, generated as a straight line, is subtracted with a simple program. The resulting correction for the shifting baseline gives the simplified plot shown in Fig. 4b. This plot more clearly represents the evolution of H20 with temperature maximizing in the 400°C isothermal region. Correcting for shifting baselines brings uni-

156 A. G. Nerheim

formity to the presentations, whether for a single band, as in the case of H20, or multiple bands, as in the case of the methyl/methylene characteri-zation. The uniform presentation enhances the sensitivity to composi-tional changes of evolved gases needed for complex problems involving thermal stability.

D. FT-IR-EGA and Thermal Gravimetric Analysis

of Fiberglass Fillers

Figure 5 shows the TGA weight-loss curve in a study of the thermal stability of the sizing agent on a fiberglass filler. The thermal stability of the sizing agent is of interest because it enhances the properties of the fiberglass filler. A weight loss of 1% shown by the weight-loss curve is due to the thermal decomposition of all of the sizing agent. Structural informa-tion on the thermally evolved gases causing the 1% weight loss is based on the FT-IR-EGA technique.

Because the sizing agent comprised only 1% of the fiberglass filler sample, the sample holder was enlarged by a factor of 10 to accommodate the larger sample, 1.5 g, needed to obtain adequate spectra. The evolution of products from the sample under programmed temperature conditions, monitored with a modified GC-FT-IR system, gave the needed spectral data. Data are displayed as 227 compressed stacked spectra characteristic of a given group plotted against temperature.

Figure 6 shows 227 compressed spectra characteristic of H20 plotted against temperature. The evolution of H20 starts at room temperature, levels off below 100°C, reaches a small maximum at 310°C, and begins an increase at 340°C that is terminated as the heat is turned off at 400°C.

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Figure 7 shows the evolution of components with compressed spectra plotted against temperature. Representing aliphatic CH, aromatic CH, alcoholic OH, and ester carbonyl, the spectral plot patterns show maxima at various temperatures. The coincidence of the peak maxima of the spectral plot patterns suggests that a structure containing all the repre-sented groups may evolve at that temperature. At 190°C the coincidence of maxima in three of the four plots indicates that the evolving products are esters with aliphatic and aromatic but no alcoholic groups. This is consistent with the selected spectra representing the 190°C region indicat-ing phthalates derived from aromatic acids and aliphatic alcohols.

At 340°C the coincidence of peak maxima in three of the four plots indicates that evolving products are esters with aliphatic and alcoholic but no aromatic groups. At 390°C the coincidence of peak maxima in all four plots indicates that the evolving products are esters with aliphatic, alco-holic, and aromatic groups.

Only the peak at 390°C, representing the aromatic group, is free of interferences. The sharp peak shape like that of the aromatic peak is only hinted at for the other three groups by sharp peaks superimposed on the interference from the peaks at 340°C. If it is assumed that all four groups make up the structure, the peak shapes for the other three groups are the same as that for the aromatic group. Therefore, interferences at 340°C from all the peaks at 390°C are assumed to be negligible, because the interference at 340°C from the aromatic peak at 390°C is negligible. In addition to indicating marked differences in interferences, the plots strongly indicate that the peaks at 340°C are broader and larger than those at 390°C.

30 100 200 300 400 400 Heat off and cooling Temperature, °C

Fig. 6. Evolution of H20 from fiberglass filler and sizing agent indicated by the com-pressed characteristic band at 3854 cm~' plotted against temperature.

30 100 200 300 400 400 Heat off and cooling Temperature, °C

Fig. 7. Evolved gases showing changes in different spectral regions as a function of temperature, (a) Aliphatic CH; (b) aromatic CH; (c) alcoholic OH; (d) ester C = 0 .


The larger peaks at 340°C indicate polyester oligomers originating from aliphatic acids. The smaller peaks at 390°C indicate polyester oligomers originating from aromatic rather than aliphatic acids. Evolution of more aliphatic than aromatic polyester oligomers is suggested by the differ-ences in the size of the respective peaks. The aliphatic polyester oligo-mers evolve at lower temperatures and over a broader temperature range than do the aromatic polyester oligomers according to differences in the shapes of the respective bands. At the high temperatures of 340 and 390°C, the evolution of polyester oligomers is more likely than the evolu-tion of a mixture of alcohols and esters.

Figure 8a shows the evolution of C02, a minor component, with char-acteristic compressed spectra plotted against temperature. The maxima at 340 and 390°C coincide with the respective maxima for aliphatic and aromatic polyester oligomers. Apparently, C02 is a decomposition prod-uct connected with the thermal evolution of polyester oligomers. The variegated appearance of the plot suggests a partially hidden maximum in the 320°C region. A difference in the spectra of the gas evolved in the region suggested by the partially hidden maximum indicates a second component. Expansion of the compressed spectrum at the partially hid-den maximum at 310°C and that of pure C02 are shown in Fig. 8b. Com-parison shows a band at 2274 cm-1, not found in C02, present in the spectrum. The band at 2274 cm-1 is characteristic of isocyanate. Strong interference from C02 occurs. With the elimination of C02 interference by spectral subtraction, the corrected absorbance at 2274 cm-1 is plotted against temperature in Fig. 8c. The maximum at 320°C corresponds to the partially hidden maximum that is part of the variegated appearance in Fig. 8a. The corrected spectrum tends to confirm that the variegated appear-ance suggests real spectral changes caused by a second component. In addition, the corrected absorbance gives a helpful profile of the thermal stability of the isocyanate component of the sizing agent.

Analysis of the sizing agent extracted from the fiberglass showed no evidence for isocyanate but clearly showed the major components: aro-matic and aliphatic polyesters. The evolution of isocyanate seemed to be the only apparent inconsistency or difference induced by the FT-IR-EGA technique. To test the apparent inconsistency, the FT-IR-EGA technique was also applied to the extract.

Why heat caused isocyanate to evolve from the sizing agent on the fiberglass but not from the sizing agent extracted from the fiberglass was a question that required further study. The study centered on other nitro-gen-containing groups. Characteristic absorbances are at 3588 and 3658 c m 1 for NH groups and at 1652 cm-1 for secondary amide carbonyl groups.

160 A. G. Nerheim

Fig. 8. A second component, an isocyanate, indicated in the plot of the characteristic C0 2 spectral region against temperature, (a) Spectral change indicating a second compo-nent; (b) expansion of the indicated spectrum showing the characteristic isocyanate band at 2274 c m 1 ; (c) plot, after spectral subtraction of C0 2 interference, indicating the evolution of isocyanate with temperature.

Figure 9 shows the characterization of nitrogen-containing groups in the sizing agent extracted from the fiberglass filler with chloroform. Fig-ure 9a shows the conventional spectrum from 3607 to 3547 cm-1 with characteristic NH bands at 3588 and 3568 cm-1. Figure 9b shows the conventional spectrum from 3607 to 3550 cm-1 with a characteristic NH band at 3588 c m 1 as well as its compressed spectrum plotted against temperature. A variegated appearance suggests that the plot is sensitive

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162 A. G. Nerheim

to the second band at the side of the band at 3588 cm-1. Figure 9c shows the conventional spectrum from 3565 to 3547 cm-1 with a characteristic NH band at 3568 cm"1 as well as its compressed spectrum plotted against temperature. Comparison of Fig. 9b,c shows differences in the shape and maxima of the compressed plots. The differences strongly suggest that the molecules with the characteristic NH band at 3588 cm-1 are different from those with the characteristic NH band at 3568 cm 1 . A possible connec-tion between the NH bands and the characteristic secondary amide car-bonyl band at 1652 cm-1 can be developed by comparing the respective compressed plots against temperature. Figure 9d shows the compressed band at 1652 cm-1 plotted against temperature. Comparison of the shape and maxima of the respective compressed plots indicates a closer rela-tionship between the amide band at 1652 cm-1 and the NH band at 3568 than at 3688 cm-1. The NH band at 3688 cm-1 is probably that of carba-mate rather than an amide.

Comparison revealed differences between the nitrogen-containing com-ponents evolved from the extract and from the sizing agent on the fi-berglass filler. Much more carbamate evolves from the extract than di-rectly from the sizing on the fiberglass filler. This is balanced by isocyanate evolving from the sizing on the fiberglass filler but not from the extract. The differences encourage speculations about the reaction of isocyanate to give carbamate.

One speculation is that an isocyanate, probably a diisocyanate, reacts with the SiOH group to bind the sizing agent to the surface of the fi-berglass. Presumably, the resulting surface carbamate is not thermally stable and reverts to the isocyanate on heating. The extracted carbamate, however, is more thermally stable and retains the carbamate structure and does not revert to an isocyanate. Although the speculations are rather uncertain, the comparison of the results obtained on the extract with those obtained directly on the sizing agent attached to the surface of the fiberglass is significant. The most significant difference is that isocyanate does not evolve on heating of the extract but does evolve on heating of the sizing agent attached to the fiberglass filler.

In a thermal stability study of another sizing agent on fiberglass filler, the plot pattern for C02 showed no evidence for isocyanates, in marked contrast to that shown in Fig. 8 for the previous sample. The additional necessary structural information was provided by the CH stretching region.

The compressed spectra of the CH stretching region for 3200 to 2600 cm-1 plotted against temperature is shown in Fig. 10a. Two maxima are shown with distinctive plot patterns. The two patterns are different, sug-


100 200 300 400 Temperature, °C

Fig. 10. Characteristic CH region plotted against temperature and the spectra making up the plot, (a) The CH region (3200-2400 cm1) compressed and plotted against temperature; (b) bands at 2976, 2939, and 2887 cm"1 making up the pattern shown by the first maximum in Fig. 10a and bands at 2980, 2939, and 2883 crrr1 making up the second maximum.

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(b)

r * I



gesting that the spectra making up the patterns are different. Figure 10b shows that the CH spectrum characterized by bands at 2976, 2939, and 2887 cm-1 makes up all the pattern shown by the first maximum in Fig. 10a and that the CH spectrum characterized by bands at 2980, 2939, and 2883 cm-1 makes up all the second maximum. The regular plot pattern indicates that all the spectra differ only in intensity. Apparently, the spec-tral consistency and differences can be picked up from the plot patterns. The first plot pattern is likely that of a solvent for the sizing agent. The second plot is probably that of a polyether such as polyethylene oxide that makes up the bulk of the sizing agent.

164 A. G. Nerheim

III. APPLICATION OF INFRARED SPECTRAL DATA OF RESIDUALS DURING THERMAL ANALYSIS

A. Adaptation of Equipment and Procedures

Another function of thermal analysis is to provide information on struc-tural changes in components remaining in the heated zone. A computer-ized Perkin-Elmer 180 dispersive ir spectrophotometer interfaced with a heated sample cell with temperature programming gave the necessary data automatically. Both the spectral data and the temperature at the end of scan were stored in the computer for later processing to show the spectral changes caused by temperature changes. Rather than the conven-tional vertical position, the sample was mounted horizontally to eliminate the flowing of the sample during thermal treatment.

The heated sample cell was mounted horizontally in an attachment made by the Harrick Instrument Company (Fig. 11). The ir beam passed through the sample by means of a special arrangement of mirrors. Sam-pling is simpler with horizontal mounting. Either liquids or solids can be laid on a horizontal KBr window without the special cells and clamps needed for vertical mounting. The horizontal configuration provides un-impeded evolution of gases from the sample.

To increase the temperature range, the original black heater block that came with the horizontal stage was replaced by an aluminum one made with a highly polished reflective surface. The number of heaters was in-creased from four to eight. With more heaters more heat was generated, and with a reflective surface less heat was lost to radiation. To sense the temperature of the sample on the surface of the KBr window, a thermo-couple coming in from the side of the block made contact with the KBr window. The output of the thermocouple was interfaced to the computer

Sample film on surface KBr window \ /\

Heated cell

Pencil heater-^O JO

Thermocouple for >fQ heater control '

Thermocouple for, sample temperature

ir beam

Fig. 11. Horizontally mounted heated sample attachment.


through a temperature indicator showing a digital display of the tempera-ture of the KBr window. To regulate the temperature of the heater block, a thermocouple mounted 2 mm from two adjacent heaters was connected to a temperature programmer made by the Valley Forge Instrument Com-pany. The heater leads were also connected to the temperature program-mer. Sensing temperature through the thermocouple, the programmer regulates by adjusting the voltage feed to the heaters. The programmer operates isothermally or may be set to increase the temperature at a given rate.

B. Infrared Analysis of Thermally Induced Imidization

A thermally induced imidization reaction was studied while the temper-ature increased until shutdown at 300°C; 20 repeat scans of the region 4000-600 cm-1 were obtained. The primary changes in spectra with tem-perature indicate changes in concentration of specific functional groups. Computer plots of absorbance against temperature for each of the se-lected group frequencies suggest changes in concentration. The computer plots of each group are expanded with the maximum absorbance set at 1 and the minimum absorbance at 0. Therefore, the plots represent mole fraction changes with temperature.

Figure 12a represents the effect of increased temperature on a decrease in mole fraction of amine carboxylate absorbing at 1625 cm-1. The de-crease starts at ~57°C. The decrease continues to 0.5 mol fraction at 93°C and levels off at 240°C. Accompanying the decrease in carboxylate is the increase in imide represented by the absorbance at 1772 cm-1 shown in Fig. 12b. The increase starts before 57°C and increases to 0.5 mol fraction at ~88°C. The increase levels off near 130°C. The increase complements the decrease in carboxylate and suggests the formation of imide groups from amine salts.

Secondary differences suggest a more complex change than simply the formation of imide groups. The increase to 0.5 mol fraction imide is ac-complished ~5°C before the 0.5 mol fraction decrease in carboxylate. The molar difference between carboxylate depletion and imide formation with increasing temperatures suggests that products other than imides are be-ing formed. A review of the recordings of the 20 scans obtained during thermal treatment indicates band formation at 1650 cm-1, suggesting that amide products form after imides to account for part of the carboxylate depletion. Other secondary effects, such as small changes in plots after initial leveling, include thermal effects on the absorbances in addition to actual changes in molar concentration of the components. Despite these effects, useful information is derived from the plots.

166 A. G. Nerheim

A-Amin

Amax — Arr

A-AtT

- Amin

100 200 300 Temperature, °C

Fig. 12. Changes in characteristic absorbances with temperature indicating imidiza-tion. (a) Effect of temperature on the relative absorbance of the characteristic amine carbox-ylate band at 1625 cm-1; (b) effect of temperature on the relative absorbance of the charac-teristic imide band at 1772 cm-1.

For the most detailed spectral information on thermally induced struc-ture changes such as imidization, the two techniques may be combined to give direct data on the components leaving and the components remaining in the heated zone. Spectral analysis of the thermally released compo-nents would only show H20 but would not distinguish between the chemi-cal H20 produced by the imidization starting before 57°C and adsorbed H20. With calibration, molar relationships may be established between the leaving and the remaining components.

FT-IR analysis of the remaining components further enhances the com-bining of the two techniques. With calibration, a closer relationship can be established between temperature and structural changes. It is for the more complex changes that the combination of the two techniques holds


most promise. More sophisticated data handling procedures can be devel-oped to enhance the well-established conventional thermal techniques.

IV. CONCLUSION

The application of spectral techniques enhances thermal analysis. FT-IR-EGA gives structural data on the evolved components causing the weight loss shown by TGA. Interpretation of FT-IR-EGA is aided by compressed spectra that enable one to view hundreds of spectra at the same time and tell at a glance if any are different and which ones they are. The technique is limited to components leaving the heated zone.

Structural data on components remaining in the heated zone can be helpful and are provided by a complementary technique. Spectral changes of these components may provide more significant structural data than FT-IR-EGA. For a given sample one technique may be more effective than the other. Because of the complementary nature of the techniques, samples can be analyzed by both to obtain the most detailed information.

It is for more complex changes that the combining of the two tech-niques holds the most promise. This involves the application of FT-IR to the components remaining as well as those leaving the heated zone. With calibration, molar relationships can be established between components leaving and remaining for the most detailed information. More sophisti-cated data handling procedures can be applied to the evolving spectral techniques to aid the well-established conventional thermal techniques.

REFERENCES

Fenner, R. A., and Lephardt, J. O. (1981). J. Agric. Food Chem. 29, 846. Lephardt, T. O., and Fenner, R. A. (1980). Appl. Spectrosc. 34, 174. Liebman, S. A., Ahlström, D. H., and Griffiths, P. R. (1976). Appl. Spectrosc. 30, 355. Roush, P. B., and Huppier, D. A. (1982). Am. Lab. {Fairfield, Conn.) 14, 160.

DETERMINATION OF FUNCTIONAL GROUPS IN COAL BY FOURIER TRANSFORM INTERFEROMETRY

Paul Painter Michael Starsinic Michael Coleman Polymer Science Program Department of Materials Science and Engineering The Pennsylvania State University University Park, Pennsylvania

I. Introduction 169 II. Aspects of the Structure of Coal 172

III. Infrared Spectrum of Coal 175 A. The OH and NH Stretching region 177 B. The CH Stretching Region 177 C. The Region 1800-1500 c m 1 182 D. The Region 1400-1000 crrr1 186 E. Bands between 1000 and 700 c m 1 187

IV. Sample Preparation 189 A. Alkali Halide Pellets 191 B. Diffuse Reflectance 194

V. Determination of Oxygen-Containing Functional Groups in Coal 196 A. Measurement of OH Groups 197 B. Determination of COOH Groups 207 C. Approximate Distribution of

Oxygen-Containing Functional Groups in a Set of Vitrinites 210

VI. Determination of Aliphatic and Aromatic CH Groups in Coal 213

VII. Application of Spectroscopic Data to the Calculation of Coal Structural Parameters 235 References 240

I. INTRODUCTION

In many respects, those of us who practice, write, and lecture about Fourier transform interferometry (FT-IR) are guilty of perpetuating an illusion. To the uninitiated, this technique seems to have burst on an


ISBN 0-12-254104-9

170 Paul Painter, Michael Starsinic, and Michael Coleman

unsuspecting world with the promise of solving all sorts of intractable problems. In reality, we are observing the impact of instrumental ad-vances on a well-understood analytical tool. It is rather like seeing an old friend return from the wilderness with new clothes and a face lift. The surface may be superficially dazzling, but underneath many of the old problems persist. In addition, our old friend has become more compli-cated. It has managed to become hooked up to a computer capable of all sorts of sophisticated data manipulations. Because of this, it is terribly easy to generate spectral results that have only a passing acquaintance with reality. Consequently, when applying FT-IR to complicated prob-lems, such as the characterization of multicomponent polymeric materials like coal, the spectroscopist still has to rely on the fundamentals of his or her art: experience, common sense, and informed intuition.

This is not to say that FT-IR spectrometers do not have overwhelming advantages over the dispersive instruments that many of us older hands recall, either with loathing or nostalgia depending on whether our new machine has once again broken down These advantages have been de-tailed in various texts and reviews (see Griffiths, 1975; Koenig, 1975), some of the best of which can be found in previous volumes of this treatise (Hirschfeld, 1979; Krishnan and Ferraro, 1982). Essentially, the use of an interferometer rather than a system of gratings and slits confers advantages of enhanced optical throughput and speed. For many applica-tions, however, the optical advantages are not critical, and the novelty of the instrument has been the application of various data handling pro-grams. Naturally, such programs could equally well be applied to data obtained on dispersive instruments. In fact, many of the routines that have become associated with FT-IR were first developed in the 1960s by Jones and co-workers (1963; Jones, 1969a,b). But in one sense this work was ahead of its time. It is inconvenient and time-consuming to punch data onto tape or cards and transfer this information to a large, multiuser computer for processing. Once spectrometers were coupled to small, powerful, reasonably priced minicomputers, the ball game changed. Now many spectroscopists run around with the harried and guilty look of those who feel they should know more about computers. This is because we have at our disposal cleverly designed programs that are both interactive and simple to use. In the depths of our spectroscopic souls, however, we are suspicious of some of the results we are generating—and rightly so. Nevertheless, the advances are real, and as long as a critical eye is kept on technique, we can have some confidence in them.

The application of FT-IR to the characterization of coal has demanded the utilization of the complete range of this instrument's capabilities. The optical advantages result in spectra with a superior signal-to-noise (S/N)

5 Determination of Functional Groups in Coal 171

ratio, a prerequisite if a number of subsequent data manipulations are to be performed. The sensitivity of the instrument also allows it to be used as a detector in, for example, liquid and gas chromatography (GC) experi-ments. Some very nice initial results were reported by Brown et al. (1981), who applied HPLC-FT-IR to the monitoring of functionalities in size-separated synfuels. The use of GC-FT-IR to detect the gaseous products of coal pyrolysis can be confidently anticipated. The use of various data handling programs has made a major impact on the analysis of mineral matter in coal (Painter et al., 1978, 1981a; Painter and Cole-man, 1980), and the detection of the products of coal oxidation (Painter et al., 1981b; Fuller et al., 1982; Rhoads et al., 1983). Ultimately, how-ever, the real nitty-gritty of coal structural analysis is the quantitative determination of specific functional groups. This problem pervades all aspects of coal science, from the question of the structure of coal and its variation with geographic and geological origin to questions concerning the chemical changes that occur on liquefaction and gasification. The development of appropriate methodology has been a central concern of ours for some years; so, at the risk of being parochial and ignoring good work in other areas of the application of FT-IR to the study of coal, we shall concentrate here on the quantitative determination of oxygen-con-taining functional groups and aliphatic CH and aromatic CH content. These functionalities are the most amenable to infrared (ir) analysis. Un-fortunately, the procedures are not simple, and the field is not without controversy.

A final word concerning the organization of this chapter is in order. It is often the case that when a new technique comes along the results and experience of those who worked on older instruments are regarded as somewhat prehistoric and sometimes casually discarded. As a result, much time and effort are spent reinventing the wheel. In order to get a good grasp of the usefulness and limitations of FT-IR, particularly in the study of a complex material such as coal, it is necessary to consider the results in the context of what has gone before. Consequently, much of this chapter also considers results obtained on dispersive instruments. As we shall see, much of what we are doing now is not original. Essentially, FT-IR has allowed us to develop ideas that can be found scattered throughout the literature of the 1950s and 1960s, the heyday of ir studies of coal. Furthermore, in order to discuss the ir spectrum of coal in a coherent fashion, it is necessary to have at least a qualitative understanding of current theories concerning the structure of coal and how this would be reflected in the spectrum. Accordingly, in the following two sections we consider coal structure and band assignments, respectively, and then turn our attention to quantitative analysis.


II. ASPECTS OF THE STRUCTURE OF COAL

Neavel (1981) remarked that coal is analogous to fruitcake in that it is an aggregate of different and distinguishable components. One of these components is often mineral matter, which can vary from trace amounts to —30% by weight, depending on the origin of the sample. The inorganic component can exist as a separate phase or as inorganic elements che-lated or ionically bound to the organic component. The latter type of structure often occurs in lignites, which have a significant concentration of carboxylic acid groups that have exchanged to form carboxylate salts. The organic component consists of so-called macerals, entities that can usually be distinguished under an optical microscope. For most U.S. coals the major maceral type present (often greater than 90% of the or-ganic phase) is vitrinite, so that an understanding of many structure-property relations will ultimately depend on a knowledge of the structure of this maceral type and its variation with rank.

The rank of a coal is defined in terms of the degree of metamorphism or coalification of plant matter. Although this is a continuous function, it is convenient to assign a coal to one of a relatively small number of rank classes. In North American terminology, and arranged in ascending order of carbon content, these can be written as peat —» lignite —> subbituminous coal —> bituminous coal —> anthracite.

This is a simplified classification from which more elaborate systems have been constructed (Berkowitz, 1979). At this point, however, it is sufficient for our purposes to note that coal is primarily an aromatic material, and the aromaticity (the fraction of carbon atoms involved in aromatic units) increases with rank. On the basis of analysis of x-ray scattering curves, it has been suggested that coals in the bituminous range contain polycyclic condensed systems consisting of an average of one to three rings. A proportion of these have phenolic OH groups. Six-mem-bered rings are thought to predominate, although five-membered configu-rations have been detected. Many of the condensed systems are heterocy-clic, containing oxygen, nitrogen, or sulfur atoms. A substantial amount of hydroaromatic structure is thought to be present, and the various con-densed-ring systems are linked predominantly by alkyl and ether linkages.

There have been numerous attempts to link bits and pieces of such information to form pictorial representations of ''typical'' structures found in coal. A classic example is the hypothetical structure proposed by Given (1960), illustrated in Fig. 1. This was constructed so as to conform with various experimentally measured parameters, one of the most impor-tant of which was the ratio of aromatic to aliphatic hydrogen determined in Brown's (1955) seminal ir spectroscopic study of British coals. Peter


Fig. 1. Given's model of coal structure.

Given has been the first to point out that many of the arguments offered in the defense of this structure are no longer valid. Nevertheless, it remains a useful visualization and is the proper starting point for a consideration of the evolution of coal model structures. A number of these have been proposed, and the interested reader should consult the paper by Spiro (1981), which describes space-filling models of various structures. Many of the features of Given's model have been incorporated into subsequent hypothetical structures, but we shall consider only one important modifi-cation because of its relevance to our discussion of ir measurements.


Most of the aliphatic groups in Given's model are in hydroaromatic or alicyclic structures. In order to account for the fragments found on lique-faction or pyrolysis, however, it has been proposed that short aliphatic linkages (principally methylene) constitute the principal connecting bridges between the aromatic entities. The model proposed by Wiser (1978), shown in Fig. 2, incorporates these features. Such structures were conceived as being clustered in stacked layers, so that a bituminous coal could be thought of as a random stacking and orientation of micelles.

It should be emphasized that the creators of these models never consid-ered them to be unique structures or repetitive units characteristic of a coal "molecule." They should be considered useful illustrations of the types of units to be found in coal. Nevertheless, this approach has until recently dominated our concepts of coal structure. This has changed with the move to consider coal as a macromolecular network.

The application of the theoretical tools of polymer science to the char-acterization of coal structure dates from the work of van Krevelen (1966) and van Krevelen and Schuyer (1957) but has been expanded considera-bly with the work of Larsen (1981), Green et al. (1982), and Lucht and Peppas (1981). Much of this work has involved the determination of mo-

OH

Fig. 2. Wiser model of coal structure.


lecular weight. (For an infinite three-dimensional network, the molecular weight between cross-link points is the quantity of interest.) One aspect of this approach that is relevant to this discussion, however, is the consider-ation of coal as a condensation polymer (van Krevelen, 1966) If, for example, a trifunctional unit is polymerized, the initial products are low molecular weight materials, a predictable distribution of monomers, di-mers, trimers, etc. At a certain point during the polymerization an insolu-ble three-dimensional network is formed, the so-called gel. Nevertheless, soluble material or sol remains, but the sol/gel ratio continues to decrease as the reaction proceeds and low molecular weight material is incorpo-rated into the network. Van Krevelen (1966) determined a gratifying cor-respondence between coal and condensation polymers in this regard; the proportion of soluble material that can be extracted from coal decreases with increasing rank. [Note that this characteristic is solvent dependent. Pyridine solubility increases with coal rank to reach a maximum in coals of —88% carbon content, then decreases precipitously in higher-rank coals. If other solvents (e.g., ethylenediamine) are considered, however, a general trend of decreasing solubility with increasing rank becomes apparent.] Accordingly, there is some merit to the view that the soluble material extractable from coal corresponds closely to the units found in the parent structure. For bituminous coals, where the extractable material can be 20-30% (or more), this would not seem to be unreasonable. The principal danger occurs when a selective solvent is used to extract a minor component that may not be representative. In any event, the importance of coal extracts for ir studies is that an independent measure of the aliphatic and aromatic CH content can be determined by Ή NMR. This should allow various methods for determining ir absorption coefficients to be tested directly by studies of extracts. This is discussed in Section VI.

III. INFRARED SPECTRUM OF COAL

A prerequisite for the application of ir spectroscopy to the quantitative determination of functional groups in any material is correct band assign-ments. Most assignments are based on the so-called group frequency approach. Since the classic studies of Coblentz at the turn of the century, a number of dedicated spectroscopists have systematically measured an enormous range of compounds and empirically established band fre-quency-functional-group relationships. Two of the best compilations and interpretations of these data are to be found in the books by Bellamy (1975) and Colthup et al. (1975). When considering group frequencies, one must always keep in mind an important limitation. Infrared bands are assigned to vibrations of chemical bonds in terms of bond stretches, bond


angle bends, etc. Some of these vibrations are more or less localized. For example, CH stretching modes and C = 0 stretching modes are relatively "pure"; in other words, they are not coupled to or mxed with other vibrations. Conversely, the vibrations of an ether linkage in a —C—C —O—C type of structure are mechanically coupled to the vibrations of the adjacent C—C bond. The ir bands characteristic of the vibrations of this unit cannot be separately assigned to CC and CO stretch, but have a hodgepodge character including various contributions from these (and other) bonds. Consequently, the only bands that are of direct use in quantitatively measuring functional groups are those in which the vibra-tional motion is well defined.

This brings us to a discussion of band assignments in the ir spectra of coals. Although coal is an extraordinarily heterogeneous material, the general features of the spectra do not vary significantly with rank or mac-eral type. The variations are principally in the relative intensities of the bands that comprise the spectrum, as we illustrate later. This allows us to consider the spectrum of a vitrinite concentrate obtained from a bitumi-nous coal and work through the band assignments. These assignments will then, with one or two reservations concerning intensities and subtle shifts in band position, be applicable to other coals. The spectrum we shall use as our example is shown in Fig. 3. Selected assignments are

Coal 0-H groups water

CH 2 ,CH 3

Bending modes

I C-0 stretch, C-C stretch f 0-H bend, etc.

-> | i i i | i i i | i i i | 1 1 1 1 1 1 1 , 1 1 , 1 . , , ,

3600 3200 2800 2400 2000 I600 I200 800 cm-1

Fig. 3. FT-IR spectrum of a vitrinite concentrate (PSMC 52).

Aliphatic C-H stretch Aromatic ring

s t re tch^


given in his figure, and a more complete listing is given in Table 1. For expository purposes it is convenient to divide the spectrum into five re-gions and consider these separately.

A. The OH and NH Stretching Region

The region 3800-3200 cm-1 of the ir spectrum is characteristic of vari-ous OH and NH stretching modes. Although the general assignment of bands in this frequency range is relatively straightforward, separate as-signment to specific groups is far from trivial. For example, non-hydro-gen-bonded OH groups absorb near 3600 cm-1 (a weak shoulder can be seen at this position in Fig. 3), whereas their hydrogen-bonded counter-parts have peaks near 3400 cm 1 . In addition, NH groups absorb in the range 3200-3400 cm 1 , whereas the OH stretching modes of hydrogen-bonded —COOH groups display a broad weak band between 3300 and 2500 cm-1. As if this were not bad enough, the strong OH stretching mode of water is superimposed on these bands. The spectrum shown in Fig. 3 is that of a sample dispersed in KBr. Sample preparation is considered in the following section, but it is pertinent to point out here that water is ad-sorbed onto KBr during grinding. Its effect can be minimized, but not eliminated, by drying. (The spectrum shown in Fig. 3 was not dried.) Furthermore, water is bound by coal itself in a complex manner that is apparently a function of rank. Undaunted by these difficulties, several investigators have nevertheless used this region of the spectrum to de-termine OH groups quantitatively. These results are considered in Section V.

B. The CH Stretching Region

The second region of the spectrum we shall discuss is characteristic of CH stretching modes. Generally speaking, aromatic CH groups have bands between 3100 and 3000 cm-1, whereas aliphatic CH stretching bands fall between 3000 and 2700 cm 1 . There are some exceptions to this rule; for example, the bands of very highly strained aliphatic rings are shifted to the 3000 cm-1 range. There is little evidence, however, for the presence of such groups in coal, but this simplifies band assignments only marginally. To start with, the aromatic CH modes are not pure stretching vibrations, but a Fermi resonance complex with overtones and combina-tions of lower-frequency modes. Fortunately, these lower-frequency vi-brations are also aromatic CH modes (various CH bends). Partly as a result, the number of aromatic CH bands varies with the pattern of aro-matic ring substitution, which in turn varies with rank. In lignites there is a single weak band. The bituminous sample shown in Fig. 3 clearly has at

TABLE 1

Band Assignments for the Infrared Spectra of Coals

Aliphatic and aromatic

Wavenumber ( c m 1 )

3030 2950sh 2920,

2850

1600

1490sh 1450

1375

900-700

860 833 (weak)

815

750

groups

Assignment

Aromatic CH CH3

Aliphatic CH, CH2, and CH3

Aromatic ring stretch

Aromatic ring stretch CH2 and CH3 bend; possi-

bility of some aromatic ring nodes

CH3 groups

Aromatic CH out-of-plane bending modes

Isolated aromatic H 1,4-Substituted aromatic

groups Isolated H and/or two

neighboring H 1,2-Substituted, i.e., four

neighboring H

Oxygen-containing functional groups

Wavenumber (cm - ')

3300

1835 1775-1765

1735 1690-1720

1650-1630

-1600

1560-1590

1330-1110

1100-1000

Assignment

Hydrogen-bonded

C = 0 , anhydride C = 0 , ester with elec-

tron-withdrawing group attached to single-bonded oxygen

O II

Ar—O—C—R C = 0 , ester C = 0 , ketone, aldehyde,

and COOH C = 0 , highly conjugated,

O II

e.g., Ar—C—Ar Highly conjugated hydro-gen-bonded c = o

Carboxyl group in salt form COO

CO stretch and OH bend in phenoxy structures, ethers

aliphatic ethers, alcohols


least two aromatic CH modes. In higher-rank samples we have detected at least three bands. This is an important factor when one is using curve-resolving techniques to separate the overlapping contribution of aliphatic and aromatic CH modes in higher-rank samples. As we show later, this is accomplished by means of curve-resolving techniques. If this is to be done accurately, a sufficient number of bands to reproduce the band area accurately must be included in the fit.

The model structures proposed by Given and Wiser demonstrate the importance of being able to distinguish between and measure aliphatic and hydroaromatic entities. Unfortunately, we do not believe that this can presently be accomplished by means of ir techniques, as a detailed analy-sis of the aliphatic CH stretching modes will indicate. A scale-expanded plot of the region 3000-2700 cm-1 of the spectrum presented in Fig. 3 is shown in Fig. 4. We have curve-resolved this profile into five bands. It is not our purpose to consider curve-resolving methodology here; this has been reviewed in an excellent article by Maddams (1980), which should be required reading for all spectroscopists. We discussed the application of the principles enunciated by Maddams to the analysis of coal spectra in a

2923 I

1 1 3000 2900 2800

cm Fig. 4. Aliphatic CH stretching region of a vitrinite concentrate (PSMC 52) curve-

resolved into five bands.


previous review (Painter et al., 1981c), but it is pertinent to point out that a prerequisite for accurate curve resolving is a knowledge of the number of bands in the spectral region of interest. Derivative methods or self-deconvolution procedure can aid in this, but neither method is infallible. Depending on such factors as the relative intensities of the bands involved, it is not possible to detect modes separated by less than about half their half-width using the second derivative. Self-deconvolution requires an assumption of the band shape, and in our experience it is rather easy to generate artifacts using this technique. In the final analysis, the results of any curve-resolving exercise should be regarded with a degree of healthy skepticism. We should always ask ourselves the question, Do the results seem reasonable in terms of what we know of the structure of this mate-rial and group frequencies? In this respect, the bands curve-resolved in Fig. 4 (using a least squares procedure and a band shape function that is a sum of Laurentzian and Gaussian contributions; see Painter et al., 1981c) are not unreasonable.

On the basis of assignments most often made in this region of the spectrum, it is tempting to assign the bands at 2956 and 2864 cm-1 to the asymmetric and symmetric stretching modes of CH3 groups, the bands at 2923 and 2849 cm-1 to the asymmetric and symmetric vibrations of CH2 groups, and the band near 2891 cm-1 to lone CH groups. Unfortunately, the situation is much more complicated, as a consideration of the funda-mentals of the spectra and model compounds will demonstrate.

The limitations of curve resolving and the inherent broad character of the ir bands of coal mean that the bands we have identified are effectively inseparable composites of contributions from different groups. The band at 2923 cm-1 certainly has a contribution from methylene bridges (but not hydroaromatic structures). Methyl groups attached directly to aromatic groups also have their strongest band near this frequency (Bellamy, 1975). The 2891 cm-1 band is not simply due to lone CH groups, but also has a contribution from the overtone and combination bands of lower-fre-quency bending modes, probably intensity-enhanced by Fermi resonance interactions. Examples of the characteristic CH stretching modes of hy-droaromatic structures are illustrated in Fig. 5, which compares the spec-tra of three model compounds: 9,10-dihydroanthracene, 9,10-dihy-drophenanthrene, and indane. The regions between 3000 and 3100 cm-1

and 2800 and 2900 cm-1 (assigned to aromatic CH stretching modes and symmetric aliphatic CH stretching modes, respectively) are complicated by combination and overtone modes of lower-frequency bending modes. This is not uncommon in such low molecular weight model compounds and is a severe limitation in using such materials for determining extinc-tion coefficients for use in quantitative coal studies. The key observation


2950

Fig. 5. Comparison of the CH stretch-ing region of the infrared spectra of three hydroaromatic model compounds. (A) In-dane; (B) 9,10-dihydroanthracene; (C) 9,10-dihydrophenanthrene.

3150 2700

to be made from these spectra, however, is that the frequency of the asymmetric CH2 stretching mode appears near 2950 cm-1 in both 9,10-dihydroanthracene and 9,10-dihydrophenanthrene, significantly shifted from the 2925 cm-1 frequency usually observed for CH2 groups. In the spectrum of indane the shift is smaller, to 2935 cm-1. Corresponding shifts for sterically strained alkane ring compounds have been considered in some detail in classic group frequency texts, such as that of Bellamy (1975). The CH stretching frequencies of CH2 groups increase and their intensities decrease progressively with bond angle strain. A correspond-ing interpretation is clearly in order for the frequency shifts observed in hydroaromatic structures such as 9,10-dihydroanthracene and 9,10-dihy-drophenanthrene. We would anticipate that the steric strain would be less in indane. In materials such as Tetralin, where bond angle rotations can


relieve even more steric strain, the asymmetric CH stretching modes appear at almost the same frequency as in the alkanes.

To summarize these results, the 2956 cm-1 band that can be curve-resolved in the spectra of coals, as well as the band near 2925 cm-1

discussed previously, must be considered a composite of various overlap-ping contributions. Methyl groups attached directly to aromatic rings have a band near 2950 cm-1 (as well as a stronger mode near 2925 cm-1), methyl groups attached to other alkyl groups have bands between 2960 and 2970 cm-1, and methylene groups in certain types of hydroaromatic structures absorb near 2950 cm-1. Because of these complications the modes between 2900 and 3000 cm-1 are of little use in distinguishing among various types of aliphatic structures. The symmetric stretching modes near 2850 and 2865 cm-1 are not as sensitive to structure as their asymmetric counterparts and so are potentially of some value in providing at least an approximate measure of methylene and methyl group content, respectively. In order to obtain a measure of total aliphatic CH content, however, we are forced to use the entire integrated area between 2995 and 2750 cm-1, thus assuming that the average value of the absorption coeffi-cient (relating band intensities to the concentration of the appropriate functional group) stays approximately constant from coal to coal. This is clearly unsatisfactory but seems to give consistent values for coal ex-tracts, where aliphatic CH content can be independently checked by Ή NMR (see Section VI).

C. The Region 1800-1500 cm - 1

Bands in this region of the spectrum are characteristic of various aro-matic ring modes and carbonyl and carboxylic acid groups. The carbonyl stretching modes of the latter groups have well-established group frequen-cies, summarized in Table 1. The assignment of the 1600 cm-1 band, the strongest in the spectrum of coal, has been a subject of controversy for some time. We have argued that an assignment to an aromatic ring stretching mode is most likely (Painter et al.f 1981c, 1983b). The alterna-tive assignment, favored in some reviews (Dryden, 1963), is to a highly conjugated hydrogen-bonded carbonyl. Many authors, however, have preferred to leave the question open. Some of the most recently cited FT-IR evidence rests on the observation that in the spectra of coal liquefac-tion products and coal there is an increase in the intensity of the 1600 cm-1 band that parallels increasing phenolic OH content (Solomon, 1979; Solomon et al., 1982). Unfortunately, these data could be interpreted in terms of both assignments. Ring modes could be intensity-enhanced due


to the presence of phenolic OH, or it could be argued that the correlation simply reflects the fact that in coals with a highr phenolic OH content there are more chelated conjugated carbonyl groups. If the controversy is to be resolved, the previous evidence in favor of the carbonyl assignment has to be explained. The principal evidence is the following:

(1) Fujii (1963a,b) observed that the intensity of the 1600 cm-1 band in certain coal samples increased after methylation and reduction re-actions.

(2) Fujii et al. (1970) also observed an increase in the intensity of the 1600 cm-1 band with increasing oxygen content of a set of coals.

(3) There is an absence of appropriate model compounds in which a strong band near 1600 cm-1 can be unambiguously assigned to an aromatic ring mode. In the spectra of most aromatic materials the 1600 cm-1 band is weak.

The assignment of the 1600 cm-1 band to a carbonyl group is anomalous on a number of grounds. First, conjugated hydrogen-bonded structures similar to acetylacetone have more than one characteristic absorption band. In effect, functional groups of this type have four nearly equivalent bonds (two CO and two CC), and prominent modes near 1600, 1500, 1450, and 1260 cm-1 should be observed (Colthup et al., 1975). In the spectra of coals, bands are observed at these frequencies but are relatively weak and broad and can be clearly assigned to other functional groups. Second, on methylation of the OH groups involved in hydrogen bonding to a highly conjugated carbonyl, we would expect a shift from about 1600 to 1650 cm"1, where carbonyl groups in, for example, quinone-type structures absorb strongly (Bellamy, 1975; Colthup et al., 1975). On methylation, however, Fujii (1963a,b) observed a band appearing at much higher fre-quencies, near 1700 cm-1.

In contrast to the observations of Fujii (1963a,b), Durie and Sternhell (1959) observed no change in the intensity of the 1600 c m 1 band on acetylation of a set of coals. As we previously pointed out (Painter et al., 1981c, 1983b), the discrepancy between the acetylation results of Durie and Sternhell and the methylation studies of Fujii can be easily explained. The initial work of Fujii involved the study of a low-rank coal (77.9% carbon), whereas Durie and Sternhell examined bituminous coals (84.0-89.9% carbon). In low-rank coals there is usually a significant concentra-tion of COO" groups, which absorb near 1580 cm"1 and hence contribute to the intensity of the 1600 cm"1 band. Consequently, we would anticipate an increase in the intensity of the 1600 cm"1 band with increasing oxygen content (and decreasing rank) of the coal as a result of the contribution of


bands due to carboxyl groups that overlap the 1600 cm-1 mode. Further-more, on methylation (but not acetylation), the COO- group is converted to an ester that absorbs near 1700 cm-1, as observed by Liotta (1979).

The previous work concerning the relation of the intensity of the 1600 cm-1 band to the oxygen content of the coal and the changes observed on methylation can therefore be readily explained in terms of bands due to carboxyl groups that overlap the 1600 cm-1 band. In order to confirm an assignment to a ring stretching mode, however, additional evidence is required. Two factors must be considered. First, the aromatic ring stretching mode is weak in the spectra of most low molecular weight aromatic materials (benzene, naphthalene, anthracene, phenanthrene, etc.), yet intense in the spectra of coals. Second, in the characteristic pattern of ring stretching frequencies observed for most aromatic materi-als, four modes are usually observed: near 1600, 1590, 1490, and 1450 cm-1. In most coals the 1600, 1490, and 1450 cm-1 bands are clearly present. The 1490 cm-1 band is a weak shoulder, whereas the 1450 cm-1

mode is partially obscured by the aliphatic CH2 and CH3 bending modes. This leaves the 1590 cm-1 band.

We shall first consider the question of the intensity of the 1600 cm-1

band. In his seminal paper on coal, Brown (1955) made the observation that the 1600 cm-1 band is much more intense in the spectra of phenols, presumably because the presence of the oxygen atom results in a larger change in dipole moment during the vibration. However, we also have to consider that most model compounds are low molecular weight materials. If aromatic structures are linked in a polymeric structure, we would ex-pect the ir spectrum to be influenced by such factors as conjugation and vibrational coupling. We have synthesized phenolic resins that appear to be excellent spectroscopic models for coal (Painter et al., 1983b). There are remarkable similarities in the spectra, particularly the intense 1600 cm-1 band observed in this polymer. Consequently, it is not unreasonable to assign a strong 1600 cm-1 band to an aromatic ring stretching mode in structures in which there is the possibility of intensity enhancement due to the presence of phenolic groups or due to the linkage of aromatic entities by methylene and possibly ether bridges.

Although the arguments and evidence so far presented in favor of an assignment of the 1600 cm-1 band to an aromatic ring stretching mode are cumulatively convincing, the missing 1590 cm-1 peak must still be identi-fied. Furthermore, we would anticipate the presence of a complex of bands in the region 1600-1700 cm-1 of the spectrum due to modes associ-ated with quinone groups (thought to be present in coal) in addition to the aromatic ring stretching modes. (Additional complications due to the presence of carbonyl and carboxyl groups can be expected in oxidized or


low-rank coals.) Clearly, the identification of these various bands requires a deconvolution of the 1600 cm-1 region of the spectrum.

If curve-resolving procedures are to be applied with any confidence, it is necessary to first determine the number of bands contributing to a particular spectral profile and also obtain an initial estimate of peak posi-tions and widths at half-height (Maddams, 1980). We have mentioned the use of derivative methods to obtain such information and described a least squares curve-resolving program that uses a band shape function that is a sum of Gaussian and Lorentzian contributions (Painter et al., 1981c). These procedures have been applied to the 1600 cm-1 region of a number of vitrinite concentrates (Painter et aL, 1983b). Representative spectra are shown in Figs. 6 and 7 (concentrates PSMC 36 and 49 with carbon con-tents of 84.3 and 88.3%, respectively). The second derivative of each spectrum is also shown in each figure, and minima clearly show the pres-ence of a number of bands. By use of the data from the second-derivative profile, each spectrum has been resolved into its constituent bands, again as shown in each figure. It can be seen from Fig. 6 that the second derivative clearly demonstrates the presence of two bands near 1600 cm-1, at 1614 and 1589 cm 1 . The frequencies and relative intensities of

Fig. 6. Bottom: infrared spectrum between 1800 and 1500 cm ' of a vitrinite concentrate, PSMC 36 [84.3% carbon (dmmf)] with individual curve-resolved bands. Top: second derivative of spec-trum.

1495

1800 cm

1500

Paul Painter, Michael Starsinic, and Michael Coleman

I

Fig. 7. Bottom: infrared spectrum be-tween 1800 and 1500 cm ' of a vitrinite con-centrate, PSMC 49 [88.3% carbon (dmmf)], with individual curve-resolved bands. Top: second derivative of spectrum.

these two bands are classic group frequencies for aromatic ring stretching modes. The only other possible assignment of the 1589 cm-1 band is to a COO~ group, but such groups can be readily identified by acid washing, when they shift to 1700 cm-1. Such a shift did not occur for these samples.

D, The Region 1400-1000 cm - 1

The region 1400-1000 cm-1 is a superposition of a number of broad overlapping bands. Only one mode, the 1375 cm-1 methyl bending vibra-tion, has a clear-cut, well-established identity. The others are highly mixed and coupled vibrations. In some studies bands in this region have been deconvoluted and assigned to ethers (Solomon, 1979; Solomon et al., 1982). A close examination of the literature concerning coal spectra and group frequencies, however, demonstrates that assignments in this region are complicated, and it is often not possible to assign bands to specific functional groups. Essentially, vibrations involving CO stretching (presumably in both ethers and phenols), CC stretching, OH bending, and CH2 bending appear in this region of the spectrum. Close-lying vibrational energy levels having the same symmetry properties are allowed to mix, so

186

1498

1800 1500


that each mode can take on some of the character of the others. There is the further possibility or even probability of mechanical coupling be-tween, for example, adjacent CC and CO stretching vibrations in ethers and between CO stretching and OH bending motions in phenols. Conse-quently, bands between 1000 and —1350 cm-1 cannot be described in terms of simple motion of specific functional groups or chemical bonds.

E. Bands between 1000 and 700 cm - 1

In the spectra of most bituminous coal samples, three bands near 860, 815, and 750 cm-1 can be clearly identified. These have been assigned to aromatic CH out-of-plane bending modes. Work in this laboratory has demonstrated, however, that these bands are a superposition of a number of modes (Kuehn et al., 1982). Figure 8 shows the scale-expanded region 925-700 cm-1 of the spectrum of one of the vitrinite concentrates together with the second-derivative profile. A very sharp minimum at 875 cm"1 is due to the presence of trace amounts of calcite, represented by the shaded area in the original spectrum. Naturally, sharp bands give more clearly defined and intense second-derivative profiles than broad bands. The con-tribution of calcite was subsequently subtracted out, but the key observa-

Fig. 8. Bottom: region 900-700 cm ' of the infrared spectrum of a vitrinite concentrate (shaded area represents a small contribution from calcite). Top: second derivative of this spectrum.

900 800 cm"1

—I 700


tion is the presence of other minima (e.g., at 780, 830, and 888 cm-1) in addition to the modes apparent from a visual inspection of the spectrum. Tschamler and de Ruiter (1962) also observed these modes, which were much more intense in the spectra of certain solvent extracts, so it can be reasonably assumed that they are not artifacts due to background ' 'noise" in the spectrum. Furthermore, the same bands were determined in the spectra of a number of vitrinite concentrates (Painter et al., 1978). We would assume that the effect of noise would be random.

By use of the parameters defined by second-derivative studies, the presence of seven bands was determined in the region 900-700 cm 1 . A typical curve-resolving result is shown in Fig. 9 (PSMC 67). In all spectra, broad bands at 834 and 785 cm-1 were observed. The remaining five bands were considerably sharper. It is important to note that, if seven curves are to be accurately defined in such a limited region of the spectrum, it is essential to have a large number of points defining the spectral profile. These spectra were obtained routinely at a resolution of 2 cm-1, but as a check a spectrum of one vitrinite concentrate was obtained at a resolution of 1 cm-1 and gave similar results upon curve resolving. The bands were much less clearly defined at lower resolutions (e.g., 4 cm1)» however, and detailed work should be performed at a higher resolution.

By considering the results obtained from curve resolving a number of vitrinite concentrates, it was demonstrated that the bands at 830 and 785 cm-1 have a different pattern of behavior as a function of rank com-pared with other bands in this region (Kuehn et al., 1982). One possible explanation is that these bands have a contribution from aliphatic CH2

^ ■ Fig. 9. Spectrum shown in Fig. 8 curve-re-crn1 solved into seven components.


rocking modes. Isolated and two adjacent CH2 units have bands near 830 and 785 cm-1, respectively. In the vitrinite spectra, however, the bands resolved at these frequencies are both broad and relatively intense, much more so than expected from an assignment to vibrations of aliphatic groups. Furthermore, a decrease in the intensity of such modes would be anticipated as a function of increasing rank, rather than the observed slight increase. These observations can be explained in terms of the limi-tations of curve resolving mentioned earlier. It is not possible by means of second-derivative methods to discriminate among bands separated by less than their semi-half-widths. (The resolution also depends on factors such as the relative intensities of the bands involved.) It was therefore sug-gested that the bands resolved at 830 and 785 c m 1 are composites of aliphatic rocking modes and "secondary" aromatic out-of-plane bending modes. Any aromatic molecule with more than one CH group has a num-ber of out-of-plane bending vibrations, but many of these modes are usu-ally less intense than the characteristic in-phase CH vibration used for group frequency identifications. For example, anthracene has a promi-nent band at 788 cnrr1 in addition to the stronger 750 cm-1 band used to identify four adjacent aromatic C—H bonds. It is these modes that have been identified as "secondary" (clearly, the 788 cm-1 band that is also characteristic of four adjacent CH groups would in practice be insepara-ble from any aliphatic CH2 rocking modes near 785 cm1) .

The remaining five bands curve-resolved between 900 and 700 c m 1 can be clearly assigned to specific out-of-plane modes. Regions characteristic of a specific number of adjacent CH groups are marked on Fig. 9. Both the 888 and 864 cm-1 bands can be assigned to lone aromatic CH groups, and the clear separation of these modes suggests groups in different local environments (e.g., isolated CH groups on the central carbons of an an-thracene-type structure compared with an aromatic CH group that has become isolated through substitution of other functional groups onto adja-cent carbon atoms of a ring). The frequency range characteristic of three adjacent aromatic CH groups overlaps considerably with that characteris-tic of two adjacent groups, although we prefer an assignment of the higher-frequency 815 cm-1 mode to the latter. Four adjacent CH groups are clearly defined by the band at 750 cm-1.

IV. SAMPLE PREPARATION

Traditionally, ir spectra can be obtained by two principal methods, transmission and scattering, although there are various options in each of these categories. In addition, the optical advantages of FT-IR are such that spectra of reasonable quality can now be obtained with photoacoustic


devices. Each of these has advantages and disadvantages in terms of speed, quality of spectrum, susceptibility to distortion, and application to quantitative analysis.

Transmission methods usually rely on dispersing the sample in a me-dium that is transparent in the ir region of interest. The work of Brown (1955) in the 1950s was based on dispersing coal into Nujol mulls. P. H. Given (private communication, 1983) recalls that this was an extremely tedious procedure and that Brown would sit for hour upon hour labori-ously grinding his samples with a mortar and pestle while reading a book. Such procedures were necessary to reduce the particle size of the sample to less than that of the ir radiation and so reduce scattering effects. Even with modern methods of sample preparation, however, such scattering effects are seldom entirely eliminated, as we shall see.

Most transmission methods for solids now rely on dispersing the sam-ple in an alkali halide matrix (usually KBr), which can be pressed into a glassy pellet. We discuss some of the advantages and disadvantages of this procedure in the following section, but it is pertinent to point out here that the grinding is now done mechanically, usually with a so-called Wig-L-Bug, so that the methodology lends itself to accurate weighing and reproducible grinding, a necessity for good quantitative work. An alterna-tive to dispersion in a transparent matrix was employed by Gethner (1980), who produced thin sections of coal by microtoming. This proce-dure does not readily lend itself to quantitative work and is certainly not something that most laboratories are equipped to perform. Accordingly, we will not discuss this method here, but those readers interested in swelling and other in situ studies should certainly consult this paper.

Scattering or reflection methods for obtaining ir spectra used to involve the use of attenuated total reflectance (ATR). This relies on good contact between the ATR windows and the sample, something that is not easy to achieve for powdered solids such as coal. This changed with the pioneer-ing work of Fuller and Griffiths (1978) and Fuller et al. (1982), who suc-cessfully developed diffuse reflectance techniques for FT-IR and demon-strated their usefulness in coal characterization studies.

It is beyond the scope of this chapter to describe the details of sample preparation procedures. The interested reader should refer to appropriate texts and papers. Our concern here is the application of these procedures to quantitative spectroscopic studies of coal and the pitfalls to be avoided. We first discuss conventional dispersion procedures and then briefly con-sider diffuse reflectance. Photoacoustic studies have so far been limited to demonstrations of the capacity of the technique to obtain spectra, and we do not consider these preliminary results here.


A. Alkali Halide Pellets

For most laboratories the method involving alkali halide pellets is the simplest and easiest to use and lends itself readily to quantitative work. It is not without pitfalls, as we discovered in some of our initial studies of mineral matter in coal (Painter et al., 1978, 1981a). The method essentially consists of grinding coal, usually in the presence of KBr, and pressing the resulting powder into a glassy pellet using an evacuated die. It should be noted that Estep et al. (1968) determined that both the sample and the KBr have to be ground in order to obtain maximum absorption. The way this is accomplished, however, depends on the hardness of the sample.

In initial work in our laboratory we ground ~ 1 mg of coal and 300 mg of KBr using a Perkin-Elmer Wig-L-Bug. This used to take about 20 min. We subsequently purchased a new Wig-L-Bug and did not initially appre-ciate that this was an improved model and a much more vicious instru-ment. [The stainless steel capsule would occasionally come free during grinding and ricochet around the laboratory, endangering the lives of graduate students and (more importantly) the physical integrity of our instruments.] We initially thought that we would probably still need about 20 min grinding time. When smoke started to rise from the grinder after about 5 min, it became obvious that a new grinding study was in order. The point of this anecdote is contained in Fig. 10; grinding instruments vary from laboratory to laboratory, so each should establish individual optimum conditions for sample preparation. We found that band intensi-

30 40 Time (sec)

50

Fig. 10 . Plot of the integrated absorption in the region 900-700 cm a coal sample versus grinding time in KBr pellet preparation.

60

in the spectrum of


ties for coal reached an optimum value after about 20 sec grinding time in our new Wig-L-Bug. However, this is not the end of the story. Grinding conditions are also sample dependent.

Most of our early work on coal was concerned with the analysis of mineral matter. We thought that we had established a routine and repro-ducible methodology, when it was pointed out to us by Elliott et al. (1984, discussed in Painter and Coleman, 1980) that hard materials, such as mineral quartz, are protected by the KBr if they are initially ground together. Accordingly, such materials should be preground, placed in the capsule with about 25 mg of KBr, and ground for the appropriate amount of time. (A small amount of KBr is necessary to prevent the sample from sticking to the sides of the capsule.) The remaining 275 mg of KBr are then added and ground for an additional period to obtain maximum mixing and dispersion. Unfortunately, this procedure cannot be applied to every sample. Soft minerals such as kaolinite agglomerate under these condi-tions and hence should be prepared by the older procedure, grinding in the presence of KBr.

A word about weighing is appropriate here. Because 200 or 300 mg of KBr are used for pellets, accurate weighing of the matrix material is not a problem in most laboratories. For coals, 1-3 mg of material are conven-tionally used, whereas for some minerals it is necessary to use 0.1-0.3 mg if the sample is not to overabsorb, pushing the intensities outside the range of the Beer-Lambert law. For good quantitative analysis an accu-rate microbalance is therefore a necessity. As we shall see, experimental errors have a major impact on the determination of coal structural param-eters, so there are no shortcuts. If accurate quantitative measurements are to be made, one must know precisely how much sample is in the pellet. Naturally, there will always be a loss of material (e.g., sample and KBr that stick to the side of the grinding capsule), but this can be ac-counted for by weighing the pellet after pressing and comparing this weight with the amount of material originally placed in the capsule (Painter et al., 1978, 1981a; Painter and Coleman, 1980).

We have discussed the importance of grinding for a sufficient period of time, but it is also important that the sample not be ground for too long a period! This is because KBr adsorbs water during grinding. Although it has occasionally been claimed that this adsorbed water can subsequently be completely removed by drying, this is emphatically not so. We have referenced elsewhere (Painter et al., 1981c) work that was performed in the 1960s to establish this point, notably that discussed by Friedel (1966). We will not reiterate these points, because a more definitive study has been reported by Likhtenshtein et al. (1980). Using thermogravimetric analysis, these authors demonstrated that the moisture content of coal


and, surprisingly, KBr was changed only slightly by separate vibromill-ing. In contrast, the moisture content of the mixture was changed signifi-cantly. (In other words, using a blank KBr pellet to subtract out the contribution of water to the spectrum of coal will not work.) This effect is concentration dependent and decreases with increasing coal content. The hygroscopic character of KBr alone, both before and after grinding, is simply too low to account for the intensity of the water bands in the spectra of coal-KBr mixtures. In such systems, vibromilling apparently leads to an interaction that reaches a maximum with concentrations of coal less than 1% and leads to the formation of a new phase. The sorption properties of this phase are substantially different from those of the summed individual components. Subsequent drying of the tablets does not eliminate the actual cause of the increased adsorption capacity of the pellets. A dried pellet takes up moisture very quickly to achieve the equilibrium state of this new phase under the given ambient conditions.

Another important point of this study is related to background scatter-ing. There is always a sloping background in the spectra of coals. On the basis of an examination of well-ground pellets under the microscope, it was surmised some years ago that this background was only partly due to particle scattering and that the wings of electronic absorption bands were also being observed. For anthracites this is a reasonable argument, be-cause one would expect that low-lying electronic energy levels in such highly aromatic materials would give absorption bands in the near ir. This seems less likely for lower-rank coals, and such an interpretation is even more suspect when one considers the spectra obtained by diffuse reflec-tance, illustrated in the following section. Both the amount of water ad-sorbed (by the KC1 in this case) and the background scatter are far less in these spectra. The work of Likhtenshtein et al. (1980) provides an an-swer. They point out that the interaction in mixtures of a low concentra-tion of coal in KBr leads to a disturbance of the uniformity of the matrix of the pellets during subsequent storage or heating. Infrared radiation is then scattered from the microdomains of the deformed tablet. This scattering effect and the enhanced absorption of water decrease and are minimized when the concentration of coal in a mixture is about 4-5%. This concen-tration (or higher) of coal in KBr (or KC1) is the amount routinely used in diffuse reflectance measurements.

The quantitative analysis of samples used in transmission studies is based on the Beer-Lambert law. This is usually written

A = abc (1)

where A is the intensity of the band of interest (integrated areas or peak heights can be used, but the units will change accordingly), a an absorp-


tion coefficient, b the thickness of the sample, and c the concentration of the material giving rise to the band absorption. This form of the Beer-Lambert law was derived for use with solutions. A more convenient expression for solid samples is

A = acW/S (2)

If A is a band area (absorbance units cm-1), then the units of the absorp-tion coefficient a are absorbance units cm-1 rng"1 cm-2. The term c is the weight percentage of the functional group of interest, W the weight of the sample in the pellet in milligrams (we routinely disperse the sample in 300 mg of KBr), and S the area of the pellet (cm2). In some texts the term S is omitted, presumably because 13-mm pellets are now routinely used by most laboratories.

We have found it convenient to rearrange this equation to c = eA (3)

where e is a "conversion factor," equivalent to S/aW. We have set up our equations in this form because it is far more convenient for the numerical procedures that have to be employed to obtain quantitative results in coal studies.

B. Diffuse Reflectance

A number of workers have presented ir spectra of coals obtained by diffuse reflectance measurements, but these have usually been presented as examples of the application of the technique. The only detailed study of a set of coal samples was reported by Fuller and Griffiths (1982), who were interested primarily in coal oxidation. Nevertheless, these authors also presented examples of the diffuse reflectance spectra of coals that are of considerable relevance to this part of our discussion. We shall assume a general familiarity with the optics of this attachment, which is now readily available from instrument manufacturers. In these studies coal samples are usually dispersed in KC1 (although spectra of undiluted powdered coals have been reported) and simply placed in a cup located so that incident light can be focused onto the powder and scattered light collected by suitably shaped mirrors. To the extent that the sample is not pressed into a pellet, this is simpler, but the advantages of "sample preparation" cited for this technique in the literature are not overwhelming. There are other, to our minds more important, advantages, but these are offset by problems pertaining to quantitative work.

The general theory for diffuse reflectance at scattering layers within powdered samples was developed by Kubelka and Munk (1931). It is similar in form to the Beer-Lambert law described earlier and (for an


"infinitely thick" layer) can be written as

/(A») = (1 - R^nRoo = kls (4) where R«> is the absolute reflectance of the layer, s a scattering coefficient, and k the molar absorption coefficient. Fuller and Griffiths (1978) pointed out that in practice a perfect diffuse reflection standard has not been found, and R^ is replaced by RL, where

, = RL (sample) 00 RL (standard) p ;

Here, RL (sample) is the measured reflectance of the sample, whereas RL (standard) is the measured reflectance of a selected nonabsorbing stand-ard. Either KC1 or KBr is most often used.

The Kubelka-Munk theory predicts a linear relationship between the molar absorption coefficient k and the peak value f(Roc) for each band, provided that s remains constant. The parameter k is related to the molar absorptivity a and the molar concentration c by

k = 2.303ac (6) for dilute samples in low-absorbing or nonabsorbing matrices. Hence,

f(RJ = (1 - R^/lRo, = elk' (7) where k' = s/2.303a. If the diffuse reflectance spectra are converted to the Kubelka-Munk function f(Ro°), they should therefore appear similar to absorbance spectra and, within limits, be used for quantitative analysis. As Fuller and Griffiths (1978) pointed out, however, for intense absorp-tion bands large deviations from linearity are observed, even at low con-centrations, and the spectra of neat materials may be considerably differ-ent from the spectra of the same material diluted in an alkali halide matrix.

In spite of these problems, quantitative measurements of band intensi-ties as a function of coal oxidation displayed consistent and progressive changes (Fuller et al., 1982). No attempt was made in this study to relate band intensities to the concentration of corresponding functional groups, the subject of interest here, but before proceeding we should examine the diffuse reflectance spectra of a range of coals. Those reported by Fuller et al. (1982) are reproduced in Fig. 11. The key point we wish to make is the advantage that diffuse reflectance has in minimizing scattering and the effects of water adsorption during sample preparation. Spectrum d in Fig. 11 is that of an anthracite, although the intensity of the aromatic CH stretching mode indicates that this sample has a higher hydrogen content than other coals of this rank. Nevertheless, we have obtained transmis-sion spectra of similar samples, and no matter how much we grind and


I600 IOOO 3400 2800 2200 cm"1

Fig. 1 1 . Diffuse reflectance spectra of (a) a lignite, (b) a subbituminous coal, (c) a high-volatile bituminous coal, and (d) an anthracite. The elemental carbon/hydrogen and oxy-gen/carbon ratios and percent mineral content are as follows: (a) H/C = 0.86; O/C = 0.21; mineral content, 15%. (b) H/C = 0.81; O/C = 0.17; mineral content, 10%; (c) H/C = 0.71; O/C = 0.04; mineral content, 13%. (d) H/C = 0.44; O/C = 0.02; mineral content, 7%.

attempt to minimize moisture adsorption we always obtain a massively sloping background and bands associated with water. This is presumably a result of the factors unearthed by Likhtenshtein et al. (1980) and dis-cussed in the preceding section. Diffuse reflectance has a clear advantage in obtaining the spectra of difficult, highly absorbing materials such as high-rank coals.

V. DETERMINATION OF OXYGEN-CONTAINING FUNCTIONAL GROUPS IN COAL

Until relatively recently, the quantitative determination of oxygen func-tionality in coal rested on what could be gleaned from elemental analysis


of the total oxygen present together with measurements of OH content, usually by acetylation reactions, and carboxylic acid content by ion-ex-change procedures. The elemental analysis of many coal samples has, in turn, relied on determining total oxygen by difference, a procedure that results in an accumulation of errors. Chemical methods of analysis are subject to errors from the potential lack of accessibility of reagents to functional groups buried in the interior of the coal macromolecular net-work. Infrared spectroscopy has, in the past, been most widely used to check the completeness of such chemical reactions. This is still an ex-tremely useful application of FT-IR, particularly because difference pro-cedures allow a more accurate estimation to be made of the extent of the appearance or disappearance of bands associated with particular func-tional groups. [The use of FT-IR in studies of coal oxidation is a good example (Painter et al., 1981b; Rhoads et al., 1983).] Here, however, our concern is with the quantitative determination of specific functional groups. We believe that it is now possible to obtain reasonably accurate numbers for the OH and COOH content of coals. Less reliable "ballpark" figures can be obtained for quinones and ethers.

A. Measurement of OH Groups

Most ir methods for the determination of OH groups in coal have relied on measurements of the intensity of the 3400 cm - 1 band. Osawa and Shih (1971) plotted the intensity of this band divided by the factor W/S, where W is the weight of coal in the KBr pellet and S the area of the pellet, against the OH content determined by acetylation methods (Fig. 12). (This normalized intensity they confusingly called a "specific extinction coefficient," which is not the same quantity labeled an extinction coeffi-cient in many ir texts.) It can be seen that a good straight-line correlation was obtained, but there is an intercept at positive values of ir intensity, presumably due to the presence of trace amounts of water in the pellet.

The problems associated with adsorption of water by the KBr-coal complex is the most significant difficulty with this method. Osawa and Shih (1971) went to great lengths to keep their disks as dry as possible during sample preparation, and the results indicate that they achieved consistent methodology. Transferring their correlation of peak intensity to OH content to results obtained in other laboratories is a more difficult problem, because sample preparation procedures and methods for esti-mating baselines and hence band intensities are bound to differ. When FT-IR was first applied to coal studies, however, there were no alterna-tives to this procedure, and Solomon applied this correlation in his initial studies of coal structure (Solomon, 1979). More recently, data for the OH


0.2 'S)

CN E o

0.1

0 5 10

Hydroxyl content (%daf)

Fig. 12. Relation between OH content and specific extinction coefficient at 3450 cm-1. Key: O, Japanese coals; · , foreign coals. From Osawa and Shih (1971) with permission.

content of coals as determined by radiochemical methods of analysis have become available (Yarzab et aL, 1979). This has allowed the relationship between ir band intensities and OH content to be determined for a com-mon set of coal samples. Solomon and Carangelo (1982) were thus able to establish their own correlation between band intensity and OH content. They compared this to data published by Osawa and Shih (1971) and noted some differences. These were most likely due to the difference in water content of pellets prepared in different laboratories and dried by different methods. As Solomon and Carangelo pointed out, there was some scatter in plots of the 3450 cm-1 peak intensity versus OH content, but a significantly better correlation was obtained by using the optical density at 3200 cm-1. This is shown in Fig. 13.

In this laboratory, OH content of coals has been measured by a differ-ent procedure. In part, this is because some of our early coal studies were aimed at determining changes occurring as a result of low-temperature oxidation. We therefore wished to avoid heating KBr pellets, which can result in small degrees of oxidation even under the low pressures obtained in vacuum ovens. We therefore sought to combine ir measurements with acetylation procedures, an idea first advanced by Durie and Sternhell


0.4

0.3

6 \

<£ 0.2 o o CVJ

ro

0.I

0 2 4 6 8

Oxygen in OH (wt%dmmf) Fig. 1 3 . Specific extinction coefficient for a number of coals at 3200 cm"' as a function

of oxygen concentration as OH in the coal. From Solomon et al. (1982) with permission.

(1959). Essentially, acetylation introduces strongly absorbing ester bands into the spectrum of coal:

O acetic II

Coal—OH > Coal—O—C—CH, anhydride

These have established group frequencies well separated from bands associated with water. Naturally, the accuracy of this procedure depends on the completeness of the reaction, so we examined the spectra of dried KBr pellets of a number of fresh and even highly oxidized coals (Rhoads et al.y 1983), where cross-linking might make "buried" OH groups less accessible. A typical result is shown in Fig. 14, where the spectrum of an original coal is compared with that of its acetylated product. Each spec-trum has been adjusted so that the aliphatic CH stretching mode near 2920 cm- 1 is plotted to full scale. An exception to this plotting mode is an inset showing the spectrum of a blank KBr pellet. This spectrum was plotted on the same absolute absorption scale as the spectrum of the original coal. Despite extensive drying (2 days at 120°C under vacuum) there is clearly residual absorption near 3400 cm - 1 in the spectrum of the KBr that can be assigned to water. There is an equivalent residual absorption in the spec-trum of the acetylated sample, indicating that the acetylation is as com-plete as the sensitivity of ir measurements can determine.


3800 2700

cm"' Fig. 14. (A) FT-IR spectrum of an Illinois no. 6 coal, dried 2 days at 120°C.

(B) Spectrum of the same coal after acetylation. Inset: spectrum of a blank KBr pellet (after drying).

The rest of the spectrum of a typical acetylated coal is illustrated in Fig. 15. The difficulties encountered by Durie and Sternhell (1959) are clearly illustrated in this figure. The bands due to the acetyl group overlap those of the original coal and are thus not easily measured. It is now a straight-forward task to isolate the bands due to the acetyl group by simply sub-tracting the spectrum of the original coal (Fig. 15).

When we initially studied this method, we found to our surprise that the carbonyl stretching region of the difference spectrum was a composite of a number of bands. After some false starts and difficulties [those readers concerned with the problems of curve resolving should consult our origi-nal paper (Painter et al., 1981c)], we developed a procedure for curve fitting this region of the spectrum. This relies on derivative methods to define the number of peaks that contribute to a spectra profile and give an initial estimate of peak positions and width at half-height. These parame-ters are used as input to a program that least-squares-fits the defined bands


I i I | I I I | I I I | I 1 I I 1 I I I I I I I , ,

2000 1800 1600 1400 1200 1000 800

Fig. 15. (A) FT-IR spectrum of an acetylated HVC Arizona coal (PSOC 312). (B) FT-IR spectrum of the original coal. (C) Difference spectrum.

to the observed spectral profile. A typical difference spectrum and its second-derivative profile are shown in Fig. 16, and the resulting curve-resolved bands are shown in Fig. 17. The bands near 1770, 1740, and 1670 c m 1 can be assigned to acetylated phenolic OH, alkyl OH, and NH groups, respectively. The 1710 cm-1 band is due to residual acetic acid. The band shape is defined as a sum of Gaussian and Lorentzian functions in the proportion/to (1 - / ) , with the fractional parameter/being deter-mined during the least squares fit.

In initial work we calibrated the intensities of the ir bands to the total concentration of OH groups, measured as weight percentage of oxygen as OH, by using the results of radiochemical analysis of the same coals (Yarzab et al., 1979). Unfortunately, these data cannot be used to deter-mine individual extinction coefficients for the 1770, 1740, and 1670 cm-1

bands because of a practically linear relationship between their intensities as a function of coal rank. Thus, although the absolute intensities of the acetyl bands decrease with increasing rank of the coals, surprisingly, the ratio of the intensities (e.g., 1740 cm_1/1770 cm-1) does not change signifi-cantly from coal to coal.

Bellamy (1975) noted that extinction coefficients for the carbonyl stretching mode of various groups of esters are remarkably consistent. Consequently, we used data from model compounds to obtain these pa-rameters. The total OH content of the coal so determined was then com-

I 1 1 1 1900 1800 1700 1600

cm Fig. 16. Bottom: difference spectrum between 1900 and 1550 c m 1 obtained from an

acetylated lignite. Top: second derivative of the spectrum.

1900 1800 1700 1600 cm

Fig. 17 . Resolution of the difference spectrum obtained from the acetylated lignite in Fig. 16 into six bands.


TABLE 2

Conversion Factors for Acetylated Coals3

Group e

Phenolic ester 0.217 Alkyl ester 0.176 Amide 0.323

a Model compounds used: 1-naphthyl acetate, acetylated anthraflavic acid, Ai-acetyl-L-trypto-phan ethyl ester, acetylated 9-anthracenemen-thanol.

pared with the results of chemical and 13C-NMR analysis in order to check the accuracy of our results (Snyder et ai, 1983). We anticipated some differences between the extinction coefficients for alkyl and phenolic es-ters, but on the basis of work reported in the literature values should be reasonably similar. Extinction coefficients for the amide group are much more variable. However, NH groups in coal are a minor component, so that an estimate is all that we can expect, considering the other errors inherent in measuring the intensity of a weak, curve-resolved band.

Model compounds were initially chosen according to our judgment of their similarity to units thought to occur in coal. Unfortunately, only a limited number of such materials are solid at room temperature, a prereq-uisite for the determination of conversion factors appropriate for materi-als that have to be dispersed in an alkali halide matrix. The models used and the conversion factors e determined are summarized in Table 2. The conversion factors are based on the definitions given in Eq. (3) (Sec-tion IV).

Conversion factors for phenolic and alkyl esters and amide groups hav-ing been determined, there remains the problem of applying these factors to coal. The quantity we wish to determine is the percentage of oxygen as OH in the original (unacetylated) coal. Consequently, the ir band intensi-ties have to be corrected by a weight factor that is proportional to the degree of acetylation. This is not the only correction. The intensities in the spectrum of the acetylated coal are first normalized to the equivalent of 1 mg of total material and then corrected for the amount of mineral matter by multiplying by 1/(1 - MM), where MM is the fraction mineral matter present in the coal. This normalizes the intensities to the equiva-lent of 1 mg of organic material. The weight percentage of oxygen as alkyl and phenolic OH and nitrogen as NH can then be determined from the intensities of the 1770, 1740, and 1670 cm-1 bands using the equation


o, OH IeA/43 1 - /e (8)

where / is the normalized intensity of the band under consideration (de-fined as a peak height, although band areas work equally well), e the corresponding conversion factor (from Table 2), and A the atomic weight of the atom to which the acetyl group is attached (16 or 14).

The results of the FT-IR determinations were compared with those obtained by 13C-NMR and radiochemical analysis of the same samples, as shown in Table 3. The results of the chemical and 13C-NMR analysis are also individually plotted against the total percentage of oxygen as OH (and nitrogen as NH) as determined by FT-IR in Fig. 18. It can be seen that there is good agreement between the FT-IR and chemical methods for practically all of the samples. The discrepancy between the FT-IR and 13C-NMR measurements for four coal samples can be readily explained. The samples are of low rank, and the ir spectrum of the unacetylated coals, shown for the region 2000-1400 cm-1 in Fig. 19, demonstrates that these materials have an appreciable initial concentration of carboxyl groups. This naturally results in an overestimation of the percentage of

12

10

τ 6 o o ° /

/

A /

/

/ /

A

y a /

■4 A O

2 4 6 8 10 O as OH, F T - I R (% dmmf coal)

Fig. 18 . Plot of percent oxygen as OH as determined by chemical and NMR methods versus percent oxygen as OH as determined by FT-IR. [O, chemical (Sobkowiak et al., 1984); Δ, NMR (Zürn et al., 1981)].


TABLE 3

Calculation of Oxygen as Hydroxyl (% dmmf Coal)

PSOC«

241 230 231 315 312 212

272 287 276 284 306 268 301 236

ASTM class

Subbituminous B Subbituminous B Subbituminous B HVC HVC HVC

HVB HVB HVA HVA HVA HVA HVA Medium volatile

Carbon (%)

73.9 76.3 76.6 76.6 78.0 79.3

82.1 82.2 83.5 83.7 83.8 86.3 88.2 90.4

Chemical6

6.4 6.5 6.4 6.1 5.0 5.8

4.7 5.3 4.2 4.1 3.4 2.5 2.9 1.0

FT-IR

7.9 6.5 5.7 5.8 4.1 5.6

4.1 6.0 4.6 4.3 5.3 2.8 2.8 0.8

13C NMR

Raw data

8.4 10.9 10.0 9.2

NOd

9.7

3.7 5.6 3.7 3.6 4.0 2.1 3.4 1.5

Corrected0

5.8 7.1 8.1 5.2

NOd

7.5

— — — — — — —

a Designation of the Pennsylvania State University Coal Data Base. b Sobkowiak et al. (1984). ( Corrections were made using approximations for intensity in the l3C-NMR data due

to carboxylic acids in the original coals. d ND, Not determined.

Fig. 19 . Scale-expanded FT-IR spec-tra in the range 2000-1400 cm"1 of five coals of high oxygen content.

I 2000 1400


oxygen as OH as determined by ,3C NMR. The relatively low concentra-tions of such groups in the original sample make measurements difficult. Corrections were made accordingly, giving good agreement among all three sets of measurements, as shown in Table 3.

It is well known that the total percentage of oxygen as OH in a coal varies systematically as a function of rank. If we now consider the indi-vidual concentrations of phenolic and alkyl OH as determined by FT-IR, plotted as a function of the percentage of carbon in the original coal in Fig. 20, we can see that these individual components also vary in a corre-spondingly systematic fashion. We believe that this is an important result. It has previously been assumed that OH groups in unoxidized coals are almost exclusively phenolic. These results not only demonstrate the presence of alkyl OH groups, but show that the relative proportions of

85 90

Fig. 20.

75 80 % Carbon

Plot of percent oxygen as phenolic OH ( · ) , percent oxygen as alcoholic OH (Δ), and percent nitrogen as NH (O) versus carbon content for a range of coals.


phenolic and alkyl OH are approximately the same, both decreasing sys-tematically as a function of increasing coal rank.

B. Determination of COOH Groups

Low-rank coals, those in the lignite and subbituminous range, usually display bands near 1700 cm-1 associated with carboxylic acid groups. Furthermore, bands associated with these groups also appear in the spec-tra of oxidized coals. The best procedure available for the quantitative determination of COOH content before the introduction of FT-IR was the ion-exchange method described by Shafer (1979). As we shall see, how-ever, the conditions of this procedure led to incomplete exchange and values of COOH content that were too low. The value of applying FT-IR to such problems is that the extent of a reaction can be estimated in addition to the proportion of a particular functional group (Starsinic et al., 1984). This is not to say that FT-IR methods are without problems. There is a reliance on curve-resolving procedures, which could introduce errors. Nevertheless, they are probably the best that can be achieved at this point and certainly give reasonable values that are unbiased by incomplete exchange.

The FT-IR procedure is not a new method but simply a combination of Shaffer's approach and the FT-IR measurements. It is illustrated in Fig. 21, where the spectrum of a demineralized lignite is compared with that of a Ba2+-loaded sample. The difference spectrum obtained by spectral sub-traction (on a 1 : 1 weight basis) is shown in the same figure. It can be seen

2000 cm·1 500

Fig. 2 1 . FT-IR spectra between 2000 and 5000 cm '. (A) Ba2+-Loaded lignite; (B) demineralized lignite; (A - B) difference spectrum.


that the 1600 cm - 1 region of the spectrum broadens on exchange. This is due to a shift of the mode near 1700 cm- 1 to —1565 c m 1 . This change can be more clearly seen in the difference spectrum, where positive and nega-tive (above and below the baseline) bands are clearly resolved. Similar results were obtained for four other cation-loaded samples, and the as-signments of these spectral bands is well established. The 1710 cm- 1 band is due to COOH groups, whereas the 1564 cm"1 vibration can be assigned to structures of the type COO M+. These differences demonstrate the ca-pacity of FT-IR to detect qualitative changes in the coal upon exchange. In order to obtain quantitative results, however, it is necessary to sepa-rate the overlapping contributions of bands in this spectral region. This was accomplished by the use of least squares curve-fitting procedures.

Second-dervative methods were again employed in an attempt to iden-tify the bands in this region of the spectrum. Because of the considerable overlap of broad bands, however, some modes were identified in what can only be described as an uncertain fashion by shoulders on the second-derivative profile. In coals of higher rank, however, similar bands were much more clearly seen and were identified with certainty. In part, our curve fitting was based on this previous experience, but in any event the principal aim was to separate the overlapping contributions of the bands near 1700 and 1600 c m 1 in order to measure the intensity of the former. These bands are relatively well separated, so that the area and peak height of the curve-fitted 1710 cm- 1 can be determined with some confidence. Unfortunately, this confidence does not extend to the COO - band curve-fitted at 1564 c m 1 , where overlap with a numbr of other poorly identified bands can lead to significant errors in intensity measurements (Starsinic et al.t 1984).

A total of eight bands were curve-fitted to the spectral profiles shown in Figs. 22 and 23. After refined values of the band-shape factor, band inten-sities, widths at half-height, and locations were obtained, a second com-puter program was used to plot such bands, as also illustrated in these figures. Measurements of peak heights for all the exchanged samples, normalized to the equivalent of 1 mg of dry demineralized coal, are sum-marized in Table 4. There is a dramatic increase in intensity of the 1565 cm- 1 band on exchange. This is accompanied by a decrease in intensity and an apparent shift in the COOH band from 1710 to —1705 cm- 1.

The first surprise concerning the data shown in Table 4 is that a band near 1705 c m 1 remained so intense in the Ba2+-exchanged sample, for which exchange reactions are assumed to be complete. It was demon-strated that this band is not due to other functional groups but to unex-changed COOH groups, on the basis of further observation of exchange upon soaking in NaOH solutions (Starsinic et al., 1984). A very weak


TABLE 4

Normalized Peak Heights (Absorbance Units) from Curve-Fitted Spectra

Sample

Demineralized Ba2+-Loaded K+-Loaded Na+-Loaded Mg2+-Loaded Ca2+-Loaded Ba2+-Loaded per 24 hr

NaOH soak

mEq COOH/g demineralized

coal·1

2.10 1.53 1.61 2.06 2.04 —

1710 cm"1

0.39 0.20 0.31 0.26 0.17 0.25 0.13

1622 c m 1

0.52 0.59 0.50 0.52 0.50 0.56 0.56

1564 c m 1

0.02 0.49 0.25 0.24 0.44 0.52 0.57

a From Jones (1969a).

residual absorption near 1699 cm-1 was observed in the spectra of these NaOH-soaked samples, however, and this was assigned to other carbonyl groups. The latter band had to be accounted for when the intensity of the 1700 cm-1 band was related to COOH content. For the Texas lignite used in this study, it was determined (using Schäfer's method) that the concen-

Fig. 22. Bottom: FT-1R spectrum between 1850 and 1500 cm ' of deminer-alized Texas lignite and component bands from curve resolving. Top: second derivative of spectrum.

1495

1850 1500


I850 I500

Fig. 23. Bottom: FT-IR spectrum between 1850 and 1500 cm 1 of Ba2+-loaded lignite and component bands from curve resolving. Top: second derivative of spectrum.

tration of oxygen present as COOH groups is 6.72%. FT-IR shows that the exchange is incomplete, and therefore this number is too low. It can, however, be directly related to the difference in the intensity of the 1710 cm-1 band between the spectra of the demineralized original lignite and the barium-exchanged sample. In addition, because a difference is used, the contribution of the residual 1699 cm"1 band in the spectrum of the NaOH-soaked sample is subtracted out. Consequently, one can deter-mine an absorption coefficient (or, more accurately in terms of the defini-tions used here, a conversion factor) relating band intensity to concentra-tion of COOH groups, which in turn should allow a true (or at least a more representative) measure of COOH groups. This calculation yields values of 9.2% oxygen as COOH, significantly higher than the 6.72% value ob-tained chemically, suggesting that acetate exchange is incomplete.

C. Appoximate Distribution of Oxygen-Containing Functional Groups in a Set of Vitrinites

In addition to OH groups and, in low-rank coals, COOH groups, coals contain oxygen in quinones, heterocyclic rings, and ether linkages. Be-cause the vibrations of these groups contribute to the spectrum as highly


75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 % C (dmmf)

Fig. 24. Percentage of oxygen present as phenolic OH (D) and alkyl OH (O) in a set of vitrinite concentrates.

coupled modes, they are not easy to measure by ir techniques or, as it turns out, any other method. We attempted to obtain an estimate of the relative proportions of these groups for a set of vitrinite concentrates by difference, using the total oxygen content determined by elemental analy-sis and the proportions of other groups measured by FT-IR. This was not by any means definitive, but if nothing else provided a ballpark figure for the distribution of these groups.

All the samples used in this study were obtained from fresh bituminous coals and thus displayed no evidence of COOH groups or other nonconju-gated carbonyls. The total percentage of oxygen as OH groups was deter-mined by the acetylation procedures described earlier and are plotted as a function of the carbon contents of the samples in Fig. 24. The trend of decreasing OH content with increasing rank (carbon content) is similar to results obtained by other workers.

The measurement of conjugated carbonyl groups in coals was made possible by the deconvolution procedure for the 1600 cm-1 region of the spectrum described in Section III.B. Individual bands assigned to hydro-gen-bonded and non-hydrogen-bonded quinone carbonyl groups (in higher-rank coals) may be detected around 1650 and 1670 cm-1, respec-tively. It must be pointed out that dried KBr pellets could not be used in this portion of the quantitative FT-IR work. On drying, the intensity of the 1650 cm-1 quinone band increased dramatically in some spectra, in a few cases to twice the intensity of a nondried sample spectrum. This was attributed to oxidation and/or some type of internal chemical rearrange-ment. Thus, all 1650 and 1670 cm-1 intensity measurements were made on spectra of nondried KBr pellets and do run some risk of interference from OH bending modes of water in the KBr. The intensity measurements were calibrated using two model compounds of known quinone carbonyl


concentration, benz[>y]anthracene-7,l2-dione and benzanthrone. Pellets were made of various sample weights for these compounds and a "con-version factor" calculated (see definition in Section IV.A). Values for the benz|>]anthracene-7,12-dione and benzanthrone were 3. II and 4.08, re-spectively. Because values for the vitrinites could be anticipated to be somewhere between these two, conversion factors were averaged, with a value of 3.6 used for coal spectral measurements. It is not desirable to average conversion factors, because fairly large errors will be introduced. If nothing else, however, a relative measure of quinones and trends as a function of rank can be gained. The values do not show a large range, from 0.1 to 0.4 wt % oxygen as quinone, nor does there seem to be a rank correlation.

Quinone bands in the coal spectrum are fairly weak compared with neighboring aromatic bands. Thus, there are probably considerable errors in intensities. Nevertheless, even by assuming a uniform contribution to all spectra by KBr-bound water and taking into account errors introduced by averaging conversion factors, FT-IR offers at least an estimate of conjugated carbonyl concentrations. Furthermore, because this number is small, the percentage of oxygen in ethers and heterocyclic groups can be

Fig. 25. Estimate of the distribution of oxygen-containing functional groups in a set of vitrinite concentrates.


obtained by subtracion of OH and conjugated carbonyl oxygen from the total oxygen content. This value must be strictly regarded as an estimate, because the total oxygen content is determined by difference and obvi-ously already has accumulated errors. Errors is measuring OH and quinone oxygen will now contribute as well! We would reiterate, how-ever, that there is presently no other method for estimating these groups.

The results of this analysis are shown in Fig. 25. This plot indicates that, for the vitrinites studied, at the lower end of the scale over half of the oxygen is present as OH. This amount decreases gradually until at 90% carbon most oxygen is present as ether and heterocyclic groups with some quinones. Ethers and heterocyclics show a decrease in weight per-centage present with increasing rank.

VI. DETERMINATION OF ALIPHATIC AND AROMATIC CH GROUPS IN COAL

As pointed out in our discussion of various models proposed for the structure of coal, the aliphatic and aromatic CH content is a key parame-ter. In addition, the type and distribution of CH groups clearly affect many chemical properties, so the development of reasonably accurate methods for the measurement of the distribution of these groups is essential. Un-fortunately, a range of values is scattered throughout the literature, and there are substantial disagreements and some controversy. Having done our fair share to add to this rumpus, we must preface this section with a "consumer warning." We can attempt to be objective but will naturally be prejudiced by our experiences in adapting methodologies. (We cannot claim to have been original in developing any particular method in all of our work. Each time we think that we have found a new approach we find a paper from the 1960s expounding it all in great detail.) The concerned reader is urged to consult the original papers describing work in this area and make his or her own judgment.

It is our considered opinion that anyone commencing work on the ir spectra of coals should be forced (at gunpoint, if necessary) to read Brown's seminal paper on this subject (Brown, 1955). Many of the issues that are still of concern (background scatter, band assignments, etc.) were addressed in this paper. In addition, Brown demonstrated the variation in the aromatic and aliphatic CH content as a function of rank and deter-mined values for the ratio of these quantities that were widely used in developing structural models. The spectra he had to work with were not of high quality, as illustrated in Fig. 26. Furthermore, samples were pre-pared as Nujol mulls, a technique that does not readily lend itself to the quantitative determination of individual functional groups. What can be


Λ M.7%C

89.3%C

9/.4%C

?*5%C

7«/%C

?* /#C

_ / V ^ I

^ \ \ n . . \ H . . VJ , , \N

i*00 2600 1600 MOO /OOO 3400 2600 /SOO MOO /OOO Wat/* rwmStr

Fig. 26. Infrared spectra of a set of coals studied by Brown (1955). Key: (—) Nujol absorption bands; (····) uncertain intensity. Reproduced with permission.

determined is the ratio of aromatic and aliphatic CH content from the ratio of the intensities of the appropriate bands. An advantage of this method is that it does not require a knowledge of sample weights and correction factors for the mineral matter and moisture contents of the coals. These factors cancel when a ratio of intensities is considered. We have tried to take advantage of this in our latest work, as we discuss below.

Brown considered the ratio of the peak heights at 3030 and -2925 cm-1

as a measure of aromatic versus aliphatic CH content. The data of Brown together with data obtained by Fujii et al. (1970) for a set of Japanese coals are shown in Fig. 27. Surprisingly, these data show very little scat-ter. If the peak heights of the individual modes are examined, however, (Fig. 28), there is a much wider variation (the FT-IR data we present below, although based on integrated intensities rather than peak heights, show similar trends). The aromatic CH/aliphatic CH ratio increases only slightly with rank until coals with a carbon content of —87% are exam-ined, but after this point there is a dramatic increase.

1.5

80 95 85 90 Carbon content %(daf)

Fig. 27. Relation between the carbon content and the ratio of the specific extinction coefficient at 3030 cm-1 (A'3030) to that at 2920 cm-1 (A2920)· Key: O, Japanese coals; · , foreign coals; A, foreign coals (Brown, 1955). From Fujii et al. (1970) with permission.

0.6 u.o

0.5

0.4

0.3

0.2

0.1

0.0

~ (a)

- 6

-

0

0

I

• t

1

To \#

1 1 70

0.07

0.06

0.05

0.04

0.03

0.02 L

0.01 L

(b)

J 75 80 85 90 95 80 85 90 95

Carbon content %(daf) Carbon content %(daf)

Fig. 28. (a) Relation between carbon content and specific extinction coefficient at 2920 cm-1. Key: O, Japanese coals; · , foreign coals, (b) Relation between carbon content and specific extinction coefficient at 3030 cm Fujii et al. (1970) with permission.

Key: O, Japanese coals; · , foreign coals. From


Whether band areas or peak heights are used, a knowledge of the ab-sorption coefficients (or ''conversion factors") relating band height or area to the concentration of corresponding functional groups is required. Furthermore, in coal studies we are essentially considering an average absorption coefficient, thus assuming that the proportions of the various functionalities that contribute to the aromatic and aliphatic peaks do not vary much from coal to coal, although the total aliphatic and aromatic CH content obviously changes considerably with rank. To the extent that the proportions of various groups change with rank, we would anticipate a corresponding systematic variation in the average absorption coefficients. Brown (1955) assumed that the ratio of the absorption coefficients was more or less invariant with rank. He determined from model compounds an average value of this quantity, «3030/02925» of 0.5. (In other words, the ratio of the band intensities has to be multiplied by a factor of 2 [see Eqs. (2) and (3), Section IV.A] to obtain the corresponding aromatic hydrogen/ aliphatic hydrogen ratio, ΗΆΓ/Η.ά].) The values obtained from model com-pounds ranged from 0.3 to 1.0. Nevertheless, the values for ΗάγΐΗά\ ob-tained by Brown cannot be casually dismissed. In a later study of soluble coal products, good agreement between results obtained using ir and !H NMR was obtained.

Other important studies of the aromatic and aliphatic CH content were reported by Tschamler and de Ruiter (1962), Durig et al. (1966), Ret-cofsky and Friedel (1968), and Retcofsky (1977). These studies are signifi-cant for the methodologies they describe, procedures that have subse-quently been adapted in FT-IR studies. For example, Durie and co-work-ers (1966) obtained soluble extracts from a set of Australian vitrains and determined the ratio of absorption coefficients by calibrating with the values of Hdr/Hd\ determined by Ή-NMR measurements. The assumption was then made that there is a correspondence in the structure of the extractable material and the coal macromolecular network, so that this ratio can be applied to the parent material. This paper is also important because it considered the measurement of band areas in addition to peak heights and used the region 900-700 c m 1 , characteristic of aromatic CH out-of-plane bending modes.

Tschamer and de Ruiter (1962) attempted to determine the distribution of aromatic and aliphatic CH groups by using the data on hydrogen con-tent of coals determined by elemental analysis. If the hydrogen in COOH groups is neglected (it is small and relevant only to low-rank or oxidized coals), we can write

ΗΎ = HÖH + Har + //al (9)

where Ητ is the total weight percentage of hydrogen in the coal deter-mined by elemental analysis, and H0n, Har, and //ai are the weight per-


centage of hydrogen in OH, aromatic CH, and aliphatic CH groups, re-spectively. This method was used by Solomon (1979) and Solomon et al. (1982) in their FT-IR studies of coal structure, and we now turn our attention to this work.

Solomon first adjusted his spectra to account for background scatter and mineral content (Fig. 29). Each spectrum was then fitted to a set of 26 Gaussian bands, the individual widths and positions of which were held constant (Fig. 30). It is our opinion that these procedures are flawed, and we have presented our arguments in the literature (Painter et al., 1981c). A review chapter such as this is not the proper place for a one-sided presen-tation of views, however, and the concerned reader should consult the original papers. Of more relevance to this discussion is the method used

(a) 140-

120-

1.00-

0 0.80-σ -O 1 0.60-<

0.40-

0 .20-

0.80

a,0.60 o c σ _o § 0 . 4 0 <

0.20

4000 3600 3200 2800 2400 2000 1600 1200 800 400 Wavenumber

Fig. 29 . Correction of coal spectrum for scattering and minerals, (a) Correction of coal spectrum; (b) determination of mineral spectrum by addition of reference spectra. The ab-sorbance is normalized to a sample size of 1 mg/cm2 in the KBr disk. From Solomon (1979) with permission.

(b)

Mineral spectrum 6 8%

Kaolin 18%

11lite 5.0%


SYNTHESIS

PSOC 170 COAL

ALIPHATIC C-H

AROMATIC C-H -£^_

HYDROXYL O-H

ETHER C - O - C . C - 0

C A R B O N Y L C = 0

4000 3600 3200 2800 2400 2000 1600 1200 800 400 Wavenumber

Fig. 30 . Synthesis of infrared spectrum. From Solomon (1979) with permission.

to determine absorption coefficients, defined in terms of conversion fac-tors [see Eq. (3), Section IV.A). Values of H0n were determined by methods described in the previous section, so that the equation could be rearranged to give

ΗΎ — HOH — Η^ + ΗΆ\

Then from Eq. (3) we can write

" a r ~ € a r A a r

Hd\ = €a |Aai

(10)

(11)

(12)


where ear and eai are the conversion factors for the aromatic and aliphatic bands, respectively, the areas of which are given by Aar and Aaj. Solomon used the aliphatic stretching modes between 3000 and 2700 for Aa| and the region 900-700 cm - 1 to measure Aar. Equation (10) can now be written as

HT ~ How = 6arAar + 6a,Aa, (13)

and rearranged to give

\Hj — / /OFT \ / / χ — HQ\\J

A plot of the qualities in parentheses allows the values of eai and ear to be determined from intercepts (Fig. 31). Data from model compounds and various coal reaction products were initially included to extend the range of the data (Solomon, 1979). Coal data alone give a "clump" of points through which a number of lines can be drawn with almost equal facility. In more recent work (Solomon et al., 1982), data from coals and coal products alone were used to give the results shown in Figs. 32 and 33. It is interesting that values of ear and eai for low-rank coals were different from those for bituminous samples. We shall compare the values of the conver-sion factors obtained by Solomon and co-workers with those obtained in this laboratory.

We have used a variation on the approach used by Tshamler and de Ruiter (1962) Solomon (1979), and Solomon et al. (1982) in our work, combining this with an initial study of a set of coal extracts in an attempt to validate the accuracy of our procedures (Sobkowiak et al., 1984; Ries-ser et al., 1984). Equation (13) shows that for each coal spectrum we have two unknown quantities, the conversion factors ear and eai. If the struc-ture of a set of coal samples were therefore sufficiently similar, ear and ea| would be the same, and the data could be obtained by the solution of a set of simultaneous equations. We applied this methodology to a set of coal extracts, where the values of ear and eai were independently determined by 'H-NMR measurements (Sobkowiak et al., 1984). Consistent results were obtained, provided that extracts from coals of broadly the same ASTM rank class were considered. These results are presented in Table 5. Be-fore we consider the results of applying this methodology to coal and vitrinite samples, however, it is important to discuss which ir bands were used and how their areas were measured.

We obtained spectra of a wide range of coal samples and vitrinite con-centrates, principally because we thought that a large data base would significantly help us to obtain precise solutions to Eq. (13). Although this was of some help other problems were more acute. Typical examples of coal spectra, chosen so as to encompass the range of rank of the sample

900 r-

800

700 φ-

^ 600 >-χ ο CC a > ι

X I

500 h

< 400

300

400 800 1200 A(ABCDE) l (HTOTAL_H HYDROXYlJ

Fig. 3 1 . Calibration of aromatic and aliphatic absorption peaks. Letters and numbers designate coals; circles designate chars; squares, tars; diamonds, model compounds. Key: 1, dihydroanthracene; 2, methylanthracene; 3, 9-methylanthracene; 4, 9,10-dimethylanthra-cene; 5, triphenylmethane; 6, 2-hexadecanol; 7, jY-eicosone; 8, tetradecanone; @ , averages for 15 aromatic compounds (see Table 2). From Solomon (1979) with permission.

800

A(al)/[H(total)-H(hydroxyl)]

Fig. 32 . Regression analysis to deter-mine aromatic (ar) and aliphatic (al) absorp-tivities; bituminous coals and products. From Solomon et al. (1982) with permission.


TABLE 5

Conversion Factors for Extracts

Parent coal

833 785 866 594 739 592 821 339 338 815 801

Rank

Lignite Subbituminous C Subbituminous A HVC HVB HVB HVA HVA HVA HVA Medium volatile

Conversion factor for aliphat ic modes x

0.105 0.122 0.133 0.249 0.214 0.221 0.200 0.190 0.171 0.181 0.162

102 Conversion factor for aromatic modes x 102

(0.343) (1.804) (1.573) 1.150 0.682 0.568 0.642 0.643 0.610 0.657 0.605

set, are illustrated in Fig. 34. These samples contain relatively small pro-portions of mineral matter (—10%); nevertheless, they are sufficient to introduce two problems. First, the mineral bands overlap the aromatic hydrogen out-of-plane bending modes between 900 and 700 cm-1. This problem can be handled by successive subtraction of the spectra of the individual mineral components, but we preferred, whenever possible, to subtract the spectrum of the appropriate low-temperature ash (LTA).

600 ,_

200 400 600 800

A(al) /[(H(total) - H(gydroxyl)]

Fig. 33. Regression analysis to determine aromatic (ar) and aliphatic (al) absorptivi-ties; lignite and subbituminous coals and products. From Solomon et al. (1982) with permis-


I—i—i—i—i—r—i—i—|—r—ι—ι—|—i—i—ι—|—ι—ι ι—| ι ι ι \ ι ι ι |—ι—ι ι—[—η 3800 3400 3000 2600 2200 1800 1400 1000 600

Fig. 34. FT-IR spectra of three coal samples. (A) Subbituminous coal, PSOC 785 (73.21% C); (B) high-volatile bituminous C coal, PSOC 592 (83.42% C); (C) medium-volatile bituminous coal, PSOC 801 (88.34% C).

This eliminates difficulties that can arise from using mineral standards that are not precisely the same as the species found in the coal. A more important reason for resorting to this procedure, however, is related to the second problem introduced by the presence of minerals. For quantita-tive purposes we wish to determine appropriate ir band intensities per milligram of coal organic material. Consequently, the spectra have to be corrected for the moisture and mineral matter contents. These data have been determined and tabulated for these samples, but therein lies the rub. Such determinations are made on much larger sample sizes than the 1-3 mg used for ir analysis. Accordingly, even though the samples are pre-ground in order to obtain optimum homogeneity, the use of small samples can produce errors due to local variations in the distribution of minerals. The mineral matter content can in principle be determined from the sub-traction parameters required to eliminate the mineral bands, but this pro-cedure will not account for pyrite (which has no bands in the mid-ir region). It is far more accurate to prepare an LTA that is representative of all the major minerals present and subtract its spectrum from that of the coal (Fig. 35). This not only reveals some of the previously ''overlapped" organic modes, but provides a measure of mineral content. Unfortu-nately, this procedure is not a comprehensive panacea. In coals with a significant amount of carboxylate groups, organic sulfur and organic ni-trogen can be fixed as inorganic sulfate and nitrate, resulting in extrane-


1030 U0I0

T T 2000 1800 1600 1400 1200 1000 800 600

cm1

Fig. 35. Subtraction of mineral matter bands from the FT-IR spectra of coals. (A) Original coal spectrum (PSOC 680). (B) Low-temperature ash of this coal. (C) Differ-ence spectrum.

ous bands. Largely because of this factor, LTAs of under half of our coal samples were prepared. For most of these samples the mineral content determined by subtraction of the LTAs corresponded to the mineral con-tent listed in the coal data base. Not surprisingly, there were one or two major discrepancies. This is of critical importance in the determination of conversion factors, as we discuss below. Before we consider these calcu-lations, however, it is important to discuss certain regions of the coal spectra in more detail. Our concern centers on the choice of appropriate bands for measuring aromatic and aliphatic CH together with the most accurate measure of their intensities.

The scale-expanded region 900-700 cm"1 of the spectra of three sam-ples, PSOC 680 (80.6% carbon), PSOC 808 (84.7% carbon), and (88.4% carbon), is shown in Fig. 36. As in most coals, there are three principal bands, near 870, 815, and 750 cm 1 , that can be clearly assigned to aro-matic CH out-of-plane bending modes. It should be noted that there are frequency shifts as a function of rank. The mode characteristic of lone CH groups near 870 cm - ! in PSOC 801 shifts to lower frequency in coals with a lower carbon content. Furthermore, by second-derivative methods it can be shown that several bands contribute to this region of the spectrum


,—i 1 1 1 1—i 1 900 850 800 750 700 '

Fig. 36. Comparison of the out-of-plane bending modes of three coals. (A) High-volatile bituminous C (PSOC 680). (B) High-volatile bituminous A (PSOC 808). (C) Medium-volatile bituminous (PSOC 801).

(Kuehn et al., 1982). Consequently, we believe that there are problems associated with procedures where this region of the spectrum is curve-fit to just three bands the frequency and half-width of which are held con-stant. Unfortunately, it is not possible to apply our previously developed curve-fitting methods (Painter et al., 1981c) accurately to this region of the coal spectra, because of the increased noise level and slight band distortions produced by subtraction of the LTA spectra. In the spectra of vitrinite concentrates, however, additional bands are more clearly seen and can also be identified by derivative methods (Kuehn et al., 1982).

We reported that curve-fitted bands near 830 and 785 cm- 1 in the spec-tra of vitrinites showed a trend as a function of rank that was different from that of the other bands in this region (Kuehn et al., 1982). Certainly, this wavenumber range is also characteristic of aliphatic CH2 and CH3 rocking modes, which can be clearly identified in the more aliphatic ex-tracts of certain coals (Riesser et al., 1984). The key question is, To what extent do these aliphatic rocking vibrations contribute to the region 900-700 cm- 1? Are they weak enough to neglect? At this time we have no definitive answer. Because of these uncertainties in band assignments and the problems of accurately subtracting out the mineral bands of low-rank coals, we used the CH stretching region near 3050 cm- 1 as a measure of aromatic CH content (Sobkowiak et al., 1984; Riesser et al., 1984).

5 Determination of Functional Groups in Coal

| — m — i | i i i | i i i | i i i | i i i | i i i 1

3200 cm1 2600 Fig. 37 . (A) Spectrum of the CH stretching region of PSOC 782; (B) composite

aromatic CH stretching peak determined by curve-fitting techniques; (A - B) difference spectrum.

There are also difficulties in using the CH stretching region of the spectra of coals for quantitative measurements. There are two main prob-lems. In low-rank coals the aromatic CH stretching mode is weak, and its intensity is therefore difficult to measure accurately. In high-rank materi-als this mode is much stronger and significantly overlaps the aliphatic CH stretching modes, as illustrated in Fig. 37 for PSOC 782 (91.0% carbon). Our solution to the latter problem is also shown in the figure (Sobkowiak et al., 1984; Riesser et al., 1984). We can least-squares curve-fit the aro-matic modes of this coal to two bands, using a function that is a sum of Gaussian and Lorentizian band shapes. The difference spectrum, shown in the same figure, indicates that, if nothing else, the area of the band can be successfully reproduced.

The problem of band overlap is most critical in the medium- and low-volatile bituminous samples. Because we were concerned with the possi-ble effect of even slight errors in intensity measurements, we also curve-fit the data for the vitrinites of high volatile A (HVA) bituminous rank, even though an initial inspection of the spectra suggested that simple integrated intensities could be used. To our surprise, we found that such integrated intensities could also be used for all our samples, provided that suitably modified conversion factors were employed. Presumably, for the higher-rank samples the integrated intensities are factors directly propor-tional to the true areas. We now turn to these calculations.

As outlined earlier, we attempted to obtain values for the conversion


factors 6ar and eai by solving Eq. (13) as a set of simultaneous equations using data from a number of coal samples (Riesser et al.} 1984). We (somewhat arbitrarily) subdivided our data set according to ASTM rank classification and set about obtaining solutions by standard matrix and least squares iterative procedures. We immediately ran into trouble. Solu-tions for one or two data sets were meaningless (we obtained a negative value for ear), whereas others were heavily weighted by the inclusion or absence of data from specific coals. With the benefit of hindsight, we now realize that this is not surprising. The simultaneous equations we were seeking to solve are, in the mathematical sense, classically ill conditioned. Errors in the data are magnified. This is because we were attempting to obtain solutions to a set of equations in which the magnitudes of the coefficients were very similar. The values for the percentage of hydrogen from elemental analysis and the areas of the aromatic and aliphatic peaks all lie in approximately the same range for coals of the same rank.

The work of Tschamler and de Ruiter (1962), Solomon (1979), Solomon et al., (1982), and ourselves (Kuehn et al., 1982; Sobkowiak et al., 1984; Riesser et al., 1984) is based largely on equating ir data to elemental hydrogen. Consequently, at the risk of beating the subject to death, we believe it important to emphasize the major problem of this method. The difficulties are perhaps best perceived in terms of an analogy in which solutions to two simultaneous equations of the form ax + by = c [cf. Eq. (13)] in two unknowns (x and v) are to be obtained. Each of these equa-tions can be plotted graphically, and if there is a well-conditioned, unique solution, the lines described by each equation will intersect at a well-defined point. If the two equations are dependent, the lines will be paral-lel, and there will be no single solution. An ill-conditioned problem, such as the one encountered here, will have almost parallel lines, so that an intersection point will be difficult to define with precision. By considering a much larger number of equations in the same two unknowns, we are essentially attempting to obtain a more well defined intersection point. Nevertheless, a range of values will clearly give almost equally valid solutions. This problem is inherent in this methodology because of the nature of the data obtained from coals and does not depend on the methods used to obtain solutions, whether the iterative least squares or matrix procedures used in this laboratory or the graphical method em-ployed by Solomon. If this were not bad enough, we also have to consider additional problems involving the data.

As we mentioned briefly in passing, the solutions we obtained to Eq. (13) are heavily influenced by the precision of the data and the inclusion of certain coals in the data set. Specific coals, the boghead coals from Utah, for example, are unusual. One such sample (PSOC 155) is more like an oil


shale than a coal, with a high aliphatic content and a low aromaticity (as determined by 13C NMR). A broad rule of thumb is that coals with strong, sharp aliphatic bands near 2920 cm-1 cannot be used as part of the general data set for determining conversion factors. Such modes are characteris-tic of fairly long sequences of methylene units, which are not found in most coal samples. These types of aliphatic structures have different absorption coefficients than other aliphatic groups, those attached di-rectly to aromatic units, for example (Bellamy, 1975).

The precision of the data can be improved by preparing a number of pellets of each sample. This certainly helps with vitrinite concentrates, in which case corrections due to mineral matter content (and moisture) are usually small. For coal samples, however, the corrections for mineral matter content can lead to significant errors (i.e., significant in terms of obtaining solutions to ill-conditioned equations), particularly for those coals where an LTA has not been prepared. As a result of these factors, we initially performed our calculations for medium- and low-volatile and HVA bituminous rank materials using the data for vitrinite concentrates alone. In order to illustrate the range of valid solutions, we plotted the root mean square deviation between the measured and calculated hydro-gen content against values of eai (and similarly ear). The minimum in this plot is the optimum solution. By use of the areas of the curve-fitted bands for the medium- and low-volatile vitrinites from the Lower Kittaning seam, the curve shown in Fig. 38 was obtained (Riesser et al.y 1984).

^o) 6

CO

T. b

o S 4

c O

'£ 3

- o o o cc

J _ _L _L J 0.10 0.12 0.14 0.16 0.18 0.20 0.22 0.24

2 0.26 0.28 0.30

Conversion Factor 6 ,x 10 al

Fig. 38 . Plot of the root mean square deviation (Hca[c - //exp) versus assumed values of the aliphatic CH conversion factor for a set of medium- and low-volatile bituminous vitri-r.ites.


Solutions for eai in the range 0.19-0.21 (and ear in the range 0.68-0.55) were calculated. The data for the HVA vitrinites gave similar solutions (optimum values of eai near 0.19 and ear near 0.63).

This methodology is capable of defining only a broad range of solutions because of the ill-conditioned nature of the equations. A key point, how-ever, is that the minima in our least squares plots correspond closely to the values of the conversion factors determined previously for bituminous coal extracts by direct calibration from Ή-NMR measurements (Kubelka and Munk, 1931) (see Table 5). We did not assume that conversion factors determined for coal extracts can be directly applied to their parent coals, but demonstrated a correspondence using an independent method of de-termination (however imperfect). It is not unreasonable that there should be such an agreement, at least for bituminous coals where the extracts often represent more than 20% (and often 30%) of the parent coal.

The results of calculations for bituminous coals based on curve-re-solved data provide good evidence for a correspondence in the conver-sion factors determined for parent coals and their extracts. Surprisingly, solutions to the data obtained from simple integration of the stretching modes gave equivalent results. Again, by appropriate plots it can be dem-onstrated that a broad range of solutions can be obtained in relating inten-sities of ir bands to hydrogen content. Optimum solutions were found to be in the range 0.18-0.20 for eai (and 0.70-1.10 for ear), close to the values of 0.20 and 0.85 for eai and ear, respectively, determined for the integrated band areas of bituminous coal extracts (Sobkowiak et eil., 1984). The data for the lignite and subbituminous coals show a sharper minimum, near ea| values of 0.19. This is presumably because in these samples there is a much higher proportion of aliphatic to aromatic hydrogen, so that well-defined solutions are obtained.

We initially anticipated that conversion factors would vary as a func-tion of rank. It is possible and perhaps even probable that they do, as indicated by the results of Solomon et al. (1982). Unfortunately, because methods based on equating band intensities to total hydrogen give a range of almost equally valid solutions, subtle variations cannot be calcu-lated with certainty. We therefore used values for the conversion factors eai and €ar of 0.19 and 0.63, respectively, for the curve-fitted data and values of 0.20 and 0.85, respectively for the integrated band intensities. These correspond closely to the values obtained for bituminous coal ex-tracts by direct calibration, although we again emphasize that this corre-spondence was not assumed. These conversion factors are not exactly comparable to those obtained by Solomon (1979; Solomon et al., 1982), first because the precise values depend to some extent on the integration limits defined, and second because Solomon used the out-of-plane bend-


ing modes to measure aromatic hydrogen content. However, we might anticipate that values for the aliphatic stretching modes should lie some-where in the same range. Converting Solomon's values for absorbtion coefficients to the conversion factors defined here, ea) values of 0.15 were originally reported (Solomon, 1979) and later modified to values of 0.18 for bituminous coals (Solomon et ai, 1982). The latter value is not far from our calculated values of 0.19 to 0.20, and such a convergence is encourag-ing. Unfortunately, even small differences in aliphatic CH content are reflected in proportionally larger differences in calculated aromatic CH content, so this leads to significant differences in the calculated values of ΗΆγΙΗΆ\ values. The //ar///ai values determined by Solomon and co-work-ers and Brown are shown in Fig. 39. There is clearly a large difference. Values determined in our laboratory are somewhat between those ex-tremes but generally closer to those of Brown. Our calculated values of //ai and //ar are' listed in Table 6 for coals and Table 7 for vitrinites. For medium- and low-volatile bituminous coals and vitrinites from the Lower Kittaning seam (medium- and low-volatile and HVA bituminous rank), the values not enclosed in parentheses are based on curve-fitted data, whereas those within parentheses were obtained from integrated band areas. The agreement is surprisingly good. For lower-rank material, val-ues, of Ha\ and //ar were determined simply from integrated data (Riesser et al, 1984).

Plots of aromatic and aliphatic CH content and the ratio of aromatic to aliphatic hydrogen versus percentage of carbon (dry-mineral-matter free,

1.4

1.2

X°1.0

l ° 0 . 8

0.6

0.4

0.2

70 75 80 85 90 %C(dmmf)

Fig. 39. Plot of the values of HJHal reported by (I) Solomon (1979), (II) Gerstein et al. (1984), and (III) Brown (1955).

TABLE 6

Distribution of Hydrogen-Containing Functional Groups in Coals as Measured by FT-IR

Coal PSOC

Medium- ; 1197 782

1196 801

1195 1198

C (wt%, dmmf)

H (wt %, dmmf)

H a s OH (wt %, dmmf)

and low-volatile bituminous 91.1 91.0 89.1 88.4 87.1 86.2

HVA bituminous 276

1193 306 338 768 907 339 708 821 808 268

1116 301

88.9 87.7 86.5 86.3 86.0 85.9 85.5 85.5 84.9 84.7 84.4 83.7 83.5

HVB bituminous 739 854 773

83.5 81.6 81.0

HVC bituminous 592 594 680 548

81.6 81.5 80.6 79.0

4.9 4.4 5.6 5.4 5.4 5.1

5.3 5.9 5.4 6.2 6.1 5.7 5.9 6.0 5.9 5.7 5.7 5.6 5.5

5.7 5.9 5.6

5.9 5.8 5.6 5.3

Lignite and subbituminous 866 231 230 240 A1 242 240A4 241 785 791

77.0 76.6 76.3 75.1 74.5 74.3 73.9 73.2 72.1

5.5 5.0 5.0 6.0 5.4 5.3 5.3 5.4 5.7

0.04 0.04 0.06 0.16 0.11 0.04

0.29 0.20 0.21 0.27 0.31 0.28 0.32 0.31 0.25 0.24 0.16 0.35 0.18

0.30 0.27 0.28

0.28 0.39 0.30 0.21

0.24 0.40 0.41 0.25 0.40 0.29 0.40 0.23 0.28

Aliphatic CH

(wt %, dmmf)

2.4(2.1) 2.4(2.1) 3.6 (3.6) 3.4 (3.3) 3.7 (3.6) 3.2 (2.8)

3.6 3.8 4.2 4.7 4.6 3.7 4.2 4.3 4.4 4.0 3.7 4.3 3.6

3.9 4.0 3.5

3.6 4.5 3.4 3.0

2.7 2.6 2.9 3.8 2.7 3.5 2.6 3.5 4.1

Aromatic CH

(wt %, dmmf)

2.8 (2.4) 2.2 (3.0) 2.0 (2.2) 2.0(1.9) 1.8(2.0) 2.8 (2.3)

1.3 1.6 1.5 1.6 1.5 1.6 1.5 1.5 1.3 1.5 1.7 1.3 1.7

1.3 1.1 1.2

1.3 1.1 1.3 0.9

1.0 1.2 1.0 0.9 0.7 1.1 0.8 1.1 0.7

Aromatic CH

Aliphatic CH

1.17(1.13) 0.91 (1.43) 0.56 (0.62) 0.58 (0.57) 0.48 (0.55) 0.72 (0.82)

0.35 0.41 0.35 0.33 0.33 0.43 0.36 0.35 0.31 0.38 0.46 0.30 0.47

0.33 0.27 0.35

0.36 0.24 0.39 0.33

0.38 0.46 0.36 0.23 0.24 0.32 0.30 0.32 0.18


TABLE 7

Distribution of Hydrogen-Containing Functional Groups in Vitrinite Concentrates as Measured by FT-IR

Aliphatic Aromatic C H Has OH CH CH Aromatic CH

Maceral (wt %, (wt %, (wt %, (wt %, (wt %, PSMC dmmf) dmmf) dmmf) dmmf) dmmf) Aliphatic CH

Lower Kittaning seam samples 52 50 54 56 48 72 49 16 71 68 70 25 18 62 20 27 30 39 36 58 59 33 67

91.1 90.0 89.8 89.4 88.8 88.7 88.3 86.3 86.2 86.2 85.2 85.2 84.8 84.7 84.5 84.1 84.1 84.0 83.9 83.7 83.3 83.0 82.3

4.8 5.3 4.7 5.3 5.4 5.6 5.3 5.6 5.5 5.5 5.3 5.2 5.5 5.5 6.0 5.3 5.2 5.7 5.3 5.3 5.6 5.3 5.5

0.00 0.02 0.03 0.13 0.11 0.04 0.03 0.20 0.25 0.23 0.29 0.43 0.28 0.34 0.25 0.42 0.29 0.30 0.29 0.41 0.33 0.26 0.36

3.0 (2.7) 2.7 (2.8) - (2.6) 3.2 (3.0) 3.0 (2.9) 3.4(3.1) 3.1 (2.9) 3.5 (3.4) 3.5 (3.7) 3.7 (3.6) 3.8 (3.7) 3.3 (3.4) 3.6 (3.6) 3.1 (3.3) 4.0 (4.0) 3.4 (3.5) 3.0 (3.2) 4.0 (3.8) 3.1 (3.1) - (3.4) - (3.7) 3.0 (3.4) - (3.6)

2.4 (2.0) 2.1 (1.8) - (2.3) 2.2(1.7) 1.9(1.7) 2.4(1.9) 2.1 (1.7) 1.9(1.6) 1.6(1.3) 1.7(1.4) 1.4(1.2) 1.1(1.2) 1.5(1.3) 1.3(1.4) 1.5(1.4) 1.0(1.2) 1.1(1.2) 1.8(1.2) 1.2(1.2)

- (l.i) -(1.1) 1.2(1.2)

- (1.0)

0.81 (0.75) 0.76 (0.65) — (0.89)

0.69 (0.56) 0.62 (0.59) 0.71 (0.61) 0.68 (0.61) 0.53 (0.46) 0.46 (0.35) 0.46 (0.38) 0.37 (0.33) 0.34 (0.36) 0.41 (0.37) 0.41 (0.42) 0.37 (0.34) 0.30 (0.34) 0.38 (0.35) 0.45 (0.33) 0.39 (0.39) — (0.32) — (0.30)

0.42 (0.36) — (0.28)

Illinois Basin sample 108 109 107 106 105 78 77 104 75 74 73

85.0 83.0 83.0 81.8 81.5 81.1 81.1 80.8 78.9 78.3 78.2

4.9 5.3 5.2 5.1 5.1 5.4 5.6 5.1 5.3 5.0 5.4

0.47 0.34 0.50 0.52 0.52 0.48 0.45 0.52 0.48 0.37 0.53

2.5 3.5 2.8 3.5 3.9 3.7 3.8 3.5 3.1 3.3 3.6

1.2 1.2 1.1 1.0 1.1 1.0 1.1 1.1 0.9 1.1 0.9

0.46 0.36 0.39 0.28 0.29 0.26 0.30 0.31 0.31 0.34 0.26

{Continued)


TABLE 7 (Continued)

Maceral PSMC

C (wt %, dmmf)

British vitrinite samples Coegnant,

Gellideg North,

Celyen Roddymoor,

Balart Aldwarke,

Silkstone Woolley,

Wheatley Markham,

Main Teversil,

Dunsil

91.4

89.9

88.8

86.9

86.6

82.2

81.5

H (wt %, dmmf)

4.6

5.0

5.3

5.4

5.6

5.5

5.1

H as OH (wt %, dmmf)

0.00

-0.00

-0 .00

0.31

0.28

0.40

0.42

Aliphatic CH

(wt %, dmmf)

2.6

3.2

3.8

3.5

3.7

3.9

3.5

Aromatic CH

(wt %, dmmf)

3.1

2.8

2.1

1.5

1.9

1.2

1.1

Aromatic CH

Aliphatic CH

1.18

0.88

0.55

0.43

0.51

0.32

0.32

dmmf) are displayed in Figs. 40 and 41, respectively. It can be seen that there is considerable scatter in the individual plots of //ar///ai (compare these with the results of Fujii, et al., Fig. 27). This is only partly due to inherent variability in coal structure. We would anticipate errors in the weighing and preparation of KBr pellets, as well as in the corrections

5n

H

Aliphatic CH

OO O Oo

oo o

OZ> O D D

° Q. ° O D D Aromatic CH

74 84 70 72 76 78 86 90 80 82 % C (dmmf)

Fig. 40 . Initial determination of the distribution of hydrogen as aliphatic CH (O) and aromatic CH (D) plotted as a function of weight percent carbon (dmmf).


1 . 2 -

1 . 1 -

1 . 0 -

.9-

.8-

.7-

. 6 -

. 5 -

. 4 -

. 3 -

. 2 -

. 1 -

Q _

7 D 7 1

o

1 72

o° ° o

o

1 1 1 73 74 75

o o °

~l 1 76 77

° < 9 o

1 Γ"" 78 79

o o o

~~I I 1 80 81 82

o °°„ o ° o 0

Q ° Οθ ° 8 ^cPoo 0 °oo o0*«**

83 84 85 86 87

<*>

o o υ oo

o o

— i 1 r— 88 89 90

o°

o

o

—i 1 91 92

% C (dmmf)

Fig. 41. Ratio of aromatic to aliphatic CH (Har/Ha\) plotted as a function of weight percent carbon (dmmf) for the coal and vitrinite samples.

applied to the data to account for mineral matter and moisture content. These experimental errors cancel when the ratio of aromatic to aliphatic hydrogen (and the corresponding band areas) is considered, so that the plot of Har/Ha\ versus percentage of carbon displays a narrower band of values. This is one of the strengths of the methodology used by Brown (1955) more than 25 years ago. Although one could argue with the use of model compounds to calibrate the ratio of peak heights of the CH stretch-ing modes, the values of HJH^ obtained in this study are not that differ-ent. We determined somewhat higher values up to about 86% carbon (in the range 0.25-0.40 as opposed to 0.20-0.30) but similar values for higher-rank coals.

Because of the advantages of using the ratio of band areas, we have proposed a modified methodology for determining i/ar and Ha\ (Riesser et al., 1984). The ratio HJH^ should first be determined from the corres-ponding ratio of peak areas and conversion factors. Using the equation

H' = Har + //al (15) where H' is the weight percentage of elemental hydrogen adjusted for OH (and COOH) content, we can obtain

Iff

ΗΛ = H„IHa + 1 0 6 )

TABLE 8

Modified Values of Har and Ha\ (wt %)

Coals

PSOC

1197 782

1196 801

1195 1198 276

1193 306 338 768 907 339 708 821 808 268

1116 301 739 854 773 592 594 680 548 866 231 230 240 A1 242 240A4 241 785 791

ΗΆΤ

2.5 2.0 1.8 1.8 1.7 2.1 1.4 1.6 1.4 1.4 1.4 1.6 1.4 1.4 1.3 1.5 1.7 1.2 1.6 1.3 1.2 1.3 1.3 1.0 1.4 1.2 1.3 1.4 1.2 0.9 0.9 1.1 1.1 1.1 0.7

# a l

2.2 2.2 3.2 3.2 3.5 2.9 3.9 3.8 4.1 4.3 4.1 3.7 4.0 4.1 4.1 3.9 3.6 4.0 3.5 3.9 4.4 3.8 3.5 4.2 3.6 3.7 3.4 3.0 3.2 3.9 3.8 3.3 3.6 3.5 4.0

Vitrinites

PSMC

52 50 54 56 48 72 49 16 71 68 70 25 18 62 20 27 30 39 36 58 59 33 67

108 109 107 106 105 78 77

104 75 74 73

Gellideg North,

Celyen Roddymoor Aldwarke,

Silkstone Woolley,

Wheatley Markham,

Main Teversil,

Dunsil

H&r

2.2 2.3 2.5 2.1 2.0 2.3 2.2 1.9 1.7 1.7 1.4 1.2 1.5 1.5 1.6 1.1 1.3 1.7 1.4 1.2 1.2 1.5 1.1 1.4 1.3 1.3 1.0 1.0 1.0 1.2 1.1 1.2 1.2 1.0 2.5 2.3

1.9 1.5

1.8

1.2

1.1

/ /a .

2.7 3.0 2.2 3.1 3.2 3.2 3.2 3.5 3.6 3.6 3.8 3.5 3.7 3.6 4.2 3.8 3.5 3.7 3.6 3.7 4.1 3.5 4.0 3.0 3.6 3.4 3.6 3.6 3.9 3.9 3.5 3.7 3.4 3.9 2.1 2.7

3.4 3.6

3.5

3.9

3.6


7-

6 -

5^

4 -

3-

2 -

1 -

0~ 7

Δ

o

o

0 72

Δ

Δ Δ

o ° o

• · o

0 O o ° a o

1 1 74 76

Δ

o

o

o <P a

78 80

Δ Δ Δ

Ο Ο

°ο° ο

ο &°

D D n

1 — 82

Δ Δ

Ο ° ο ο ο ο

°\ ν° ο

°

ο

«bo η Ο 0 η ° °

1 I 84 86

Δ

ο

ο

Δ Δ

Δ Δ Δ Δ

Δ

Δ

θ

° ο

ο

° °ο ° Β ° ο ο

D

~~Γ Γ " 88 90

ί Δ

Δ

1 92

% C (dmmf) Fig. 42. Modified values of hydrogen as aliphatic CH (O) and aromatic CH (D) as well

as total hydrogen (Δ) plotted as a function of weight percent carbon (dmmf).

Values of Har can then be found by difference (//' - Ha\). By means of this procedure, modified values of HM and //ai were obtained (Table 8) and are plotted as a function of rank in Fig. 42. Also shown in this figure is the total hydrogen content determined by elemental analysis. The scatter in the FT-IR data is now normalized to the degree of scatter in elemental analysis data. This is probably a reasonably accurate reflection of coal structure variability.

VII. APPLICATION OF SPECTROSCOPIC DATA TO THE CALCULATION OF COAL STRUCTURAL PARAMETERS

In this final section we briefly discuss how FT-IR data can be combined with 13C-NMR results to obtain further insight into coal structure. In principle, FT-IR measurements can be used to determine quantitatively the aliphatic CH, aromatic CH, and OH content of coal, whereas solid-state 13C-NMR spectroscopy can be used to determine the relative pro-portions of aromatic to aliphatic carbon (Miknis et al., 1979, 1981; Zilm et al., 1981; Painter et al., 1983a). There is much active research aimed at


extending the scope of these techniques to the measurement of additional functionalities. Nevertheless, with these measurements alone a number of fundamental structural parameters (HJCar, HJCa\9 HjHa], etc.) can be calculated. Furthermore, with the measurement of one additional parame-ter, the number of methyl groups (as measured by the fraction of aliphatic carbon or hydrogen involved in such groups), it should be possible to determine the distribution of aliphatic carbon (i.e., the relative propor-tions of CH, CH2, and CH3 groups) and then describe a "mean structural unit" in terms of average aromatic ring size and the type and distribution of bridging units and substituents. This can be accomplished by using the equations originally described by van Krevelen and Schyuer (1957) and utilized by Dryden (1958, 1962) for statistical structural analysis. Various equations were also derived by Brown and Ladner (1960) in order to utilize the data then becoming available from ^-NMR measurements. However, there were a number of uncertainties in applying these equa-tions. Dryden used data from elemental analysis together with estimates of aromaticity and other parameters (e.g., alicyclic hydrogen) that were to some degree uncertain. An iterative procedure was used to solve the equations. The application of the Brown-Ladner equation required as-sumptions concerning the atomic ratio of aliphatic hydrogen to carbon. Quantities such as this can now be determined directly from combined FT-IR and ,3C-NMR measurements. We therefore thought that it might be a relatively straightforward task to calculate mean structural units for the coal samples for which we had accumulated spectroscopic data—and it is. Unfortunately, a major problem arises once we consider whether the numbers so derived mean anything. It can be shown that for an individual coal they do not. The form of the equations are such that errors accumu-late dramatically, and our spectroscopic measurements are insufficiently precise to obtain anything but a broad description of trends as a function of rank.

The fraction aromaticity/a can now be determined with what is consid-ered to be reasonable precision by ,3C NMR using cross-polarization and magic-angle spinning. It is probably more accurate to say that most fuel scientists are comfortable with the values of fa so derived, because there are a number of sources of possible error and these are not quantified. For the sake of the arguments we wish to make here, we will assume (optimis-tically) that the values of /a are good to ±5%. Values of /a for a set of vitrinites reported earlier were combined with more recent measurements of additional samples by Martzel (1983) and Pugmire and Grant (1984) (Fig. 43). There is some scatter, but a reasonably narrow band of values is apparent.

Before turning our attention to the combination of these data with FT-


1.0-i

0.9 J

0.8-1

0.7

0.6-1

^° 0.5 H

0.4 H

0.3-1

0.2

0.H

0 " I I I I I 1 1 1 1 1 1 1 1 1 1 1

76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 % C (dmmf)

Fig. 43. Plot of the fraction aromaticity /a versus percent carbon (dmmf) for a set of vitrinite concentrates.

IR measurements, we briefly mention measurements of methyl groups by FT-IR. The most easily recognized group frequencies for methyl groups are the stretching modes near 2960 and 2870 cm-1 and the symmetric bending mode near 1380 cm-1. The overlap of these bands with other modes is severe, and even sophisticated curve-resolving procedures can-not entirely separate out the contributions of other functional groups. When methyl groups are attached to aromatic rings, however, a weak overtone band appears near 2730 cm"1 (Bellamy, 1975). This band is well separated from other modes, and the precision of FT-IR measurements is such that it should be possible to measure its band area with reasonable accuracy. The process is not entirely straightforward, due to problems with establishing a baseline in this region of a typical coal spectrum (the position of the baseline can dramatically affect the measured area of a weak ir band). An absorption coefficient for this band was determined from model compounds (Starsinic, 1984). Not surprisingly, this coeffi-cient did not vary significantly among a variety of methyl-substituted aromatic materials. This is because many of the methyl vibrational modes are not sensitive to the size and nature of the aromatic entity to which the methyl is attached. We determined that for vitrinite concentrates obtained from bituminous coals percentage of carbon found as methyl groups at-tached to aromatic units varied between 3 and 5% (with one or two out-side this range). These numbers agree very well with the total methyl content determined by 13C NMR for some of the same vitrinites by


R. Pugmire and D. Grant (private communication). Presumably, in vitri-nites very few methyl groups are present in ethyl, propyl, or similar units. (This is not the case for exinites, which are much more aliphatic. We again determined that —4% of carbon is tied up in methyl groups attached to aromatic rings, but NMR analysis indicates a total methyl content of ~7%.)

Given that we can determine reasonably accurate values for the relative proportions of aliphatic and aromatic carbon from 13C NMR, aliphatic and aromatic hydrogen from FT-IR, and methyl group concentration from either or both techniques, what can we calculate and to what extent does it mean anything? Space limitations do not permit us to consider all the equations utilized by Dryden (1958, 1962) and Brown and Ladner (1960), but a relatively simple example indicates the problem. The following two equations can be used to describe the distribution of aliphatic groups:

//a, = / /CH + //CH2 + //CH3 (17)

Cal = CCH + CCH2 + CCH3 (18)

The concentration of hydrogen and carbon in aliphatic groups, //ai and Cai, respectively, can be determined from FT-IR and 13C NMR, respec-tively. The percentage of hydrogen and carbon in methyl groups, //CH3 and CCH3> can also be determined, as already discussed. This leaves two equations in two unknowns. Consequently, we should be able to deter-mine the distribution of aliphatic species in any particular coal. Consider, however, a typical example: a vitrinite concentrate (PSMC 71, 85.2% carbon) with/a = 0.73, //a) - 3.5%, and CCH3 = 3.6%. Solving Eqs. (3) and (4) gives values of CQH = 11.8% and CCH2

= 7.6%. Now consider the effects of relatively modest errors. For example, if we optimistically de-cide that / a is equal to 0.73 ± 0.2 and H.d\ is equal to 3.5 ± 0.2%, we can determine a range of values of CCH2

a n d CCH corresponding to the upper and lower limits of these values. The values of CCH2

n o w fa" *n the range 15.1-2.2%, whereas the values of CCH fall in the range 4.5-16.2%! Clearly, for any single sample even small errors multiply to such an extent that it is not possible to define structural parameters with any degree of precision or confidence.

This situation is not confined to this simple example. For instance, we previously (Painter et al., 1983) used the Brown-Ladner equation to cal-culate the atomic ratio of aliphatic hydrogen to carbon HJC^:

CIH- HlKHJC^ /a cjH ( I V )


Here Ht\ is the fraction of total hydrogen present as aliphatic groups. Assuming errors of approximately ±5% in values of/a, it was found that proportionally much larger errors were calculated in //a|/Ca|, these errors increasing proportionally with rank. This is because HJCA\ actually varies with the reciprocal of 1 - /a , so that errors in fa become increasingly significant. (For example, even small errors in/a for a high-rank coal, say, 0.9 ± 0.02 or ±2%, result in much larger errors in 1 - / a : 0.1 ± 0.02 or ±20%.)

Clearly, the accurate calculation of structural parameters for any spe-cific coal is almost pointless, given the precision of the data presently available. Nevertheless, broad averages and trends as a function of rank can be determined. If we take the plots of/a, Ha\, Har, etc., then we can draw lines through the data. At any particular percentage of carbon we can then read off these lines values that represent an ' 'average" parame-ter value for coals ofthat rank. These average values can then be used to calculate structural parameters. This procedure is limited and unsatisfac-tory, but given the major effects that experimental errors have on the data, it is the best we can do at this time. If nothing else, they show trends as a function of rank. As an example, a plot of the distribution of aliphatic CH groups is shown in Fig. 44. It can be seen that the proportion of CH2

Fig. 44. Estimate of the distribution of aliphatic CH groups as a function of rank.


to CH groups increases as a function of rank, whereas the experimentally determined percentage of carbon as CH3 groups remains approximately constant.

REFERENCES

Bellamy, L. J. (1975). "The Infrared Spectra of Complex Molecules" 3rd ed., Vol. I. Chapman & Hall, London.

Berkowitz, N. (1979). "An Introduction to Coal Technology.'' Academic Press, New York. Brown, J. K. (1955). J. Chem. Soc. p. 744. Brown, J. K., and Ladner, W. R. (1960). Fuel 39, 87. Brown, R. S., Hausier, D. W., Taylor, L. T., and Carter, R. C. (1981). Anal. Chem. 53, 197. Colthup, N. B., Daly, L. H., and Wiberley, S. E. (1975). "Introduction to Infrared and

Raman Spectroscopy," 2nd ed. Academic Press, New York. Dryden, I. G. C. (1958). Fuel 37, 44. Dryden, I. G. C. (1962). Fuel 42, 55. Dryden, I. G. C. (1963). In "The Chemistry of Coal Utilization" (H. H. Lowry, ed.), Suppl.

Vol., p. 232. Wiley, New York. Durie, R. A., and Sternhell, S. (1959). Aust. J. Chem. 12, 205. Durie, R. A., Schewchyk, Y., and Sternhell, S. (1966). Fuel 45, 99. Elliott, J. J., Brown, J. M., and Baltrous, J. P. (1984). To be published. Estep, P. A., Kovach, J. J., and Karr, C , Jr. (1968). Anal. Chem. 40(2), 358. Friedel, R. A. (1966). In "Applied Infrared Spectroscopy" (D. N. Kendall, ed.), p. 312. Van

Nostrand-Reinhold, Princeton, New Jersey. Fujii, S. (1963a). Fuel 42, 17. Fujii, S. (1963b). Fuel 42, 341. Fujii, S., Osawa, Y., and Sugimura, H. (1970). Fuel 49, 68. Fuller, M. P., and Griffiths, P. R. (1978). Anal. Chem. 50, 1906. Fuller, M. P., Hanadeh, I. M., Griffiths, P. R., and Lowenhaupt, D. E. (1982). Fuel61, 529. Gerstein, B. C , Dubois, Murphy, P., Ryan, L. M., and Solomon, P. R. (1984). In press. Gethner, J. S. (1980). Prepr. Div. Org. Coat. Plast. Chem., 180th Meet. Am. Chem. Soc. 43,

413. Given, P. H. (1960). Fuel 39, 147. Green, T., Kovac, J., Brenner, D., and Larsen, J. W. (1982). In "Coal Structure" (R. A.

Meyers, ed.), Chapter 6, Academic Press, New York. Griffiths, P. R. (1975). "Chemical Infrared Fourier Transform Spectroscopy." Wiley, New

York. Hirschfeld, T. (1979). In "Fourier Transform Infrared Spectroscopy" (J. R. Ferraro and

L. J. Basile, eds.), Vol. 2, Chapter 6. Academic Press, New York. Jones, R. N. (1969a). Appl. Opt. 8, 597. Jones, R. N. (1969b). Pure Appl. Chem. 18, 303. Jones, R. N., Seshadri, K. S., Jonathan, N. B. W., and Hopkins, J. W. (1963). Can. J.

Chem. 41, 750. Koenig, J. L. (1975). Appl. Spectrosc. 29, 293. Krishnan, K., and Ferraro, J. R. (1982). In "Fourier Transform Infrared Spectroscopy"

(J. R. Ferraro and L. J. Basile, eds.), Vol. 3, Chapter 5. Academic Press, New York. Kubelka, P., and Munk, F. (1931). Z. Tech. Phys. 12, 593. Kuehn, D. W., Snyder, R. W., Davis, A., and Painter, P. C. (1982). Fuel 61, 682. Larsen, J. W. (1981). AIP Conf. Proc. 70, 1.


Likhtenshtein, V. I., Popov, V. K., and Rusyanova, N. D. (1980). Khim. Tverd. Topi. (Moscow) 14(4), 19.

Liotta, R. (1979). Fuel 58, 725. Lucht, L. M., and Peppas, N. A. (1981). AIP Conf. Proc. 70, 28. Maddams, W. F. (1980). Appl. Spectrosc. 34(3), 245. Martzel, E. (1983). M.S. Thesis, Case Western Reserve University, Dept. of Macromolecu-

lar Science, Cleveland, Ohio. Miknis, E. P., Maciel, G. E., and Bartuska, V. J. (1979). Org. Geochem. 1, 169. Miknis, E. P., Sullivan, M., and Maciel, G. E. (1981). Org. Geochem. 3, 19. Neavel, R. C. (1981). Adv. Chem. Ser. 192, 1. Osawa, Y., and Shih, J. W. (1971). Fuel 50, 53. Painter, P. C , and Coleman, M. M. (1980). Digilab FTSIIR Notes No. 35. Painter, P. C , Coleman, M. M., Jenkins, R. G., Whang, P. W., and Walker, P. L., Jr.

(1978). Fuel 57, 337. Painter, P. C , Rimmer, S. M., Snyder, R. W., and Davis, A. (1981a). Appl. Spectrosc.

35(1), 102. Painter, P. C , Coleman, M. M., Snyder, R. W., Mahajan, O., Komatsu, M., and Walker,

P. L. (1981b). Appl. Spectrosc. 35, 106. Painter, P. C , Snyder, R. W., Starsinic, M., Coleman, M. M. Kuehn, D., and Davis, A.

(1981c). Appl. Spectrosc. 35, 475. Painter, P. C , Kuehn, D. W., Starsinic, M., Davis, A., Havens, J., and Koenig, J. L.

(1983a). Fuel 62, 103. Painter, P. C , Starsinic, M., Squires, E., and Davis, A. (1983b). Fuel 62, 742. Pugmire, R., and Grant, D. (1984). Anal. Chem. In press. Retcofsky, H. L. (1977). Appl. Spectrosc. 31, 116. Retcofsky, H. L., and Friedel, R. A. (1968). Fuel 47, 487. Rhoads, C. A., Senftle, J. T., Coleman, M. M., Davis, A., and Painter, P. C. (1983). Fuel62,

1387. Riesser, B., Starsinic, M., Squires, E., Davis, A., and Painter, P. (1984). Fuel 63, 1253. Shafer, H. N. S. (1979). Fuel 58, 673. Sobkowiak, M., Riesser, B., Starsinic, M., Painter, P. C , and Given, P. H. (1984). Fuel 63,

1245. Solomon, P. R. (1979). Prepr. Pap.—Am. Chem. Soc, Div. Fuel Chem. 24(2), 185. Solomon, P. R., and Carangelo, R. M. (1982). Fuel 61, 663. Solomon, P. R., Hamblen, D. G., and Carangelo, R. M. (1982). ACS Symp. Ser. 205, 77. Spiro, C. L. (1981). Fuel 60, 1121. Starsinic, M. (1984). Ph.D. Thesis, Pennsylvania State University, University Park. Starsinic, M., Otake, Y., Walker, P. L., and Painter, P. C. (1984). Fuel 63, 1002. Tschamler, V. H., and de Ruiter, E. (1962). Brenst.-Chem. 43, 16. van Krevelen, D. W. (1966). Fuel 45, 229. van Krevelen, D. W., and Schuyer, J. (1957). "Coal Science." Elsevier, Amsterdam. Wiser, W. H. (1978). ACS Symp. Ser. 71, 29. Yarzab, R. F. , Abdel-Baset, Z., and Given, P. H. (1979). Geochim. Cosmochim. Acta 43,

281. Zilm, K. W., Pugmire, R. J., Grant, D. M., Wood, R. E., and Wiser, W. H. (1981). Fuel 6^,

111.

APPLICATIONS OF DIFFUSE REFLECTANCE SPECTROSCOPY IN THE FAR-INFRARED REGION

John R. Ferraro

Department of Chemistry Loyola University Chicago, Illinois

Alan J. Rein

IBM Instruments, Inc. Danbury, Connecticut

I. Introduction 244 II. Instrument Requirements for Far-Infrared

Interferometers 245 III. Instrument Design for Far-Infrared FT-IR 245

A. Optical Layout 245 B. Sources of Far-Infrared Radiation 247 C. Beam Splitters 248 D. Detectors in the Far Infrared 251 E. Filters 252 F. Far-Infrared Calibration 252

IV. Comparison of Far-Infrared Sampling Techniques 252

V. DRIFT Techniques 253 A. Historical Overview and Theory 253 B. Accessories for Far-Infrared Diffuse

Reflectance 256 VI. Applications of DRIFT in the Far Infrared 257

A. Diffuse Reflectance Measurements on Molybdate Catalysts 258

B. Diffuse Reflectance in the Study of Zeolites 264

C. Reststrahlen Effects of Alkali Metal Halides in DRIFT Experiments 270

D. Identification of Inorganic Species in the Far Infrared by DRIFT 278

References 280


ISBN 0-12-254104-9

244 John R. Ferraro and Alan J. Rein

I. INTRODUCTION

The far-infrared (far-ir) region has been defined on recommendation of the IUPAC committee as that part of the electromagnetic spectrum from <200 to 10 cm"1. At various times it has been loosely considered to be the frequency range <650 cm-1 or beyond, where the KBr prism or grating instrument cutoff occurs.

The early development of far-ir spectroscopy proceeded along the lines of reststrahlen (residual ray), prism, grating, and, later, interferometric techniques. Rubens performed numerous experiments and from 1889 to 1922 published many papers dealing with the far-ir region (Palik, 1962). In the instrumental area, early contributors were Czerny (1923), Barnes (1935), Barnes and Czerny (1931), Barnes et al (1935), Wood (1910), Randall (1932, 1939), and Strong (1931). After World War II, Golay (1947) developed the Golay pneumatic detector. This spurred construction of far-ir spectrophotometers in various universities. McCubbin and Sinton (1950), Lord (1952), Plyler (1947, 1948), and Oetjen et al (1952) led this development in the United States, whereas Yoshinaga and Yamada (1952), Hadni (1954), Genzel and Eckhardt (1954a,b), Yaroslavski et al (1956), and Bloor et al (1961a,b) were active in far-ir instrumentation development in other countries. In the late 1950s and early 1960s, com-mercial dispersive far-ir instrumentation became available.

Although in 1891 Michelson (1891, 1927) invented the interferometer that bears his name, progress in Fourier transform interferometry (FT-IR) instrumentation in the far-ir region was slow. Rubens and Woods (1911) measured the first interferogram in 1911. The FT-IR technique suffered from a lack of computational methods to transform the interferogram into a spectrum. This problem was not solved until 1965, when Cooley and Tukey (1965) developed their algorithm for fast Fourier transform spec-troscopy. Rapid progress then ensued, and soon commercial interferome-ters became available. Major contributors in this development were Bell (1970), Sanderson and Scott (1971), Mertz (1965), Gebbie and Vanasse (1956), and Connes (1961). The first commercial FT-IR instruments dealt with the far-ir region (e.g., the Polytec FIR 30 interferometer and the Beckman RIIC, Ltd. FS-720, 820, which have now been discontinued). To achieve coverage of the region lower than 300 to 400 cm-1, one must use more expensive FT-IR instruments. Most dispersive and low-priced FT-IR instruments cut off at —400 cm-1 (with some exceptions, e.g., Perkin-Elmer model #680, which has a range to 180 cm-1)·

For reviews on far-ir spectroscopy, see Bentley et al (1957), Bentley and Wolfarth (1959), Adams (1967), Ferraro (1970, 1971), Lord (1960), Stewart (1965), Wood (1963), Wilkinson et al (1962), Brasch et al (1968),

6 Applications of Diffuse Reflectance Spectroscopy 245

and Durig and Cox (1978). Adams (1967) and Ferraro (1968, 1970, 1971) demonstrated the usefulness of the far-ir region for characterizing inor-ganic and coordination compounds.

In this chapter we discuss far-ir instrumental requirements, sampling techniques, and applications, particularly concentrating on diffuse reflec-tance measurements in the far-ir region.

II. INSTRUMENT REQUIREMENTS FOR FAR-INFRARED INTERFEROMETERS

Working in the far-ir region requires instrumentation that can deal with the low signal-to-noise (S/N) ratio obtained. The S/N problem results from the poor sources and inefficient detectors available for this region. Other problems include stray light, water absorption, zero drift, fringing, and overlapping orders (in grating instruments). Most of these problems can be reduced with a vacuum or purged FT-IR spectrometer.

The first commercial interferometers were constructed for use in the far-ir. This was due largely to the following factors:

(1) Only a limited number of data points are needed for the far-ir re-gion, requiring less time for computations.

(2) Less stringent mechanical tolerances are necessary for the interfer-ometer drive in medium-resolution far-ir experiments than for near-or mid-ir measurements of similar resolution.

(3) Far-ir interferometers could be constructed easily and at lower cost.

The overall Fellgett (1958) and Jacquinot (1960) advantages of FT-IR in the far-ir region are not as striking as those realized in the mid-ir region. Griffiths (1975) pointed out that the throughput of a far-ir instrument is more a function of the/number of the source foreoptics than the interfer-ometer itself, and as a consequence, little advantage arises from the throughput energy of the interferometer compared with that of a disper-sive instrument. The greatest advantages of the interferometric instru-ments in the far-ir are the curtailment of stray light, more rapid data collection, and more accurate and precise measurements.

III. INSTRUMENT DESIGN FOR FAR-INFRARED FT-IR

A. Optical Layout

Representative examples of a modern FT-IR instrument design for far-ir studies are the IBM Instruments IR/98, Bruker IFS 113 series spec-


Fig. 1. Interferometer optics schematic for IBM Instruments IR/98. I, Source cham-ber: (a) near-, mid-, or far-ir sources; (b) automated aperture. II, Interferometer chamber: (c) optical filter; (d) automatic beam splitter changer; (e) two-sided movable mirror; (f) control interferometer; (g) reference laser; (h) remote-control alignment mirror. Ill, Sample chamber: (i) sample focus; (j) reference focus. IV, Detector chamber: (k) near-, mid-, or far-ir detectors.

trometers (Fig. 1). Far-ir measurements are facilitated by these instru-ments because of the following considerations.

1. Vacuum Operation

The major advantage of an evacuated instrument is the elimination of atmospheric interferences (i.e., water vapor and carbon dioxide). Water vapor in particular is highly absorbing in the far ir, further reducing the available ir radiation in this energy-starved region. Moreover, in high-resolution experiments, the rich rotational fine structure of background water often interferes with bands arising from the compound of interest. Although a well-purged instrument flushed with dry nitrogen gas can be used to acquire far-ir data, the time required to reduce the water vapor background to an acceptable level can be several hours as compared with several minutes for an evacuated optical bench. This is especially true for the spectral region from < 150 to 10 cm-1.


2. Genzel Interferometer

Of particular advantage for far-ir spectroscopy, this interferometer de-sign features a beam focused on the beam splitter. This is in contrast to the Michelson interferometer, which collimates the beam. Thus, in the Genzel interferometer, the beam diameter at the beam splitter is ~8 mm. This permits the use of smaller beam splitters, which has important impli-cations:

(1) Several beam splitters can be mounted on a motorized wheel, which under computer control permits the selection of a particular beam splitter. Thus, the far-ir region (400-10 cm 1 ) can be accessed through the use of the appropriate beam splitter without opening the interferometer section of the instrument. This permits rapid far-ir survey of a compound and then selection of the appropriate beam splitter to optimize the region of interest.

(2) Another advantage of smaller beam splitters is the reduction of "drum head" effects. When Mylar is stretched tightly over a large area, vibration of the film can occur, introducing noise into the spectrum.

Due to the low angle of incidence of the beam on the beam splitter (<16°), the Genzel optical design reduces instrument-imparted polariza-tion effects. This also permits the use of metal mesh (wire grid) beam splitters in addition to the Mylar beam splitters.

B. Sources of Far-Infrared Radiation

The far-ir source most generally used in FT-IR instruments is a high-pressure mercury lamp. The lamp operates with a dc arc and requires a high-voltage pulse to start the discharge. Manufacturers of FT-IR spec-trometers recommend the use of the Globar source to 125 cm 1 , because it is claimed that the emission from this source is as great or greater than that from a mercury lamp. An improvement over the standard Globar source has appeared. This is a heli-coil Globar source that has a greater heatable surface area. It appears to emit more energy and is more stable than the standard Globar and is suitable to —100 cm-1. Digilab has an-nounced a new high-temperature ceramic source that reportedly delivers 90 mW of ir radiation at the sample.

In general, the source problem continues to plague studies in the far-ir region. New sources are constantly being developed, and because of this continued interest it is hoped that the problem will be ameliorated with time. For another discussion of far-ir sources, see Genzel (1970).


C. Beam Splitters

Beam splitters in the far-ir region are usually thin, stretched polymer films with reflectance characteristics determined by thickness, refractive index, angle of incident radiation, and frequency of radiation.

The most commonly used beam splitter in the far ir is usually polyethyl-ene terephthalate (Mylar in the United States and Melinex in Europe). A new polymer called TPX (methylpentane polymer by Mitsui and Co. Ltd.) is said to have good transmission properties in the submillimeter and millimeter region. Various thicknesses of polymer film are required as one proceeds from higher to lower frequency in the far-ir. Table 1 records the various thicknesses and appropriate far-ir range. Representative beam splitter single-beam transmission profiles for 6- and 23-/xm Mylar are shown in Fig. 2. If the beam splitter single-beam files are used as sample and reference, their ratio yields the 100% line (Fig. 3).

The 100% line is an excellent diagnostic tool for determining beam splitter ranges. Where regions of high transmission exist for the particular thickness of Mylar, a flat, low noise line is observed. Areas of high ab-sorbance are characterized by much greater noise in the 100% line plot. Thus, it is clear that the 6-μιη beam splitter would be chosen for a mea-surement involving the range from 200 to 400 cm-1, whereas the 23- or 50-μ,ιη beam splitter would be used for the region below 100 cm 1 . In select-ing a beam splitter it is important to note that, although there may be some overlap in coverage for the Mylar beam splitters of different thicknesses, usually one is better (i.e., has higher transmission) for a given region.

Metal wire grids or meshes also exhibit properties of reflectance and transmission and are suitable for use as beam splitters in the far-ir region.

TABLE 1

Range of Various Beam Splitters as a Function of Thickness

Material

Mylar Mylar Mylar Mylar Mylar Mylar Metal mesh Metal mesh

Thickness (μπί)

3.5 6.0

12.0 23.0 50.0 75.0

20 lines/mm 8 lines/mm

Approximate range (cm-1)

750-125 450-80 220-40 110-20 50-10 <40

750-40 750-40


The metal mesh beam splitter gives broad coverage in the far ir (see Table 1) and is excellent as a survey beam splitter. In addition, it has narrow regions of high transmittance and can be used as the beam splitter of choice for that wavelength range. For example, the 20-line/mm metal

CO

z

500

WAVENUMBER (cm- 1) Fig. 2. Far-ir beam splitter single-beam profiles: (a) 20-line/mm metal mesh, (b) 23-/xm

Mylar, (c) β-μγη Mylar.


o z <

C/5

<

500

Fig. 3 . Far-ir beam splitter, (b) 23-μιτι Mylar, (c) 6-/xm Mylar.

WAVENUMBER (cm"1)

100% transmission lines: (a) 20-line/mm metal mesh,


TABLE 2

Properties of Various Far-Infrared Detectors3

Detector

Golay

Triglycine sulfate

Germanium bolometer Carbon bolometer Silicon bolometer InSb

Operating temperature

(K)

300

300

2.15 2.1 1.4 1.8

NEP*

8 x 10-11

1.5 x 10-9

5 x 10-13

1 x 1011

6 x 10-14

5 x 10-12 at 10 cm 1 to 10-11 at 50 cm-1

Response time

(msec)

-15

~1

0.4 10 0.3

2 x 10-4

Operating range (cm1)

5-1800 (diamond window)

5-700 (polyethylene window)

5-500 <160 5-200 5-50

a Taken in part from Ferraro (1970). b NEP, Noise equivalent power (detector sensitivity), in watts.

mesh beam splitter has excellent transmission properties in the region from 75 to 100 cm"1 (Fig. 3).

For additional discussions of beam splitters, see Bell (1972) and Grif-fiths (1975).

D. Detectors in the Far Infrared

We have mentioned the importance of the detector in far-ir spectros-copy. The use of the Golay pneumatic detector after World War II stimu-lated interest in the far-ir region. The Golay detector is a thermal detector and, with a diamond window, is useful from the visible to the far-ir region at room temperature. Its chief disadvantage is its short lifetime. In present FT-IR instruments the pyroelectric detector (triglycine sulfate and deuter-ated triglycine sulfate) equipped with polyethylene windows for the far-ir has replaced the Golay detector. Triglycine sulfate or deuterated trigly-cine sulfate detectors can be used at room temperature.

For longer-wavelength far-ir measurements, it is more advantageous to use the carbon resistance bolometer, silicon bolometer, or single-crystal doped germanium bolometer. These are classified as thermal detectors and are operated at cryogenic temperatures. A photoconductive detector such as InSb is also useful for this region. Table 2 summarizes the proper-ties and ranges of various far-ir detectors.


For additional discussions of far-ir detectors, see Möller and Rothschild (1971) and Richards (1970).

E. Filters

For information on far-ir filters, see Finch et al. (1970).

F. Far-Infrared Calibration

Far-ir instruments can be calibrated by the use of known frequencies for the rotational spectra of simple gases (e.g., H20 vapor). One can also use the orders of mercury vapor emission. Simpler techniques involve the use of yellow mercuric oxide and hexaiodobenzene. For another discus-sion of far-ir calibration techniques, see Ferraro (1971).

IV. COMPARISON OF FAR-INFRARED SAMPLING TECHNIQUES

A diversity of far-ir sampling techniques are available to the scientist. All have advantages and disadvantages (see Ferraro, 1984b). The choice of technique is dictated by the task at hand and the physical properties of the compound (see Table 3). One has the choice of using a matrix or a matrix-free technique, as well as using a transmittance or a reflectance method. For the matrix method, it is necessary to obtain a well-ground dispersion of the sample in the matrix. Suitable far-ir matrices include Nujol, Csl, high-density polyethylene powder, and adamantane. In some cases, the sample is destroyed by grinding or can interact with the matrix. In other cases, transmittance techniques are unsuitable, especially for opaque materials (e.g., molecular metals, catalytic surfaces). For these materials one must consider a reflectance method (e.g., diffuse reflec-tance infrared Fourier transform spectrometry, DRIFT, or its dispersive analog, DRUIDS; Hannah and Anacreon, 1983), or the diamond anvil cell (DAC). For a review of the DAC sampling technique in the far-ir region, see Ferraro (1984a), Ferraro and Basile (1980), and Krishnan and Ferraro (1982).

This chapter concentrates on the use of DRIFT in the far-ir region. Because of the rapidly increasing importance of FT-IR measurements in the area of catalysis and inorganic chemistry, far-ir DRIFT measurements are finding increasing use. Many of the catalysts are coordination com-pounds, and the metal atoms play an important role in catalytic behavior. Vibrations of the ligand are found in the mid-ir region. Metal-ligand vi-brations are located in the far ir. Thus, DRIFT provides a method of


TABLE 3

Comparison of Sampling Techniques Used in the Far-Infrared Region

Technique

DRIFT KBr or Csl

Polyethylene powder

Nujol mull

KBr or Csl disk

Specular reflectance

Diamond anvil cell

Attenuated total reflec-tance

Photoacoustic spectros-copy0

Advantages

Good S/N

Can be used for samples

Inert matrix Can be used for

samples Good S/N

Good S/N

Enhanced S/N

Can be used for samples

Matrix free

Matrix free

Matrix free

opaque

opaque

opaque

Disadvantages

Low-frequency absorption of matrix

Reaction with ionic com-pounds

Lower S/N

Possible reaction with labile compounds

Low-frequency absorption of matrix

Reaction with ionic com-pounds

Adherence to surface necessary

Compound must not react with metal

Diamonds needed Not suited for opaque

materials Experimental difficulties

encountered

a See Chapter 9 for information on photoacoustic spectroscopy in the far ir.

studying catalytic species in the inorganic fingerprint region (far ir). By combining mid- and far-ir DRIFT measurements, the chemist has a pow-erful probe for characterizing catalytic species.

V. DRIFT TECHNIQUES

A. Historical Overview and Theory

The diffuse reflectance technique has received wide attention in the uv-visible region, primarily because high-intensity sources and sensitive de-tectors are available. Only in recent years has the technique of diffuse reflectance in the mid-ir region taken its place with transmission and internal reflection as a sampling method of great importance in ir spec-trometry (Fuller and Griffiths, 1978a,b). The explosive growth of FT-IR is


partially responsible for this. Investigators (Willey, 1976; Fuller and Grif-fiths, 1978a,b) have shown that the limitations of the inherently weak diffuse reflectance effect and insensitive ir detectors (relative to the pho-toelectric detectors available in visible spectrometry) are partially over-come by the capacity of FT-IR spectrometers to acquire multiple scans on a sample rapidly, thus improving the S/N ratio. With DRIFT it is now possible to routinely observe disperse materials that cannot be examined by other ir techniques. The technique has also been shown to be viable in the mid-ir region with dispersive instrumentation. The general rule is that for powders or fibers that cannot be placed in a matrix for ir measure-ment, diffuse reflectance offers a sampling solution. Inorganic compounds are excellent candidates for the diffuse reflectance techniques because transmission ir spectrometry using alkali halide matrix techniques often causes ionic interchange, altering the compound of interest. Several groups (Kortüm and Delfs, 1964; Niwa et al.f 1979; Garlock and Rein, 1981a,b; Van Every et al., 1981; Smyrl et al., 1983) have shown that the diffuse reflectance technique is excellent for the study of species adsorbed on catalysts because these experiments often require a neat powdered catalyst surface.

Several excellent treatises on diffuse reflectance have been written. In particular, those by Fuller and Griffiths (1978b) and Maulhardt and Kunath (1982) offer very practical discussions of this technique.

Although diffuse reflection has become a common method in ir spec-trometry, the theory relating the concentration of a sample to its diffuse reflectance is not adequately defined. This is due to the difficulty of ob-taining optical constants, because sample transmission, scattering, ab-sorption, and reflection all influence diffuse reflectance. Simply stated, diffuse reflectance occurs when light impinges on the surface of a material and is partially reflected and transmitted. Light that passes into the mate-rial may be absorbed or reflected out again. Thus, the radiation that re-flects from an absorbing material is composed of surface-reflected and bulk reemitted components, which summed are the diffuse reflectance of the sample.

Maulhardt and Kunath (1982) presented an excellent historical over-view of diffuse reflectance. They pointed out that there are two classes of theories. The first describes the interaction of light with single particles (Rayleigh, 1899; Mie, 1908), whereas the second describes the interaction of light with a continuous or multiparticulate medium (Kubelka and Munk, 1931). It is the latter theory that has been most often applied to diffuse reflectance FT-IR measurements, largely due to its simplicity. Maulhardt and Kunath pointed out that the disadvantage of the Kubelka-Munk relationship is that there is no exact comparison between absorp-


tion coefficients obtained from diffuse reflectance measurement and the bulk material absorption coefficient.

Reflection and absorption can be considered separate effects if particle size is large relative to wavelength of light (i.e., the uv-visible range), but in the ir energy region, particle size and wavelength are often very close, complicating the relationship. In the far-ir region, where wavelength may indeed be significantly larger than sample particle size, reflection, refrac-tion, and absorption are competing effects and the theory of this interac-tion is even more poorly defined.

Fuller and Griffiths (1978a,b,1980) semiempirically described the use of the Kubelka-Munk equation for DRIFT measurements. The Kubelka-Munk equation,

/(/?.) = (1 - Roo)2/2R„ = kls

relates the absolute reflectance Roo of an "infinitely thick" layer to the scattering coefficient s and molar absorption coefficient k. In practice, R^ may be replaced by R (sample) +· R (standard), where R (sample) is the single-beam sample reflectance spectrum, and R (standard) is the single-beam reflectance spectrum of the reference material. This reference mate-rial is chosen for its nonabsorbing, highly reflective properties in the wave-length region of interest. For the mid-ir region potassium chloride powder is used, and for the far-ir region cesium iodide or high-density polyethyl-ene powder is suitable, with the reservations discussed in Section VI.C.

Some interesting ramifications result from the relationship. Fuller and Griffiths (1980) showed that for absorbance spectra the SIN ratio is pro-portional to the concentration and absorptivity, whereas in diffuse reflec-tance the spectral SIN ratio is related to the square root of the sample concentration or absorptivity. Therefore, diffuse reflection measurements appear to be particularly well suited for ir microsampling. Several groups have observed (Ferraro, 1982, unpublished data; S. Garlock, unpublished data, 1982; R. Barbour, M. Gendreau, and A. Rein, private communica-tion, 1982; Smyrl et al., 1983) that band intensities that are weak in trans-mission spectra appear to be enhanced in diffuse reflectance spectra, so that a spectrum may seem qualitatively richer in information. This is important, for example, in inorganic speciation experiments (R. Barbour, M. Gendreau, and A. Rein, private communication, 1982).

As a result of the complex interaction between ir radiation and a partic-ulate surface, the reststrahlen effect becomes particularly pronounced in DRIFT measurements, and care must be exercised to avoid this effect. More recently, Fuller and Griffith (1978b) and Grim et al. (1983) described this phenomenon and its effect on DRIFT measurements in the mid-ir region. As has been shown, if the material of interest has large particle


size relative to the wavelength of light, specular reflectance from the sample surface appears as strong inverse peaks in the diffuse reflectance spectrum (Vincent and Hunt, 1968). This effect is observed particularly in compounds that are highly absorbing and the index of refraction of which changes rapidly at intensely absorbing peaks. By a reduction in particle size, surface reflection gives way to reflection from the bulk material; thus, specular reflectance is minimized and the inverse bands diminish and reverse. It was shown that the easiest way to eliminate surface reflec-tion is to dilute the compound in an appropriate matrix. At 1 to 2% dilution, bulk conditions are approximated and diffuse reflection spectra appear to be similar to those observed in transmission experiments.

An alternative solution to the reststrahlen effect is to grind the sample neat to the point where particle size is closer to the wavelength of radia-tion. Specular reflectance is thereby minimized. In the far ir, where wave-length is long compared with particle size, bands should be free of rest-strahlen effects upon dilution and grinding of sample. However, alkali metal halides appear to be exceptions (see Section VI.C).

The use of the DRIFT method in the far ir has lagged, even with FT-IR instrumentation, primarily because one must interface a reflectance at-tachment with the spectrophometer or interferometer in an already en-ergy-starved region. Only a limited number of experiments have been reported using the DRIFT technique to 100 cm-1 with Csl powder as the diluent (A. Rein, unpublished data, 1984). The technique has now been extended to 40 cm-1 using polyethylene powder as the far-ir diluent and nonabsorbing matrix (see Ferraro et ai, 1984; Ferraro and Martin, 1984; B. Chase, personal communication, 1983). The use of polyethylene pow-der is necessary in the recording of far-ir reflectance spectra of ionic materials. Caution must be exercised in the use of alkali metal halides as diluents in the far-ir region because of their reaction with ionic materials. Another complication results from the strong photon absorption of alkali metal halides in the far ir. For the far-ir DRIFT method the preparation technique involves mixing the sample with a powdered diluent in the ratio 90-95% diluent to 5-10% sample. The mixing can be accomplished in a Wig-L-Bug or a dry box for water- or air-sensitive materials. The S/N ratios obtained from DRIFT samples when polyethylene powder is used as a diluent are —10% less in S/N than a comparable transmission experi-ment, but spectral results appear to be comparable.

B. Accessories for Far-Infrared Diffuse Reflectance

Diffuse reflectance measurements in the mid-ir region have been car-ried out using a variety of optical designs (Coblentz, 1913; Gier et al.,


1954; White, 1964). Among the most efficient are the hemiellipsoidal and ellipsoidal reflectometer design. Several researchers (Kortüm and Delfs, 1964; Blevin and Brown, 1965; Dunn et al., 1966) used these optics in conjunction with dispersive spectrometers and showed the value of dif-fuse reflection for chemical analysis. Fuller and Griffiths (1978a) reported a diffuse reflectance optics design based on ellipsoidal mirrors that was coupled with an FT-IR spectrometer to give excellent mid-ir diffuse re-flectance spectra. J. Harrick (private communication, 1979) of Harrick Scientific developed a commercial diffuse reflectance accessory that fit all FT-IR spectrometers. This design featured ellipsoidal mirrors for focusing and collecting radiation. J. Harrick (private communication, 1980) de-signed a second-generation diffuse reflectance accessory known as the "praying mantis" because of the configuration of the large ellipsoidal collecting mirrors. The advantage of this new design is twofold: (1) The specular component is more easily eliminated, and (2) there is much greater sampling room such that a heated, evacuable chamber can be placed in the focus of the optics. This permits, for example, in situ cataly-sis measurements by DRIFT. S. Garlock and A. J. Rein (unpublished data, 1980) reported observing the adsorption of NO gas on nickel mor-denite catalyst by DRIFT using the heatable, evacuable chamber in the "praying mantis" accessory. They also showed that the activation of zeolite catalyst could be followed by near-ir diffuse reflectance measure-ment using this accessory. Smyrl et al. (1983) used the accessory to monitor the heterogeneous reaction of LiH and LiOH with water and carbon dioxide. More recently, D. Sting (private communication, 1983) of Barnes/Spectra Tech developed a commercial diffuse reflectance acces-sory for FT-IR spectrometers. It features ellipsoidal mirrors at the top of the accessory that slide back, revealing the top-loading sample cup. This design makes sample changing very easy, and the compact size of the accessory permits it to be placed in the sample chambers of smaller in-struments.

The authors have found that the commercial accessories give good far-ir diffuse reflectance spectra. Both accessories are designed with means of eliminating most of the specular component of scattered light and give approximately 5-10% throughput from polyethylene powder in the far-ir region.

VI. APPLICATIONS OF DRIFT IN THE FAR INFRARED

To date, far-ir measurements have been carried out largely using tradi-tional ir sampling techniques, that is, transmission (Ferraro, 1968). Al-though the use of reflectance techniques in the far ir have been reported,


these have invariably been associated with measuring specular radiation from semiconductor materials. DRIFT experiments are often reported in the mid-ir literature, but few, if any, applications of far-ir diffuse reflec-tance have been published (see Garlock and Rein, 1981a,b; B. Chase, private communication, 1983; Ferraro and Martin, 1984; Ferraro et al.y

1984). As cited in Section V, it is clear that far-ir DRIFT measurements have

substantial value for the study of catalysts and inorganic species. Whereas mid-ir diffuse reflectance can yield the changes that occur in the structure of an absorbing molecule upon interaction with an adsorbate, far-ir diffuse reflectance can yield information on the changes in the ad-sorbate lattice upon interaction. Thus, a more complete picture of the adsorber-adsorbate interaction can be obtained using complementary far-and mid-ir DRIFT measurements. Moreover, low-frequency vibrations in inorganic compounds are observed in this spectral region (Adams 1967, 1971; Ferraro, 1968). In the following sections, the authors discuss some applications of far-ir DRIFT to the study of catalytic and inorganic systems.

A. Diffuse Reflectance Measurements on Molybdate Catalysts

Mid-ir diffuse reflectance has proved to be a valuable tool for the study of catalysts and catalytic processes (Van Every et ai, 1981). The reasons are severalfold, including the following:

(1) It is a nondestructive technique. (2) No sample preparation is necessary. (3) It can be used in situ. (4) It is an excellent method for examining adsorbed species on sur-

faces. In particular, the minimal sample preparation and capacity to investigate adsorbed species are of significant advantage to the study of catalysts. An important application of DRIFT measurements made on molybdate cata-lysts is presented herewith.

Catalysts and other inorganic compounds can undergo structural or chemical change when ground with alkali halide powders that are most commonly used for transmission ir experiments. Specifically, solid-state ionic exchange can occur between the alkali halide matrix and the com-pound of interest, broadening or shifting spectral bands. This is brought about by pressure and temperature when sample and matrix are ground


together and pressed into a pellet for transmission measurement. Alkali halides under pressures required to form a clear pellet are actually liquid, increasing the likelihood of ionic exchange.

In contrast, samples prepared for mid-ir diffuse reflectance measure-ment can be run neat or diluted in alkali halide powder, depending on the sample. The latter requires mixing of sample with matrix to minimize reststrahlen effects. This also minimizes the risk of exchange due to pres-sure or temperature. The mixture is then loaded directly into the sample cup for measurement without further preparation (i.e., no pressure for pelleting).

The pressure of grinding inorganic samples in a diluent can cause an-other effect unrelated to the matrix. Certain compounds have several structural phases that interconvert under temperature and pressure. Co-balt molybdate, for example, exhibits this behavior. Depending on stoi-chiometry, cobalt molybdate has at least two phases, designated a and ß (Courtine et al., 1968; Lipsch and Schuit, 1969). The ß form of C0M0O4 is present at ambient temperatures and pressure. In this phase, both Co2+

and Mo6+ are present in octahedral environments. The a form of C0M0O4 forms at temperatures above 300°C and has Co2+ in octahedral environ-ments, whereas Mo6+ is tetrahedral (Svintsov et al., 1975). Because dif-ferent phases of molybdate catalyst possess different catalytic activity, a structural understanding of the catalyst is crucial to a mechanistic expla-nation. For example, in FeMo04, a commercially important catalyst, the selectivity and activity of the catalyst have been directly related to the structure and phase of the catalyst (Pasquon et al., 1973).

The ß form of C0M0O4 is a blue-green compound that when heated above 350°C for 2 hr is converted to a-CoMo04, which at ambient condi-tions is lavender. The color change in this compound is clearly related to the location of water of solvation molecules surrounding the Co2+ ion. As evidenced by color changes, a-CoMo04 rapidly and dramatically under-goes a transition to /3-CoMo04 under pressure. The localized temperature and pressure effects generated by grinding a-CoMo04 in a mortar and pestle are sufficient to cause interconversion.

Because of this equilibrium, preparing a-CoMo04 for transmission ir measurement is very difficult. As soon as the catalyst is ground with KBr, conversion to the ß phase occurs. Moreover, even gentle grinding in Nujol results in change.

In contrast, both the a and ß forms of CoMo04 can be examined by diffuse reflectance FT-IR. The mid-ir diffuse reflectance spectra of ß-CoMo04 and a-CoMo04 are shown in Fig. 4. Samples were prepared for measurement by gently mixing (not grinding) KC1 and the compounds in 10:1 proportions. The mixture was lightly packed into a sample cup, and


I

1100 1000 600 900 800 700 WAVENUMBER (cm"1)

Fig. 4. Mid-ir diffuse reflectance spectrum: (a) a-CoMo04, (b) ß-CoMo04 (500 scans, 4 cm-1 resolution).

the spectra recorded. Mid-ir spectral results show broadening of the molybdenum-oxygen bands at 700 c m 1 , reflecting the difference in structure of the two phases. If the KCl/a-CoMo04 mixture is ground, the diffuse reflectance spectrum is indicative of the change to /3-CoMo04. D. Gerson (private communication, 1984) recorded the photoacoustic spectra of both ß- and a-CoMo04. The spectra (Fig. 5) are very similar to the diffuse reflectance results, reflecting the structural differences be-tween these two phases.

Far-ir diffuse reflectance measurements are equally revealing. When a-C0M0O4 is diluted with polyethylene powder, the spectrum shown in Fig. 6a results. This spectrum is different from that of ß-CoMo04 taken in the same manner (Fig. 6b). The major difference between these spectra ap-pears in the band intensities. For example, the 350 cm- 1 band is signifi-


( I — 5 Γ 1 1 1 1 1 1

I , , 1 1 j 1 1

1100 1000 900 800 700 600 500 400

WAVENUMBER (cm ) Fig. 5. Mid-ir photoacoustic spectrum (PAS) of (a) a-CoMo04, (b) /3-CoMo04 (1000

scans, 4 cm-1 resolution).

cantly stronger in <*-CoMo04. Contrary to the mid-ir, it is possible to carry out far-ir transmission experiments on the a-CoMo04 phase. Be-cause the influence of particle size on absorption spectra is less severe in the far ir, it is possible to prepare compounds by mixing (not grinding) in Nujol. Thus, a- and ß-CoMo04 were carefully mixed with Nujol, and the mulls were thinly spread on a polyethylene sheet.

These transmission measurements resulted in the spectra in Fig. 7. Although bands match quite closely for the two sampling techniques, the intensity of bands below 200 cm"1 is greater for the diffuse reflectance measurement. Band intensity enhancement relative to transmission ex-periments has been noted by the authors and others mentioned in Section V.A in both mid- and far-ir diffuse reflection measurements. When the a-C0M0O4 mull is mixed more vigorously, the far-ir transmission spectrum reveals conversion to the ß form.


I

600 500 400 300 200

WAVENUMBER (cnrT1)

100

Fig. 6. Far-ir diffuse reflectance spectrum of (a) a-CoMo04, (b) /3-CoMo04 (1000 scans, 4 cm-1 resolution).


600 500 400 300 200 100

WAVENUMBER (cm"1) Fig. 7. Far-ir transmission spectrum of (a) a-CoMo04, (b) /3-CoMo04, and (c) a-

C0M0O4 converted to /3-CoMo04 by pressure (256 scans, 4 cm-1 resolution).


Nujol, of course, is a poor medium for catalyst work. In the mid-ir region, in order to avoid severe baseline distortion, it is necessary to reduce particle size by thorough grinding. In the mid- or far-ir region, mixing Nujol with catalyst makes it impossible to study surface-adsorbed species. Although catalysts mixed in polyethylene powder or alkali halide powder yield diffuse reflectance spectra that most closely match the transmission spectra, neat /3-CoMo04 gives identifiable spectra in both the mid- and far-ir regions. If the interest is in surface-adsorbed species, the catalyst can be examined without preparation. Thus, diffuse reflec-tance measurement on catalysts is a preferred ir sampling method for the following reasons:

(1) Catalytic phase transitions induced by temperature and pressure are minimized. Also, ionic solid-state exchange reactions caused by pressing pellets for transmission measurement are not a factor in diffuse reflectance measurements.

(2) Neat catalyst powder can be used for surface adsorption experi-ments. There is no need to press thin self-supporting wafers, which tend to destroy catalytic pore volume and density.

B. Diffuse Reflection in the Study of Zeolites

Diffuse reflection measurements carried out in the ir energy region (10,000-10 cm"1) yield different, yet complementary information about compounds. In catalysts or inorganic compounds, the near-ir region may reveal overtone bands arising from adsorbed molecules. Mid-ir measure-ments give information on the nature of the adsorbate-adsorbant interac-tion. The far-ir region reveals information about the lattice structure of a catalyst as well as low-frequency metal-ligand bonding. Information re-garding pore structure in catalysts may be inferred from far-ir diffuse reflection experiments.

Diffuse reflectance spectroscopy across the total ir energy region has proved to be particularly informative as a probe of a unique class of inorganic compounds, the zeolites. The zeolite family are widely used as catalysts, adsorbants, and even in moderating chemical reactions. Their chemical behavior is in large part a result of a very unique structure (Breck, 1974). The zeolites consist of well-defined aluminosilicate lattices incorporating a variety of cations (Smith, 1963). The exact cation species and specific lattice configuration define an extensive class of compounds. The uniform crystallinity, pore size, channel size, and particle size are the key to zeolite performance. Likewise, the catalytic behavior results from the well-defined structure and wide variety of cations that can be incorpo-


rated in the aluminosilicate lattice. Zeolites are widely used in the petro-leum industry as hydrocarbon "cracking" catalysts.

Of all classes of inorganic compounds, zeolites are among the most widely studied by ir techniques. Transmission ir spectroscopy of zeolites has given valuable structural insights (Lazarev, 1972). Specifically, mid-ir measurements have been used to examine the aluminum-oxygen bonds in these compounds, to define pore vibrations, to show which linkages are structure sensitive (i.e., to define the degree of crystallinity), and to study adsorbed species in zeolites.

Because zeolite powders form thin, self-supporting wafers, most ir measurements were carried out using transmission techniques. More re-cently, near-, mid-, and far-ir diffuse reflectance FT-IR has been used to probe this class of compounds.

1. Near-Infrared DRIFT Measurements on Zeolites

The key to the performance of zeolites consists of the amount and location of water molecules in the lattice. Near-ir measurements have been performed with transmission and DRIFT techniques to study water in zeolite lattices. The transmission near-ir spectrum of 4A-type zeolite is shown in Fig. 8. Major features of this spectrum include bands arising from the OH stretch at 3600 cm-1 and the OH overtone (v\ + v\) OH stretch-bend (vx + v^) combination at 7200 and 5200 cm-1, respectively. The zeolite powder was pressed to form a thin self-supporting wafer. The extreme slope of the baseline is due to particle size scattering phenomena. In contrast, near-ir DRIFT measurement of the sample (Fig. 8) exhibits a flat baseline, and the overtone bands are more clearly observed. It should be noted that these DRIFT measurements were made on neat powders. Reststrahlen effects are not observed on bands arising from absorbed or adsorbed molecules, making DRIFT particularly useful for studying this type of chemical system.

The location of water molecules in the lattice and how they are bound can be studied by near-ir DRIFT measurements. The spectra of 4A zeolite and 5A zeolite are shown in Fig. 9. Both zeolites possess essentially the same crystal structure (i.e., the A-type aluminosilicate lattice); however, the cation, pore size, and channel size are different. 4A-Type zeolite possesses cationic sodium, whereas 5A zeolite has cationic calcium in the lattice. In 4A zeolite, water molecules are loosely bound in the zeolite channel (e.g., note that the OH stretch region is very similiar to that of free water). The DRIFT spectrum of 5A zeolite, however, exhibits a subtle splitting of the OH band at 3300 cm-1. On heating the zeolite at


1 1 1 1 1 1 1 1 1 1 1 ■ 7500 6500 5500 4500 3500 2500

WAVENUMBER (cm"1) Fig. 8. Near-ir (a) absorbance (5A neat, 256 scans, 4 cm ' resolution) and (b) diffuse

reflectance (4A neat, 256 scans, 4 cm ' resolution) spectrum of A-type zeolite.

200°C for 1 hr, we observe that the band at 3450 cm"1 has diminished, but the side shoulder remains and is nearly of the same intensity as before.

This results from OH groups located in at least two sites in the 5A zeolite lattice. Loosely bound water is found in the zeolite channels; it is this water that is driven off under heating, resulting in the disappearance

6 Applications of Diffuse Reflectance Spectroscopy

-i 1 1 1 h

267

2 I

0.0 3900 3750 3600 3450 3300 3150 3000

WAVENUMBER (cm"1)

Fig. 9. Near-ir DRIFT spectra of (a) 5A and (b) 4A zeolite (activated 200°C, I hr; 256 scans, 4cm _ l resolution).


of the OH stretch at 3450 c m 1 . Hydroxyl groups associated with the calcium cations in 5A zeolites are more tightly bound and thus are not driven off under gentle heating. In contrast, if 4A zeolite is heated under the same conditions, there is nearly a complete diminution of the band at 3450 cm-1 due to loss of water. No OH groups are strongly associated with the sodium aluminosilicate lattice.

2. Mid-Infrared DRIFT Measurements on Zeolites

The study of adsorbed species is important because it gives mechanistic insight into catalytic processes. Some work has been carried out using transmission ir techniques to study adsorbed species (Ward, 1976). These measurements are usually made on neat self-supporting zeolite wafers that are thin enough to pass ir radiation. After the wafer is activated by heating in situ, the adsorbant gas is introduced and the cell evacuated to a pressure such that background gas is largely removed without removal of the adsorbed species. Subtraction techniques in FT-IR or direct ratio recording for dispersive instruments are used to extract absorbed-species band information. Although a frequently used method, the scattering of light from the zeolite particles in the wafer can make transmission mea-surements energy-limited. Moreover, when forming the self-supporting wafer, one must be careful not to alter the fundamental properties of the zeolite. The DRIFT technique can be used to overcome some of these difficulties.

The following experiment illustrates the usefulness of the DRIFT tech-nique. For this experiment, a 4A sieve was activated in situ in the heated chamber of the diffuse reflectance accessory. This was accomplished by heating the sieve at 200°C for 4 hr at 1 torr pressure. The completion of activation was indicated by a loss of free water in the diffuse reflectance spectrum. One hundred torr of NO gas was introduced into the cell, and then the cell was evacuated to 10 torr. Diffuse reflectance measurement resulted in the spectrum shown in Fig. 10. The spectrum shows the rota-tional fine structure of NO gas with little or no adsorption on the zeolite. In contrast, the same procedure was carried out on mordenite zeolite in which 60% of the cation sites were replaced with nickel. Diffuse reflectance measurement yielded a spectrum showing a broad NO band shifted in position, along with collapse of the rotational fine structure. This is clearly indicative of NO adsorption on the Ni2+ sites in the zeolite.

The diffuse reflectance experiment was especially easy because no preparation other than packing the sample cup with neat zeolite powder was required. The entire experiment was carried out in the sample cham-ber of the FT-IR spectrometer, and 2 min of data collection yielded the

I

2100 2000 1900 1800 1700 1600 1500

WAVENUMBER (cm-1)

Fig. 10. Mid-ir DRIFT spectra of (a) 4A zeolite and (b) Ni (60% replacement) mor-denite zeolite under 10 torr nitric oxide gas (500 scans, 1 cm-1 resolution).


observed spectra. Again, reststrahlen effects do not appear to influence surface-adsorbed species, and thus the information can be readily com-pared with that from mid-ir transmission experiments.

3. Far-Infrared DRIFT Measurements

The unique properties of zeolites are imparted by their crystal struc-ture. Infrared spectroscopy in the region 50-700 cm- 1 yields significant information about the crystallinity as well as pore and channel structure of zeolites (Flanigan, 1976). Transmission ir results have shown that the bands between 600 and 400 cm - 1 are particularly sensitive to structural change, because they arise from vibrations of the aluminum-oxygen chain.

Specifically, bands at 550 and 375 cm - 1 are sensitive to lattice struc-ture; they arise from vibrations of the secondary building unit of zeolite, the double ring (Wolf and Fuertig, 1966), and the pore-opening vibration (Flanigan et al., 1971), respectively. The band at 465 c m 1 is structure insensitive and arises from bending motions in the primary building unit, the T 0 4 tetrahedron (T = Si4+ or Al3+) (Flanigan et al., 1971).

This is readily shown by diffuse reflectance measurements in the far-ir region. If the zeolite powder is heated above 700°C for 1 hr, the resultant diffuse reflectance spectrum reveals loss of the structure-sensitive bands at 375 and 550 cm- 1 , whereas the other Al—O vibrations remain. The crystal structure has collapsed.

The far-ir spectra of Y- and X-type zeolites were reported (Deuker and Kunath, 1981), and vibrational bands arising from cations on sites in the aluminosilicate framework were identified. The particular zeolites studied were NaY, NaX, LiX, and KX. The diffuse reflectance far-ir spectra of 3A, 4A, 5A, and 13X zeolites are shown in Fig. 11. Band shifts indicate the sensitivity of far-ir DRIFT measurements to the different cations and in these compounds. Types 3A, 4A, and 5A are potassium, sodium, and calcium aluminosilicates, respectively. These spectra were recorded by diluting the samples 20: 1 in polyethylene powder to reduce reststrahlen effects and are similar to transmission experiments on Nujol mulls.

C. Reststrahlen Effects of Alkali Metal Halides in DRIFT Experiments

Fuller and Griffiths (1978b) cited rest strahlen effects in solids when measured by diffuse reflectance mid-ir spectroscopy. Grim et al. (1983) also demonstrated these effects with various substances (caffeine and kaolinite) in the mid-ir region. They showed that with —20% dilutions of

6 Applications of Diffuse Reflectance Spectroscopy

-4—: 1 1 h

271

2 I

500 400 300 100 200

WAVENUMBER (cm"1)

Fig. 11. Far-ir DRIFT spectra of (a) 3A, (b) 4A, (c) 5A, and (d) 13X zeolites (1000 scans, 4 cm 1 resolution).


the solids in KBr, certain peaks appeared to be inverted when measured by DRIFT. That is, for certain bands, one observed a maximum in trans-mission of a percentage of T plot and a minimum in absorbance plot. They could eliminate these effects if the solids were diluted (—1%) with a suit-able matrix. This provides the conditions necessary for obtaining a true diffuse reflectance spectrum, analogous to a transmission spectrum (see Fuller and Griffiths, 1978b; Griffiths and Fuller, 1982).

The emphasis of this chapter has been on the far ir, and we stress reststrahlen effects in this region. The scientific literature is replete with reports of reststrahlen effects occurring for alkali metal halides obtained

1 0 0 . 0

u 5 50.0

CL

Ö.0 5 0 0 300

i*IRVENUMBER (cm"1) Fig. 12 . Far-ir DRIFT spectrum of NaCl powder, 20-40 mesh (neat). BMS

scans, VEL = 1 , 4 cm- 1 resolution. 1,500


by various reflectance techniques, exclusive of diffuse reflectance. The results of Martin and Ferraro (1984) obtained by DRIFT experiments in the far-ir appear to be new, inasmuch as far-ir DRIFT experiments have been explored only relatively recently (A. Rein, 1984, personal communi-cation; B. Chase, personal communication, 1983; Ferraro et al., 1984; Ferraro and Martin, 1984).

Martin and Ferraro examined numerous alkali metal halides by DRIFT in the far-ir region. When neat poly crystalline NaCl was used, for exam-ple, inverse peaks were obtained. Similar results were obtained when deposits of NaCl were made on an aluminum foil surface, constituting a

10.0

u

E 4 .o

en z CL

- 2 . 0 5 0 0

WRVENUMBER ( c m

Fig. 13. Far-ir spectrum (Al foil) of NaCl powder, 40-60 mesh (neat). Beam splitter BMS = 1, 500 scans, velocity VEL = 1 , 4 cm-1 resolution.


specular reflectance experiment. A neat NaCl pressed pellet, window, or single crystal also provided inverted peaks when light was reflected from the surface. The inverted peaks appeared to center on two regions in the far-ir spectrum. A strong low-frequency peak was observed near the transverse optical mode νΊ0 for NaCl (assigned at 164 cm-1 from thin-film experiments; see Mitra, 1969; Jones et al.y 1961), along with a weak higher-frequency band located near the longitudinal optical mode vL0

(calculated at 264 cm-1 for NaCl by the Lyddane-Sachs-Teller equation; see Lyddane et al., 1941). The relative intensities for the two peaks fol-lowed the expected relationship, low frequency > high frequency, similar to results of Berreman (1963) and others.

6 .0

u f 3.0

Ί1 CO Z CL CO

5(5θ «Πίδ 3ÖÖ 2ÖÖ ΠίϊΓ WRVENUMBER (cm"1)

Fig. 14. Far-ir reflectance spectrum of NaCl polished window. BMS VEL = I, 4 c m - ' resolution.

1, 500 scans,


The intense low-frequency inverted peak was always found at higher frequency than the VJO band and was accompanied by several shoulders on both sides of the main peak. The shift to higher frequency was also similar to what Grim et al. (1983) found in the mid-ir for other solids. The high-frequency peak vL0 was observed in a position similar to that ex-pected from calculations made by the Lyddane-Sachs-Teller equation. Figures 12-15 illustrate the spectral results for NaCl. Grim et al. (1983) were able to suppress the reststrahlen effects by dilution and grinding to a fine particle size. Martin and Ferraro (1984) were unsuccessful in normal-izing the inverted peaks even with dilution (to 3%, the limit of detection) and grinding with polyethylene. With other solids, normal DRIFT results

8 . 0

CL G2

f ,0

0.0 5 0 0

HRVENUMBER (cm- 1) Fig. 15 . Far-ir reflectance spectrum of NaCl single crystal. BMS

VEL = 1 , 4 cm"1 resolution. 1, 500 scans,


were obtained, even for neat materials. Neat samples of AgCl, yellow HgO, Fe203, and other materials, all of which have a higher index of refraction η than the alkali metal halides, provide normal DRIFT spectra. More recently, Martin and Ferraro (1984) were successful in normalizing the inverted peaks, but only for a mixed crystal of KBr/KCl (50/50) prepared at 800°C (see Ferraro et al., 1970). This mixed crystal is crystal-lographically a single phase. In the neat state, an inverted peak is obtained (Fig. 16). However, upon dilution in powdered polyethylene (—5%) a normalized peak is observed (Fig. 17). A ground 50/50% mixture of KBr/

3 0 . 0

LÜ U

5 15.0

0 . 0 5 0 0

Fig. 16. WRVENUMBER (cm-1)

Far-ir DRIFT spectrum of KCl/KBr mixed crystals (neat).


KCl representing two phases gave inverted peaks in the neat or when ground in a diluent.

Many factors contribute to the results observed (see Grim et al., 1983). Some of these involve particle size, change of index of refraction η in the absorption region, wavelength of light, absorptivity of the solids, scatter-ing effects, and absorption. When the particle size is large, surface effects predominate. Specular reflectance is obtained rather than diffuse reflec-tance, and the peaks are inverted. These effects become more dominant if

100.0

LU u z: £ 50 .0

CO

CL CC

5(feJ iT5ö 3ÖÖ 2Ö5 lOO WRVENUMBER (cm"1)

Fig. 17. Far-ir DRIFT spectrum of KCl/KBr mixed crystals (diluted in polyethylene).


the solids have high absorptivities and manifest a rapid change in the index of refraction. In contrast, with a reduction in particle size coupled with dilution in a suitable matrix, one obtains the bulk effect and normal DRIFT spectra are obtained. Although why a single-phase mixed crystal provides a normal DRIFT result when diluted with powdered matrix, whereas the neat mixture consisting of two phases does not, is not fully understood. In the case of a mixed crystal (e.g., KBr/KCl), it may result from a suppression or moderation in the change in the index of refraction for the absorption region, which can occur in a one-phase system and not when two phases are involved. Further work on the alkali halides involv-ing a Kramers-Kronig analysis is underway, and a forthcoming publica-tion by Martin and Ferraro (1985) will attempt to provide further discus-sions and results.

D. Identification of Inorganic Species in the Far Infrared by DRIFT

Identifying inorganic species has become increasingly important in, for example, environmental studies (Gendreau et al., 1980; Barbour and Ja-cobsen, 1980). Methods for identification include x-ray crystallography and ir spectroscopy. Most studies using the latter technique, have in-volved mid-ir transmission (Barbour et al.y 1981). Identifying both the cation and anion pair is a challenging task and requires information in addition to that supplied by mid-ir measurements. Adams (1967) and Fer-raro (1968, 1971) indicated the importance of the far-ir region with respect to the characterization of inorganic and coordination compounds. In their work, transmission studies were cited.

For some of the same reasons as discussed previously (Section VI. A), diffuse reflectance is an excellent technique for studying inorganic com-pounds. Because mixtures of inorganic compounds must often be differ-entiated, it is necessary to avoid the additional complication of ion ex-change with the matrix used for pelleting. Moreover, inorganic particles can cause light scattering even in good pellets or mulls, skewing the baseline and hiding minor spectral features. Also, the sample often must be examined nondestructively.

The difficulty of using mid-ir spectroscopy to identify a specific com-pound and not just the anion is a result of the lack of covalency of the cation. For example, the mid-ir spectra of potassium and sodium nitrate are nearly identical. The oxides of iron, Fe203 and Fe304, are particularly difficult to fingerprint by mid-ir spectroscopy. Far-ir measurements of the iron oxides (Fig. 18) reveal far richer spectra, which clearly differentiate the compounds.


5 I

600 450 300 150

WAVENUMBER (cm1)

Fig. 18. Far-ir DRIFT spectra of iron oxides (a) Fe203, (b) Fe304 (500 scans, 4 cm"1

resolution).

For these reasons, far-ir diffuse reflectance appears to be a powerful tool for differentiating inorganic species.

ACKNOWLEDGMENTS

The authors wish to acknowledge Ms. C. Chess, Ms. S. Garlock, and Dr. D. Gerson (IBM) for their assistance in the cobalt molybdate studies, Ms. S. Garlock for her assistance in the zeolite studies, and Dr. M. Gendreau (Battelle) and Ms. R. Barbour (SOHIO) for their work in the area of inorganic speciation. We wish also to acknowledge the contributions of Miss Kathleen Martin for her work in investigating various techniques in the far-infrared regions and for the reststrahlen results in DRIFT experiments in the far infrared. Some of these results have been taken from her Ph.D. thesis (1985) from Loyola University. The


support of the Searle Foundation is acknowledged and greatly appreciated by JRF, Searle Professor of Chemistry.

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CHARACTERIZATION OF ORIENTATION AND LATERAL ORDER IN THIN FILMS BY FOURIER TRANSFORM INFRARED SPECTROSCOPY

J. D. Swalen J. F. Rabolt

IBM Research Laboratory San Jose, California

IV.

Introduction Experimental Considerations

Grazing Incidence Reflection Technique Sample Preparation A. Polymer Films B. Langmuir-Blodgett Monolayers and

Multilayers Reflection at a Dielectric-Metal Interface A. Isotropie Case B. Anisotropie Case Application to the Study of Thin Films A. Langmuir-Blodgett Monolayer

Assemblies B. Polymer Films Summary References

283 286 286 288 288

289 291 291 299 300

300 308 313 313

VI.

I. INTRODUCTION

Structural characterization of submicrometer films has become increas-ingly important because of the major role of these thin films in coatings and laminates for microelectronics. Although a number of techniques have been developed, for example, ESCA, to probe thin films, in many cases they can be destructive, causing changes in the chemical and/or morphological structure. Vibrational spectroscopy, Fourier transform in-terferometry (FT-IR) in particular, is a nondestructive, structurally spe-cific technique that is ideally suited for investigating submicrometer films. In addition, the large throughput of infrared (ir) radiation provided by a Michelson interferometer in conjunction with the high sensitivity fur-


ISBN 0-12-254104-9

284 J. D. Swalen and J. F. Rabolt

nished by cryogenic detectors makes FT-IR an excellent method for de-tecting the small amount of material present in a submicrometer (0.01 to 0.5-μπι) film. In comparison, a dispersion instrument has a diminishing amount of radiation available, particularly in the low-frequency region.

The orientational information provided by ir studies of thin films de-pends on the nature of the interaction between polarized electromagnetic radiation and the film. In a standard transmission arrangement (Fig. 1), the electric field vector is oriented parallel to the surface, and any molecu-lar group having a component of its transition dipöle moment parallel to the surface will absorb some ir photons. In contrast, groups having a component of their transition dipole moment perpendicular to the surface will not interact and absorb much, and their presence in the film or on the substrate will not give rise to a band in the ir spectrum. It is clear that a transmission experiment is sufficient for isotropic samples or those ori-ented in a plane but would not adequately represent the correct concen-tration of molecular groups preferentially oriented at some angle to the surface normal.

One possible solution is to tilt the film relative to the ir beam by a rotation about an axis perpendicular to the direction of propagation, a standard technique used in the visible region. In this way, transition dipoles of groups previously oriented perpendicular to the incoming polarization direction will be so rotated that there is a component along the electric field vector that will absorb ir radiation. With successive increases in the angle of tilt, bands attributed to these groups will continue to increase in intensity. Unfortunately, the maximum intensity representative of the actual concentration of such groups occurs only when the film is rotated 90°. Normally, a steep angle (e.g., 60°) is used, and subtraction of the in-

Grazing Incidence Reflection

Fig. 1. Two methods for obtaining ir spectra of thin films; arrows indicate direction of the electric field component. On reflection from a metal surface, the E vector of the reflected wave for s polarization (upper figure) is almost out of phase with the E vector of the incident wave, and a node results. For p polarization (lower figure), however, the reflected E vector does not completely cancel the incident E vector, and the interference is instead partially constructive.

7 Orientation and Lateral Order in Thin Films 285

plane spectrum gives the perpendicular spectrum. However, this tech-nique is not very accurate, because errors can be introduced in the nor-malization before spectral subtraction.

An alternative method, first suggested by Greenler (1966), is depicted in Fig. 1. It has been referred to in the literature as ir reflection absorption spectroscopy (IRRAS), infrared reflection absorption spectroscopy (IRAS), and grazing incidence reflection (GIR). The last is preferred by the authors because it is descriptive of the conditions of the reflection experiment and does not lend itself to confusion with specular or diffuse reflectance as the other acronyms do. If the polarization of the incoming beam is resolved into two components, one parallel (/^-polarized) and one perpendicular (^-polarized) to the plane of incidence, then as schemati-cally shown in Fig. 1, the ^-polarized component undergoes a phase shift of ~ 180° on reflection (almost independent of the angle of incidence), and hence the reflected beam will destructively interfere with the incident beam, producing a node at the reflective surface. The p-polarized compo-nent, however, experiences some phase change on reflection, varying from nearly zero degrees at small incident angles while approaching 180° at large angles, but the net result is an electric field component polarized normal to the reflecting surface. Thus, when the GIR technique is used in conjunction with standard transmission measurements, polarized ir stud-ies of orientation in thin films become possible, that is, observations at two orthogonal polarizations determine the orientation of the functional groups.

Although studies of thin films by a combination of different reflection and transmission techniques were first reported using dispersive instru-ments, this chapter focuses on the more recent applications using FT-IR. In particular, investigations of solid-metal interfaces and thin films (<200 A) are discussed, whereas gas-metal and liquid-metal interfaces are omitted, because they are treated in detail in Chapter 8.

In addition to GIR and transmission studies of thin films, there have been some applications of attenuated total reflection (ATR) (Sung, 1981; Hobbs et al, 1982; Ohnishi et al, 1978; Iwamoto and Ohta, 1984; Ta-kenaka et al., 1971) to the differentiation of surface structure versus bulk structure using FT-IR. Because the penetration depth of the evanescent wave can be of the order of 2500 to 5000 A, the nature of molecular information provided is actually characteristic of long-range interfacial effects unlike that provided by GIR. Thus, FT-IR-ATR is not discussed in detail, and the interested reader is referred to other studies (Sung, 1981; Iwamoto and Ohta, 1984) that contain an explicit description of this tech-nique.


II. EXPERIMENTAL CONSIDERATIONS

Grazing Incidence Reflection Technique

The GIR technique itself is very straightforward, and in Fig. 2 the optical path of the ir beam in a commercially available (Harrick) acces-sory is illustrated. The sample coated metal slide is placed in a position such that the ir beam strikes it at grazing incidence. The light is then collected and refocused at the position it would have impinged on a sam-ple in the absence of such an accessory. In this way, the sampling stage can be easily inserted and removed without any adjustment of the FT-IR transfer optics.

The procedure for obtaining a spectrum requires the reproducible posi-tioning of the sample (film + metal) and reference (bare metal) slides in the beam path. Shown in Fig. 3 is a photograph of the sampling accessory, which has been designed so that it will slide into the standard mount (A) for sample holders and accessories found in most dispersive and FT-IR instruments. The adjustable thumb screws (B) are used to hold the sample and reference slides interchangeably. In our experience, replacement of the manufacturer-supplied metal screws with their nylon counterparts is recommended because this helps prevent cracking of the slide by over-tightening of the screws. Another modification that is extremely helpful in the recording of spectra of ultrathin films (<100 A) is the addition of a solid plastic bar (C) at the end farthest away from the sample holder (A). This bar tends to remove any slight vibration and stabilizes the accessory platform during changes of the sample slide.

Mirror

Fig. 2. Schematic of GIR attachment.


A number of other factors affect the recording of the FT-IR spectrum of an ultrathin polymer or Langmuir-Blodgett (L-B) film. At the level of sensitivity required to measure a 50-Ä film, a number of precautions can be taken to prevent baseline drift and nonlinearity. This can occur over the time period (1-4 hr) required to obtain a spectrum with satisfactory signal-to-noise (S/N) ratio with a room-temperature DTGS (deuterated triglycine sulfate) detector. A primary cause of background nonlinearity is the thermal instability of the Michelson interferometer, which can un-dergo dimensional changes with fluctuations in the ambient temperature. This problem can be resolved by careful insulation of the interferometer bench coupled with control of the room temperature to within ±2°F. The former can be accomplished simply by evacuation of the FT-IR instru-ment or by the elimination of any temperature fluctuations in a purge gas. A more viable long-term solution is to heat the interferometer assembly a few degrees above ambient temperature. A number of commercial instru-ments employ highly regulated power supplies in conjunction with a se-ries of heaters attached to or embedded in the interferometer optical bench in order to maintain a constant temperature. Instrumental stability over long working periods (8-24 hr) can then be routinely achieved.

Fig. 3. Photograph of commercially available GIR accessory connected to sampling platform that can be inserted into the beam of an FT-IR instrument. (A) Standard sample holder for accessories; (B) nylon thumb screws for quick interchange of sample slides; (C) support bar used to stabilize GIR accessory against vibration.


Baseline drift can be attributed secondarily to long-term fluctuations in the ir source, which can become troublesome at these sensitivities. Source fluctuations (Mertz, 1965) manifest themselves in the FT computa-tion by invalidating the assumption of a linear phase correction with fre-quency. Thus, in the presence of source fluctuations, the computation of single-sided interferograms must include a quadratic phase correction, or the single-beam energy spectrum will exhibit slight to moderate distortion in intensities of bands. In the worse case, the energy spectrum can actu-ally become negative. Dividing two such profiles (one from the sample and one from the reference) to form a transmission or absorbance spec-trum, one would obtain a baseline deviating markedly from linearity. A straightforward solution to such a problem is to use a highly regulated power supply for the ir source. Commercially available power supplies (e.g., Hewlett-Packard model HP6267B) can typically achieve regulation of ±0.02% and can also extend the useful life of an ir source.

A final recurring problem, which has more to do with the sample than with the instrumentation, is moisture adsorption; this is particularly trou-blesome in any film that is hydroscopic and could be a problem in the study of L-B monolayers and multilayers. In L-B films, the multilayer deposition of fatty acids and fatty acid salts from the Langmuir trough can trap adsorbed water molecules between layers. Unless water is removed (usually by placing the film under vacuum), regions of the ir spectrum, most notably between 1600 and 2000 cm-1, can be overwhelmed by sharp bands due to the trapped water molecules. In the authors' experience, the use of an evacuable interferometer is the only long-term solution. How-ever, purging with dry N2 gas for extended periods often produces accept-able results.

When all of these factors have been taken into account, it should be possible to obtain ir spectra of 50-A films by both GIR and transmission techniques with a room-temperature DTGS detector. Examples of such measurements are given in Section V.

III. SAMPLE PREPARATION

A. Polymer Films

The simplest way to produce films is to add a few drops of polymer solution at the desired concentration (10% w/w for 1-μηι films, 0.1% w/w for 200-A films, assuming an intermediate molecular weight of —50,000) to a metal-coated slide on a spinner capable of spinning rates of 2000 to 5000 rpm. Depending on the speed of rotation, and thus the shear rate, some radial orientation of polymer chains may result.


A second method of forming films is referred to as ''doctor blading" and involves moving a knife edge along a substrate to spread a solution. The separation between the knife edge and substrate, called the "wet gap," usually in the range 40-60 urn, can be conveniently adjusted by micrometer screws against Teflon riders in contact with the substrate. A stepping motor drives a plate with a vacuum chuck holding the substrate at ~1 mm/sec past the knife edge. Metal pads in front of and behind the film with thicknesses equal to that of the substrate serve to catch the beginning and final puddle of polymer solution, resulting in more uniform films over the substrate. Care must be taken, because orientation of poly-mer chains can occur in the direction of motion unless this movement is sufficiently slow to allow the relaxation of any oriented chains.

Generally, thinner films are produced by spinning, whereas thicker films (>0.5 urn) can be formed by doctor blading. In the work described in Section V.B, atactic poly (methyl methacrylate) (PMMA) was dissolved in chloroform at a concentration of 0.1% by weight. The metal-coated slide was held in place on the spinner by a vacuum chuck, and the entire metal surface was covered with solution before spinning at 4000 rpm. Thickness measurements were made by the surface plasmon technique (Pockrand et ai, 1978), and the thickness determined on silver was 180 A. Drying in a vacuum oven at 40 to 60°C, when possible, is recommended to remove residual solvent.

B. Langmuir-Blodgett Monolayers and Multilayers

A number of articles and books (Kuhn et al., 1972; Gaines, 1966) pro-vide an in-depth discussion of the structure and preparation of L-B mono-layers, and the interested reader is referred to these for more detail. The discussion that follows is designed to remind the reader about the process of L-B monolayer formation and to emphasize areas where particular care should be exercised in the preparation thereof.

The cleanliness of substrates is crucial for the successful transfer of L-B monolayers. In the case of coated slides used in GIR experiments, those on which fresh metal has just been evaporated are the most desir-able, but those stored in a desiccator flushed with dry N2 (prefiltered to remove H20 and oil) are also usable for up to 2 to 3 days. It is important that the metal surface be kept free of contaminants. Although the same is true for the substrates used in ir transmission studies, they can usually be cleaned in a solvent before L-B deposition. The materials most commonly used in transmission measurements are zinc selenide (ZnSe), silver bro-mide (AgBr), and KRS-5. These have been selected because of their negligible solubility in water and most common organic solvents and their


broad-range transmission characteristics throughout the mid-ir region (400-4000 cm1)·

The spreading and transfer of L-B monolayers onto a hydrophilic sub-strate is schematically depicted in Fig. 4. Although in the past most labo-ratories constructed their own Langmuir troughs, they have become com-mercially available (MGW Lauda, Joyce-Loebl/Vickers) and come with an array of automated features for multilayer deposition. As shown at the top of Fig. 4, the first step is to spread the molecules on the H20 sub-phase. This is usually accomplished by dissolving the material of interest in a volatile solvent (concentration —10% w/w) and then adding a few drops at the water surface. The disordered molecular layer will spread to fill the accessible area as the solvent rapidly evaporates. In the compres-sion stage, a barrier forces the molecules into close proximity and, upon proper choice of the pH and temperature of the subphase, a stable mono-layer phase can be formed. This is certainly an oversimplification of a complex process, and some words of precaution, at this point, can be

Solution Barrier Mo|eCules

: H 2 0 Subphase:;

[tt=3l AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA

COMPRESSION

[ t t r^ - j^

m m m ? m m

Fig. 4. Schematic of Langmuir-Blodgett monolayer deposition. Compression and transfer stages are explicitly illustrated. From Barraud et al. (1979).


instructive. As well as the substrates, experience has shown that the purity of the water can also be critical to the successful formation of L-B monolayers, and only the use of doubly distilled (not deionized) water is recommended. Second, the viscosity of the L-B monolayer is important when it comes to its transferability to a substrate. This is controlled primarily by two factors: temperature and pH. The correct method for determining these quantities is to start at a convenient temperature (~18°C) and record pressure (force/length used in the compression stage) versus area (cross section of a molecule) isotherms, looking for the pH, temperature, and pressure at which this curve has a very steep slope, that is, where further increases in pressure cause very little, if any, additional change in molecular cross-sectional area. The parameter range over which the monolayer transfer should be performed is thus determined. The subphase surface should be cleared of any dust or residual molecular debris, then the substrate submerged (for hydrophilic deposition) in the trough. At this point, the material is spread on the water surface and compressed at the predetermined pressure before withdrawal of the sub-strate begins. The interested reader is referred to the literature cited ear-lier in which specific parameters are given for the formation of L-B mono-layers of selected materials.

An additional variable that can influence monolayer transfer is the dip-ping speed. Typically, speeds of 1 to 2 mm/sec are used, but studies in the literature (Roberts, 1984) report multilayer deposition utilizing speeds of 0.5 cm/sec, provided that the first layer is dipped slowly and the subphase surface is immediately cleaned off before rapid deposition takes place.

IV. REFLECTION AT A DIELECTRIC-METAL INTERFACE

A. Isotropie Case

To observe the spectrum of thin layers of molecules on a surface, the addition of many FT-IR scans is generally required because of the small number of molecules present. Greenler (1966) was one of the first to show that, on highly reflecting metals, the ir spectra recorded at glancing angle of incidence lead to a field enhancement for the radiation polarized per-pendicularly to the surface, as discussed previously.

The reflectivity of a layered structure can be found from the Fresnel equations (Heavens, 1965; Born and Wolf, 1975; Swalen, 1979). These equations for anisotropic and absorbing systems of more than three layers are complicated and complex, and consequently, formulating them in terms of transmission matrices relating the optical fields in one layer to those in another offers many advantages.


The two Maxwell's equations, those involving the curl of E or H,

V x E = -dB/dt (1)

V x H = SDIdt, (2)

lead to relations among the various field components. As mentioned pre-viously there are two polarization directions that need to be considered: the case when the optical electric field vector of the electromagnetic wave is perpendicular to the plane of incidence, labeled s {senkrecht), TE (transverse electric), or 1 , and that when the optical electric field vector is in the plane of incidence, labeled p (parallel), TM (transverse mag-netic), or ||.

Let us first consider the case of ^-polarized (TE) radiation. The axes are oriented such that z is normal to the surface, x is at the intersection of the plane of the surface and the plane of incidence (i.e., in the forward propa-gating direction), and y is perpendicular to the plane of incidence. Here we have one component of the E field, Ey, perpendicular to the plane of incidence, and two components of the H field, both in the plane of inci-dence, Hx and Hz. From the x component of (1) we can relate Hx to Ey for a traveling wave e

i(ü)t~kzz~kxx) with a circular frequency ω = 2πν, and a propagation vector k in the xz plane:

(V x E)x = -dEy/dz = ikzEy = -ίωμΗχ (3)

or

Hx = -kzEy/ωμ (4)

where μ is the magnetic permeability. We assume that μ = μ0 , the mag-netic permeability of free space, because our films are nonmagnetic. In a similar manner from the z component of (1) we can relate Hz to Ey:

(V x E)z - dEy/dx = -ikxEy = -ιωμΗζ (5)

or

Hz = kxEy/ωμ (6)

Then from the y component of (2) we have the Pythagorean relation among the components of k along the x and z axes:

(V x H)y = dHJdz - dHjdx = -ikzHx+ ikxHz = ia>eyEy (7)

and with (4) and (6) becomes

kx + kz = ω2μεγ = k2 or k] = eykl - k] (8)

where k0 = 2π/λ = l/V/x0€o a n d is the propagation vector for free space. Now matching the E and H fields at an angle of incidence Θ at the interfa-


cial boundary, the Fresnel reflection and transmission coefficients can be derived. From the incident field /, the reflected field r, and the transmitted field t, we can then write for the E field

X-^yi "T tLyr tLyt \7)

and correspondingly for the H field we have

Hxi + Hxr = Hxt (10)

This equation can then be written in terms of Ey by using (4) and assuming that the materials are nonmagnetic (i.e., μχ = μ2):

Since kzr = -kzi and from (9) and (11) we can write the reflectivity r and transmissivity t,

r = EyrIEyi and t = EytlEyi (12)

as

1 - ßs 2 r = TTp and t = TTp W

where ßs = kztlkzi. The superscript s on the ß denotes s polarization. See Eq. (27) for the corresponding ß for p polarization, labeled ßp. For ab-sorbing materials ß is complex. Normally these Fresnel equations are given for real constants (i.e., nonabsorbing media). Then

kZi = hrii cos 0/ and kzt = hm cos 0, (14)

so that

and

rti cos 0/ - nt cos 0/ , . .,, r = - T L —— i and R = \r 2 (15)

rii cos 0/ + nt cos 6t ' '

Irii cos 0f /i rcos 0, |^l?

A2/ COS 0/ + nt COS 0/ Λ/ COS 0/ ' '

The factor in the transmissivity T is introduced to satisfy conservation of energy per unit area across the interface (Born and Wolf, 1975).

In a similar manner the case of radiation p polarized (TM) can be treated. We now have E and H components interchanged. There is one component of the H field, Hy, perpendicular to the plane of incidence, and there are two components of the E field in the plane of incidence, Ex

and Ez. From the x component of (2) we can relate Ex to Hy:

(V x H)* = -dHy/dz = ikzHy = i(oexEx (17)


or

Ex = kzHy/a>ex (18)

Now from the z component of (2) we can relate Ez to Hy:

(V x H)z = dHy/dx = -ikxHy = i(oezEz (19)

or

Ez = -kxHy/(oez (20)

The y component for Maxwell's equation (1) gives one more relation:

(V x E)y = dEjdz - dEJdx = -ikzEx + ikxEz = -ίωμΗ}, (21)

or

^ / e z + k>z/ex = *g or 14 = exk% - exk2x/ez. (22)

Note that the z component of the propagation vector kz for TM waves involves two orthogonal dielectric functions, ex and ez. Frequently, films by their very nature of formation become anisotropic, especially between the in-plane direction and the direction normal to the surface. Hence these two e's can be different. The importance of these kz vectors [see (8) and (22)] is that progressing in the z direction they give the exponential factors for the electric and magnetic field across a thin film structure.

Again matching the E and H fields for a traveling wave at an angle of incidence Θ at the interfacial boundary, the Fresnel reflection and trans-mission coefficients can be derived for p polarization. For the x compo-nent of the E field we have

Exi + Exr - Ext (23)

This can now be written in terms of Hy by (18):

kziHyi/exi + kzrHyr/exr = kztHyt/ext (24)

and correspondingly for the H field we have

Hyi + Hyr = Hyt (25)

Since kzr = —kzi and exi = exr, the reflectivity r and transmissivity t can be written from (24) and (25):

r = HyrlHyi and t = HytlHyi (26)

r = \rfP and t = T-h where ^fefe (27)

As before with s polarization these equations for real constants (i.e., nonabsorbing media), are


nt cos 0,· - rii cos 0, nt cos 0, + rii cos 0, and /? = \r\2 (28)

2/1, COS 0/ t _ fl; COS 0, . ,. f = TTT ■ z" and T = - i\t\2 (29)

Ht COS 0/ + A2; COS 0, fl, COS 0/ ' ' The factor in the transmissivity T introduced to satisfy conservation of energy per unit area across the interface (Born and Wolf, 1970) now includes the factor

^ · ♦ A f ^ —7= instead or —7=

Note that several authors (Heavens, 1965; Born and Wolf, 1975) calcu-lated r and t from the optical electric vectors for both s and p polariza-tions. We have done it here with the E vector for s polarization and the H vector for p polarization, a more symmetrical treatment. Nevertheless, the results for r, R, and T are identical, as it must be. The complex transmissivity t contains a factor njrii because the ratio is between mag-netic fields instead of electric fields.

In the case of a three-layer system with layers 1, 2, and 3, the optical field in the first layer will vary as

-ik z M {Kl2 + r 2 3 * ~ 2 f 6 ) ik z <™Λ

e lkz\z + -T—■ =äf e u (30) 1 + r\2r2?e~2tS

where rn and r23 are the reflectivity coefficients at the 1-2 interface and 2-3 interface, respectively. The phase shift through the middle layer is labeled δ (= kz2 di). The first term is the incident field without the scaling factor (i.e., the magnitude of the field). The second term is the reflected field traveling in the opposite direction with the total reflection factor rt:

(r12 + r23e-m) 1 + rnr23e-

rt = t o. . . . . . .„-ra (3D

For the middle layer, 2, the field has the form

t\2(e-ikz2* + r23e-2i8eikz2*)

1 + rl2r23e -2/δ (32)

Here t\2 is the transmission from the first layer to the second. The third layer, 3, includes the two transmission coefficients and the phase shift δ in passing through the middle layer. The denominator in all the expressions results from the summation of the multiple reflections in the intermediate layer:

/ l 2 ' 2 3 ^ ' 6 e-ik„<z-4) (33)

1 + n2r23e~m e ' K>


The interface between layer 1 and layer 2 is frequently defined as z = 0. Since the kz's, r 's , and f's can each be complex, these expressions must be evaluated with complex arithmetic.

The extension to a multilayer system of n layers is straightforward, but unfortunately algebraically quite complicated. If the optical constants for each layer are cast in a matrix form, the total reflectivity and transmission coefficients can be generated by matrix multiplication. The general form of this matrix was written in a convenient form by Heavens (1965), but other authors (e.g., Born and Wolf, 1975) have written similar expres-sions:

Εΐ)=λ(ί η2)λ( e*2

1 / ei8J rjkei8j\

X — I X ·

*jk \ rjke'ibj e~i8J /

or more compactly as

0 Here in (34) and (35) the optical field components are the optical electric vectors for s polarization. For p polarization merely replace the optical electric vectors E by the magnetic vectors H. The components of the electric field can be found by the relations (18) and (20). The first row of the matrix product M is for the forward-propagating wave and the second row is for the backward-propagating wave. The matrix product of the layers multiplied on the right by the transmitted wave gives the incident and reflected wave. Normally the backward-propagating wave in the last layer, Ε^ , is zero because there is no source of radiation coming from behind the multilayer system. The composite reflectivity coefficient fol-lows from the complex division of the 2,1 element by the 1,1 element of the product matrix:

r = M2fi/Mu (36) For a three-layer system,

_ ri2ei82 + r23e~i82

" " ei82 + rl2r23e~i82 <37)

which can be made identical to (31) by multiplying both numerator and denominator by e~i82. The 1,1 element of the matrix product, M u , ac-counts for the multiple reflections in each layer as the denominators in

r23ei82^

e~i82 )

vE" (34)

M VE;

(35)


(30) to (33) for one intermediate layer. The composite transmission coeffi-cient follows from the complex product of each individual transmission coefficient divided by the 1,1 element of the matrix product:

t = Π tjj+\M\,i (38)

For each layer the optical field can be evaluated from the inverse of the matrix product of matrices (34) from the first layer to thej th layer multi-plied by the vector E^ l rt), where rt is the total reflectivity of all the layers:

This inverse of the matrix product from the first layer to theyth layer is labeled as Mfj . The term Ei times the column vector (1 rt) follows di-rectly from the left-hand side of (34), where Ej is the forward-propagating wave Ej*" , and the backward wave Ef is the reflected wave rt · E{. In the same manner as (30), (32), and (33), the first element, the wave propagat-ing in the forward direction, is multiplied by e~ikzjz and the second ele-ment, the backward-propagating reflected wave, by eikzjz to obtain the z dependence in the intermediate jth layer:

Ej(z) = Efe-ikzjz' + E / A ' 2 ' (40)

where z' = z - Σ-jlJ dt. With these methods, the field for a unit incident wave on a metal sur-

face overcoated with a thin absorbing dielectric film, which is typical for our polymeric films, was calculated (see Rabolt et al., 1985). In Fig. 5 is shown, for example, one of many field configurations that have been calculated for comparison with experiment. If the field intensity (<*E2) in the middle of the overcoat is plotted as a function of incident angle (Fig. 6), one obtains a broad maximum centered around 80°. An alternative method of comparison is to plot the reflectivity ratio (i.e., the change in calculated reflectivity with an overcoating, with and without absorption, divided by the reflectivity without absorption), as done by Greenler (1966). In Fig. 7, this quantity, (R0 - R)/Ro, is plotted as a function of angle of incidence, and in this case the maximum occurs at 88°. Unfortu-nately, it is not very convenient experimentally to reach this sharp glanc-ing angle. When one is using a dispersion instrument with a beam defined by a slit, it might be possible to spread the slit with the short dimension extended along the film. With an FT-IR interferometer, however, the beam is generally more circular. At angles much less than glancing, the


3

2 CN LU

1

0 200 400 600 800 1000

Distance (Ä) from Silver Surface

Fig. 5. Square of the optical electric field plotted as a function of distance from the silver surface for a glancing angle of 80°. Note the small evanescent tail, the skin depth into the silver. The field in the PMMA is reduced from that in air by its dielectric function, a consequence of the solution of Maxwell's equations. The optical field in air is a sinusoidal curve approaching a node some distance from the surface.

beam does not fill the whole sample but covers only w/(l cos 0), where w is the beam width, / the film length, and θ the grazing angle of incidence. In contrast, when the glancing angle approaches 90°, part of the beam cannot irradiate the sample and is lost. The amount of radiation on the film is now (/ cos 6)/w. Clearly, the optimum is when this expression (as well as its inverse) is equal to unity. The lower curve is the product of this radiation expression and reflectivity ratio given in the upper curve. Notice how the experimental points measured for the 1732 cm"1 C = 0 stretching vibra-

2.0

1.5

ΐ , 1.0

0.5

50 60 70 80 90 Grazing Incidence Angle (deg)

Fig. 6. Square of the optical field at the center of the PMMA layer as a function of grazing incidence angle.

Silver PMMA Air

J I I L


0.10

0.08

0.06

o c

0.04

0.02 w ' w 50 60 70 80 90

Grazing Incidence Angle (deg) Fig. 7. Reflectivity ratio as a function of angle for a silver film (n = 4.15 + 42.6/)

overcoated with a dielectric (n = 1.4 + 0.13/) compared with a dielectric with no absorption, R0. This is essentially Greenler's (1966) curve. The lower curve includes the factor to account for the percentage of sample irradiated; at shallow angles, the ir beam irradiates only some fraction of the film, and at very large angles, some part of the beam misses the sample. See the text for details. Our experimental points are shown as circles. The point at 84° was determined from 6000 scans over a period of 4 to 6 hr. The signal became too weak at larger angles, making further measurement times unrealistic.

tion of PMMA follow the theoretical curves very well. They should follow the upper curve but, for example, the last point at 84° took more than 4 hr (6000 scans) to be recorded with sufficient S/N. Nevertheless, the signal is still increasing but requires longer and longer time to collect as the angle is increased. Operationally, it seems that -80° is optimal. Here, the optical field is high, the change in reflectivity is still reasonable, and the sample can be bathed in sufficient radiation so that one need not sum a large number of scans, requiring an inordinate amount of time to obtain a spectrum with reasonable S/N.

The reflectivity of the metal can also influence S/N. In Fig. 8, the reflectivity ratio of chromium is compared to that of silver. Although the reflectivity of chromium is only ~5% less than silver, which is a very good reflector, the reflectivity ratio plotted in Fig. 8 is ~ i . Also, the maximum is shifted slightly toward less glancing angles. Clearly, one desires a sur-face with the highest reflectivity, but bonding interactions with many other surfaces are still observable, possibly requiring patience on the part of the spectroscopist to collect more data.

B. Anisotropie Case

The discussion so far has involved isotropic films, the usual case for most polymeric films, in which the anisotropy can be quite small and the


0.10

0.08

a- . 0.06 I O

cc I 0.04

0.02

0.00

50 60 70 80 90

Grazing Incidence Angle (deg)

Fig. 8. Reflectivity ratio plotted for silver {h = 4.15 + 42.6/) and chromium (n = 3.8 + 14.70 for the ir region 4-6 μπι, the refractive indexes being approximate values ( , silver; ---, chromium).

films treated as isotropic. For some films, however, this is not possible because, for example, polyimide films (Russell et al., 1983) and many L-B films are quite anisotropic. Chollet et al. (1976), Chollet (1980), and Chollet and Messier (198?) considered this problem and showed how one can determine the orientation of functional groups from the ir spectrum as a function of angle. The theory follows from that given in Born and Wolf (1970) and in Landau and Lifshitz (1960) for the solution of the Fresnel equations in optical materials with birefringence. The reader is also re-ferred to the equations given by Philpott and Swalen (1978) for the fields in a three-layered system with a uniaxial crystal (the unique axis being at some arbitrary angle in the xz plane) as the third layer. Merely by moving the uniaxial layer to the second layer and changing the wavelength to the ir, one can apply these equations directly to the anisotropic overcoating on a metallic substrate. Basically, in birefringent materials, the direction of energy flow and the direction of propagation are not collinear, so that angular determinations are more complex.

V. APPLICATION TO THE STUDY OF THIN FILMS

A. Langmuir-Blodgett Monolayer Assemblies

1. Saturated Fatty Acids

A considerable number of studies have appeared in the literature in which dispersive ir spectroscopy (Chollet, 1978, 1980; Chollet etal., 1976; Chollet and Messier, 1982; Kajzar and Messier, 1981; Knoll et al., 1982;

T I I I I I I Γ


Golden et al., 1982) has been used to investigate the orientation of the aliphatic chain component of L-B monolayer assemblies relative to the substrate surface. Unfortunately, one of the initial problems encountered was the lack of signal from such a small amount of material, and a number of different techniques, including ATR (Ohnishi et al., 1978; Takenaka et al., 1971) and multiple reflection (Francis and Ellison, 1959) have been needed to obtain spectra of multilayers with sufficient SIN. In addition to the inability to obtain independently polarized ir measurements (i.e., a spectrum with E polarized parallel to and another with E perpendicular to the monolayer surface), more than 30 multilayers were minimally re-quired to obtain an acceptable spectrum.

With the application of FT-IR spectroscopy to L-B films, studies utiliz-ing GIR began to provide information about molecular orientation in as-semblies (Allara and Swalen, 1982; Knoll et al., 1982) of 1 to 10 mono-layers. In the case of cadmium arachidate, Cd[OOC(CH2)i8CH3]2, Allara and Swalen determined, using GIR and a cooled MCT (mercury cadmium telluride) detector, that the aliphatic tails were oriented to within 5° of the normal to the surface. Earlier measurements (as discussed later) on the acid form had reported tilts ranging from 25 to 35° with respect to the normal. Thus, this indicated that the size, valence, and bonding of the head group could dramatically influence the orientation of the hydrocar-bon tails. Allara and Swalen also determined that the absorbance of the symmetric C0 2 stretching vibration varied linearly when 1 to 10 mono-layers were sequentially placed on silver, indicating that the field was still linear at 300 A from the reflective surface. (See Fig. 5 with only a slight falloff in field.)

In a more recent study (Rabolt et al., 1983), insight into the different packing structures of arachidic acid and its cadmium salt was provided by GIR in conjunction with standard transmission measurements. The spec-tra of L-B monolayers of the cadmium salt obtained in two different polarizations using a room-temperature DTGS detector are shown in Fig. 9. The upper spectrum with E perpendicular (GIR) to the surface contains bands that can be assigned to molecular groups whose transition dipole moment has a component perpendicular to the surface. By comparison with the spectrum of Fig. 9b with E parallel to the surface, it becomes apparent that the aliphatic tails are nearly perpendicular to the surface, confirming the results of earlier studies. Table 1 contains a listing of both parallel and perpendicular bands, together with their assignment. On closer inspection of the transmission spectrum (Fig. 9b), it is clear that there are two sharp bands of similar intensity located at 1474 and 1462 cm- 1 , which can be assigned to —CH2— bending vibrations. Similarly, although not explicitly shown, there are two components of the —CH2— rocking vibration found at 719 and 731 cm- 1 in the low-frequency region.


0.04

(a) ,S(CH3)

" a (CH s ) l \ ^ C H 2 > ^S(CH2)

3200 2800 _ L

2400 2000 1600

0.06

0.01

2.25

3200 2800 2400 2000 1600

0.25 3200 2800 1600

1200

I (b) "a<CH2>

I L ^a( C H3)l l j I I I

^S(CH2)

^ . 1 1 I 1

1 i i i i

*a(C02)

—_Jl_J i i i i ~~i ".

1200

1200 2400 2000 Wavenumber (cm"1)

Fig. 9. Infrared spectra of cadmium arachidate. (a) Six monolayers on silver with E normal to substrate, (b) 18 monolayers on silver bromide with E parallel to substrate, and (c) isotropic bulk sample in KBr pellet with E unpolarized. Here δ is the CH2 bending vibra-tional mode, y is the CH2 twisting vibrational mode, w is the CH2 wagging vibrational mode, and a refers to the α-carbon atom adjacent to the carboxylate group.

Both sets of splittings are caused by crystal field interactions in the unit cell. Band splittings in these regions have been used extensively to char-acterize subcell packing in ft-alkanes and polyethylene (Hendra et aL, 1977). The presence of two components of both the —CH2— bending and rocking vibrations is indicative of two molecules per unit cell with an orthorhombic subcell packing of the —CH2— groups at the interior of the crystal. Two crystal modifications of Ai-alkanes that have orthorhombic subcell packing are possible. If the chains pack so that the methyl end groups lie in the same plane and the chains are perpendicular to the crystal surface, then the structure of the macroscopic crystal exhibits orthorhombic symmetry. If, however, the chains pack such that the methyl end group of one chain is adjacent to the (n - 2)th CH2 group of the adjacent chain, that is, the chains are slid past one another by a length of 2.54 A (corresponding to twice the projection of C—C bond on the chain


TABLE 1

Band Assignment for Cadmium Arachidate Monolayers

Band frequency (cm_1)fl

E±

2962m —

2931m 2918m

2874m 2851m 1554sh 1544sh

1450_l460sh 1433vs

1400w 1380w 1363w 1345w 1332w 1320m 1304w 1288m 1270w 1254w 1238w 1219w 1108w 993w

£||

— 2954m

c

2919vs 2895w

— 2850vs 1552shl 1541s J 1474m 1 1462m J

: } 1424w 1401sh

— -— — — — — — — — —

= } 731ml 719m J

Assignment6

â(CH3) in skeletal plane â(CH3) out of skeletal plane *S(CH3) â(CH2) Fermi resonance of i^s(CH2) +

binary combination of 6(CH2) ^S(CH3) *S(CH2)

â(C02)

6(CH2) split by crystal field

^S(C02)

— ö(CaH2) Ö(CH3)

w(CH2) y(CH2)

KCC)

r(CH2) split by crystal field

a vs, Very strong; s, strong; m, medium; w, weak; sh, shoulder. b v, Stretch (a, asymmetric; s, symmetric); δ, bend; w, wag; γ, twist;

r, rock; Ca, carbon atom adjacent to carboxylic acid group. c Should be observed in E\\ spectrum but is obscured by overlap with

strong band at 2919 cm-1.

axis), then the chains are inclined relative to the crystal surface and the crystal modification is monoclinic. In the L-B monolayers, there is the additional constraint, shown in the bottom of Fig. 4, that the head groups lie in contact with the surface. This dictates that, for the monoclinic


modification, the chains will be tilted at an angle of —29° with respect to the substrate normal, whereas in the orthorhombic case the hydrocarbon tails will be perpendicular to the substrate. On the basis of the polarization measurements shown in Fig. 9, it is apparent that the chains are perpen-dicular to the substrate and thus must pack in the orthorhombic crystal-line modification similar to that illustrated in the bottom left of Fig. 4. It is possible to extend this analysis to previous studies (Takenaka et al., 1971; Ohnishi et al., 1978; Chollet, 1978) in which fatty acids were transferred as L-B films. In all cases, a tilt of 25 ± 4° relative to the surface normal was reported for the hydrocarbon tail. On the basis of our earlier discus-sion, this suggests that the free acid L-B monolayers form a structure belonging to the monoclinic crystalline modification.

Finally it should be mentioned also that Raman spectra of L-B films of cadmium arachidate have been observed by Knoll et al. (1982) in reflec-tion, but because the Raman scattering cross sections for a saturated aliphatic chain are quite small some enhancement was required. They used the high optical fields generated at a metallic grating surface from surface plasmons and qualitatively confirmed the orientation of the meth-ylene groups.

2. Fatty Acid Monolayers Containing Diacetylene Units

In spite of the attractiveness of L-B films composed of fatty acid chains as two-dimensional ςςsingle crystals," it soon became evident that the utility of such films was limited because of their rather fragile nature, and this restricted their technological usefulness to all but the simplest appli-cations.

Attempts to circumvent this problem eventually led to the incorpora-tion of reactive moieties into the fatty acid backbone followed by topo-chemical polymerization of the L-B monolayers by exposure to uv, x-ray, or y radiation. Although the first molecules polymerized contained vinyl end groups (Puterman et al., 1974), the most common systems under investigation are those that contain diacetylene units (Tieke et al., 1976; Hupfer et al., 1983) incorporated into the hydrocarbon backbone. After polymerization, these L-B monolayers exhibit remarkable structural in-tegrity, because the van der Waals intermolecular interactions between the monomer chains have been replaced by a network of covalent bonds forming a polymer backbone in the plane of the L-B film.

Numerous characterization studies of the long-chain diacetylene com-pounds in both bulk and monolayer form have been reported in the litera-ture (Tieke and Lieser, 1982). Although much information concerning the structure of L-B films has emerged, the question of orientation of mono-layer components relative to the substrate surface has been addressed only recently (Tieke et al., 1977, 1979, 1983).


In addition to x-ray and electron diffraction, vibrational spectroscopy has shown considerable promise as a characterization technique for L-B monolayers and multilayers. Resonant Raman spectroscopy (Tieke et al.}

1982) has elucidated the conjugated structure of the monomer and poly-mer backbone but has been of limited use in determining the structure and orientation of the —CH2— sequences, because their Raman scattering cross section is not resonantly enhanced. Thus, in a typical resonance Raman spectrum of a conjugated structure, no bands attributable to —CH2— groups are observed. In contrast, FT-IR has been shown to exhibit monolayer sensitivity, as described previously, and polarization measurements should elucidate the orientation of the monolayer compo-nents relative to the substrate.

Shown in Fig. 10 are the GIR (E±) and transmission (E{) spectra of L-B monolayers of the cadmium salt of hexacosa-10,12-diynoic acid, often referred to as the 13-8 diacetylene. The presence of bands at 1535 and 1443 cm-1 attributable to ^a(C02) and vs(C02), respectively, indicate that the cadmium salt was transferred, and not the free acid. In contrast to that

-0.005 3200 2400 1600

Frequency (cm 1) Fig. 10. Polarized ir spectra of cadmium salt of hexacosa-10,12-diynoic acid; Upper

trace: five monolayers on silver (E± surface); lower trace: six monolayers on KRS-5 sub-strate (E\\ surface).


TABLE 2

Band Assignment for Cadmium Salt of Hexacosa-10,12-diynoic Acid

Band frequency (cm-')

E±

2962m

2933ssh 2923vs

2877m 2853ms

1555δηΊ 1535s J 1467sh 1443vs

1335m

E\\

— 2950wsh 2932wsh 2923vs 2898sh

— 2853vs 1732w

1535s

1468msh —

1420mbr 1335vw

Assignment^

νΆ (CH3) in skeletal plane v.A (CH3) out of skeletal plane s (CH3)

νΆ (CH2) —

s (CH3) s (CH2)

v ( C = 0 ) , free acid

νΆ (C02)

Ö(CH2) ^S(C02)

— w(CH2) + y(CH2)

a vs, Very strong; s, strong; m, medium; w, weak; sh, shoulder; br, broad.

b v, Stretch (a, asymmetric; s, symmetric); δ, bend; w, wag; γ, twist.

observed for cadmium arachidate in Fig. 9, there is no clear separation of band intensities in either the CH or C02 stretching regions of the two polarized spectra, as would be expected if the hydrocarbon tails were oriented normal to the substrate surface. In the region 2800-3000 cm 1 , the intensity of methyl group vibrations at 2962, 2933, and 2877 cm"1 is considerably diminished in the lower spectrum, where E is parallel to the surface, indicating that their major change in dipole moment is perpendic-ular to the surface. This would suggest that the end groups are approxi-mately normal to the surface and would imply that the hydrocarbon seg-ment containing the methyl group is oriented likewise. Hence, the majority of the contribution to the —CH2— stretching vibrations at 2923 and 2853 c m 1 when E is parallel to the surface (Fig. 10, lower spectrum) could be from that segment containing 12 —CH2— groups whose change in dipole moment during the CH stretch would be predominately parallel to the substrate. In a similar way, the band at 1468 cm-1 in the lower spectrum is attributable to the —CH2— bending vibrations of this seg-ment whose change in dipole moment would likewise be parallel to the surface. An important ramification of the fact that only a singlet at 1468


cm"1 occurs in the spectrum is that it obviates any suggestion of the close packing of —CH2— chains in an orthorhombic lattice as is known to occur in odd «-alkanes and polyethylene. As discussed in the previous section, such a packing would consist of two chains per unit cell and would give rise to crystal field splitting of the —CH2— bending vibrations and a sharp band intensity pattern assignable to wagging and twisting vibrations of—CH2—groups in the region 1200-1400 cm"1 (see cadmium arachidate spectrum). The absence of such splitting and a lack of sharp wag and twist bands indicates a unit cell containing only one chain, as must be the case for the 13-8 diacetylene L-B monolayers.

An interesting contrast exists for the orientation of the 8 —CH2— groups between the diacetylene unit and the carboxylate group. The ap-pearance of the va(C02) vibration at —1535 cm-1 in both polarized spectra and the presence of intense —CH2— stretching vibration components in the spectrum obtained with E perpendicular to the surface suggests that the short —CH2— segment and the attached carboxylate group are not perpendicular to the surface but inclined such that these bands result from a sizable component of the change in dipole moment both perpendicular and parallel to the substrate surface. It is interesting that, in both these vibrations, it is the asymmetric stretching component that shows up with sizable intensity in the spectrum with E perpendicular to the surface. The role of domain formation on the macroscopic scale in these films in the disordering of diacetylene chains caused by defects has not been consid-ered, and its effect on the ir spectrum is as yet unclear.

On irradiation of the 13-8 diacetylene monolayers with a low-pressure mercury lamp (254 nm, 24 mW/cm2) in an N2 atmosphere for times of up to 40 min, the "red" form of the polymer was formed, and the resulting ir spectra shown in Fig. 11 were obtained. The spectral changes are rather dramatic compared with those observed for the 13-8 diacetylene mono-layers. However, as seen by comparing the upper and lower spectra of Fig. 11, the band intensity patterns are similar, unlike that observed for the monolayers. This suggests a reorientation of both hydrocarbon seg-ments to accommodate the spatial restrictions afforded by topochemical polymerization. A more detailed analysis including specific band assign-ments is the subject of a comprehensive report (Rabolt et al.y 1985). It is sufficient to conclude from Fig. 11 that the polymerization process has caused a significant reorientation of the —CH2— chains such that now their contributions to both spectra are similar. In addition, bands attribut-able to the creation of double bonds by the polymerization process can be seen to contribute to the broad features in the region 1500-1700 cm 1 . It is interesting to note that the spectra after uv irradiation were considerably weaker in the CH stretching region in both polarizations, indicating the


0.050

0.035

0.020 3200 2800 2400 2000 1600

Frequency (cm 1 ) Fig. 11. The GIR (EJ and transmission (En) measurements of polymerized hexacosa-

10,12-diynoic acid. Polymerizations were carried out under N2 using a low-pressure mercury lamp (λ = 254 nm, 25 mW/cm2) for periods of 40 min. Spectra were recorded at 2 cm"1

resolution.

possibility that the irradiation process may have caused a degradation of the monomeric chain length in addition to polymerization.

B. Polymer Films

1. Amorphous Polymers

Many of the coatings and coating processes currently in use employ polymers that, because of their stereochemical configuration, cannot crystallize. These amorphous polymers are thus very important commer-cially, because they exhibit excellent mechanical and dielectric properties over a wide temperature range. The orientation of locally ordered seg-ments when these polymers are deposited on a solid surface is of particu-lar interest, because the relaxation of this local order may be a contribut-ing factor to the physical aging process. Current theories are concordant with the view that the onset of physical aging is accompanied by changes in local conformation. Hence, the use of structurally specific techniques (e.g., NMR) have become increasingly important for the characterization of this molecular conformation.


0.070

0.035

k <\~ Copper

Aluminum I n i U I I I I I I U I I I

Chromium

0.000

l yw ^s^s^J

_L _L 3200 1600

- 1 \

_L 800 2400

Frequency (cm- 1)

Fig. 12. The GIR spectra of 180-A PMMA on silver-, copper-, aluminum-, and chro-mium-coated substrates (2 cm ' resolution, 1000 scans).

FT-IR has also shown considerable promise for studying changes in bulk amorphous polymers, but the area of thin films seems to have been overlooked because of the inherent experimental problems mentioned previously.

In order to demonstrate the sensitivity of GIR for obtaining ir spectra of thin polymer films, PMMA was spin-coated on silver, copper, aluminum, and chromium (—1200 A), which had been evaporated onto glass sub-strates (Rabolt et al.y 1985b). Figure 12 contains the GIR spectra of 180-Ä PMMA (atactic) films on these four metals recorded at the same grazing incidence angle. It is obvious that a change in the SIN ratio of these spectra occurs in going from the silver to the chromium-coated sub-strates. This is especially evident in the 3000 cm- 1 region, where bands attributable to —CH2— stretching vibrations are found. In addition, the extent of signal loss is further exemplified by the falloff in peak absor-bance of the 1732 c m 1 C = 0 stretching mode by a factor of 2 in going from silver to chromium. The origin of this spectral degradation can be


better understood on consideration of Fig. 8, where the reflectivity ratios (R{) - R)/Ro of both PMMA-coated silver and chromium (at a wavelength of 6 μπι) are plotted as a function of grazing incidence angle. Obviously, the reflectivity ratio of the PMMA-coated chromium is significantly less than that of the PMMA-coated silver, accounting for a portion of the falloff of S/N in the observed spectra. However, the other, more subtle point made in Fig. 8 is that the maximum in the chromium curve has shifted to lower grazing incidence angle, now appearing in the vicinity of 85° as opposed to 88° for silver. Thus, because these experiments were conducted at a fixed grazing angle, which ensured a large reflectivity ratio from silver, consequently changing to a chromium-coated substrate with-out a subsequent readjustment of the angle could cause a less than opti-mum reflectivity from the latter substrate. It is therefore critically impor-tant that a process of maximization of (/?0 ~~ R)/RQ be instituted each time a new metal is used for GIR measurements. It is also important to note that the approximate factor of 2 falloff of the spectrum of PMMA on chromium compared with that on silver cannot be completely accounted for by the dimunition in reflectivity which occurs at 80° (angle at which all spectra were recorded). A slight discoloration of the chromium surface was observed during the coating process and could be due to a reaction of chloroform with the metal. This could have contributed to a lower overall reflectivity of the chromium-coated substrate than that calculated theoret-ically.

A more thorough examination of Fig. 12 reveals several other observa-tions, which may eventually have exciting structural implications. If a comparison of the region 1100-1300 cm - 1 is made for each of the spectra on the four metals, a noticeable change in relative intensities is observed for both the 1150 and 1270 cm - 1 bands. All four bands in this region are believed to be vibrations of the —COOCH3 ester side group (Willis et al., 1969); hence this raises the interesting possibility that the changes in dipole moment and thus the changes in intensity might be associated with either a differing adhesive interaction with the metals or perhaps a reori-entation of certain chemical groups with respect to the polarized electric field component normal to the surface. It is unclear at this point whether the vibration of groups at the polymer-metal interface would be discern-ible relative to those in the bulk for films of 180-Ä thickness. Studies are in progress (Rabolt et al.y 1985b) using both GIR and standard transmission measurements to elucidate structure and orientation in ultrathin PMMA films (<100A).

A final observation in Fig. 12 illustrates a very important point first made by Allara et al. (1978). On close inspection of the 1732 cm - 1 C = 0 band of PMMA on chromium, it becomes apparent that the profile is considerably asymmetric. This asymmetry can lead to small shifts in the


peak position, which, if unrecognized, could lead to an incorrect assign-ment of this shift to chemical interactions at the polymer-metal interface. Allara et al. (1978) pointed out that the electric field at a given distance (from the metal surface) within the polymer film will vary considerably in the vicinity of an absorption maximum due to the large variation of n versus k in this anomalous dispersion region, and in general the opposite sides of a reflection band will be dissimilar. In particular, they showed that this band distortion was a complex function of k (the absorptive part of the refractive index), film thickness, and angle of incidence, but the largest shifts (>10 cm-1) were found for k » 0.1 and films of thickness greater than 0.5-1 μπι. Thus, these results illustrate the extreme caution to be exercised in the assessment of intermolecular interactions from band shifts observed in GIR measurements.

2. Semicrystalline Polymers

In the absence of any stereochemical configurational restrictions, poly-mers crystallize into folded-chain lamellae under certain conditions. As shown in Fig. 13, the chains can reenter the same lamellae or an adjacent one depending on the mobility of the chain during the crystallization process. The semicrystalline nature of this structure results because of the ordered packing of chain stems into a crystalline region, with the disordered folds segregated at the lamellar surface constituting the amor-phous region. Considerable efforts have been made in the case of polyeth-ylene to characterize the nature of the folded surface both theoretically and experimentally, but considerable controversy over the adjacency or randomness of the reentrant folds still remains.

In contrast, studies of the crystalline regions in polyethylene have been straightforward because of the significant body of information provided by studies of the isostructural «-alkanes. Polyethylene crystallizes in an orthorhombic unit cell containing two molecules. Crystal field interac-

Fig. 13. Schematic of folded-chain lamellar crystal formed by crystallization of poly-mer chains.


tions give rise to a splitting of the ir-active —CH2— bending (1460-1470 c m 1 ) and —CH2— rocking (720-730 c m 1 ) vibrations (Krimm, 1954), which can be clearly observed in bulk samples. As shown in Fig. 14, these splittings are also easily detected in thin films. In this case, a l-μτη film was cast from hot xylene on a silver-coated slide. As expected, the —CH2— bending vibration is split into two components at 1472 and 1462 cm- 1 , whereas the rocking doublet occurs at 730 and 719 cm- 1. By com-parison with a spectrum obtained from an isotropic sample, it is clear that no apparent orientation is observed in a film of this thickness. However, it raises the intriguing possiblility that if crystalline lamellae were uniquely oriented with their stems perpendicular to the surface, which occurs dur-ing epitaxial growth if the unit cell parameters of both polymer and sub-strate are commensurate, then the polarization selection afforded by GIR should allow detection of crystallite orientation. The potential of this technique for characterizing the structure and orientation of epitaxially grown crystals as a function of thermal history and surface modification is promising.

0.4

0.0

Crystal Field Split Doublets

1472 1462 719

730

3200 2400

Wavenumber (cm ')

1600 800

Fig. 14. Infrared spectrum of semicrystalline polyethylene film (thickness ~ 1 μπι) obtained by the GIR technique.


A final point regarding GIR measurements of semicrystalline polymers must be made. The far-ir region (10-400 cm-1) remains one of the most underexploited regions for the study of polymeric materials. However, a number of semicrystalline polymers do exhibit lattice vibrations (Rabolt, 1984) the intensity and frequency of which are characteristic of the unit cell interactions between adjacent chain stems. For example, the crystal field interactions responsible for the band splittings in polyethylene shown in Fig. 14 also give rise to two ir-active lattice vibrations at 109 (Dean and Martin, 1967) and 72 c m 1 (Frenzel and Butler, 1964; Bertie and Whalley, 1964; Krimm and Bank, 1965). Far ir studies of polyethy-lene and polymer films, in general, using GIR are nonexistent, but they represent a fertile area for future investigation.

VI. SUMMARY

The field of characterization of thin organic and polymer films using GIR techniques is only in its infancy. Indeed, the technique for monolayer sensitivity has been refined only since the early 1980s, and only a few specific structures have been studied in detail using both GIR and trans-mission measurements. The potential for this research area is large, as indicated by our own preliminary studies of molecular motion and phase transitions in ultrathin films and L-B monolayers as a function of tempera-ture. (Naselli et al., 1985). In addition, the characterization of orientation in polymer "monolayers" should provide significant progress in our un-derstanding of how polymers physisorb on a surface. It appears that there are many molecular problems in which FT-IR studies using GIR tech-niques should provide unique insight.

ACKNOWLEDGMENTS

The authors express their appreciation to Sylvia Fujii and Karen Bryan for their assis-tance in the preparation of the manuscript. We are also indebted to Mark Jurich and Bernd Hupfer for their careful preparation of many of the samples used in this work.

REFERENCES

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Dean, G. D., and Martin, D. H. (1967). Chem. Phys. Lett. 1, 415. Francis, S. A., and Ellison, A. H. (1959). J. Opt. Soc. Am. 49, 131. Frenzel, A. O., and Butler, J. P. (1964). J. Opt. Soc. Am. 54, 1059. Gaines, G. L. (1966). "Insoluble Monolayers at Liquid-Gas Interfaces." Wiley (Inter-

science), New York. Golden, W., Snyder, C , and Smith, B. (1982). J. Phys. Chem. 86, 4675. Greenler, R. G., (1966). J. Chem. Phys. 44, 310. Heavens, O. S. (1965). "Optical Properties of Thin Solid Films." Dover, New York Hendra, P. J., Jobic, H. P., Marsden, E. P., and Bloor, D. (1977). Spectrochim. Acta, Part

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Weissberger and B. W. Rossiter, eds.), Part III B Wiley (Interscience), New York. Landau, L. D., and Lifshitz, E. M. (1960). "Electrodynamics of Continuous Media." Per-

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Chem. 82, 1989. Philpott, M. R., and Swalen, J. D. (1978). J. Chem. Phys. 69, 2912. Pockrand, I., Swalen, J. D., Gordon, J. G., II, and Philpott, M. R. (1979). J. Chem. Phys.

70, 3401. Puterman, M., Fort, T., Jr., and Lando, J. B. (1974). J. Colloid Interface Sei. 47, 705. Rabolt, J. F. (1984). In "Infrared and Millimeter Waves" (K. J. Button, ed.), Vol. 12, pp.

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946. Rabolt, J. F., Hupfer, B., and Swalen, J. D. (1985). In preparation. Rabolt, J. F., Jurich, M., and Swalen, J. D. (1985b). Appl. Spectrosc. (in press). Roberts, G. G. (1984). Sens. Actuators 4, 131. Russell, T. P., Gugger, H., and Swalen, J. D. (1983). J. Polymer Sei.: Polymer Phys. Ed. 21,

1745. Sung, C. S. P. (1981). Macromolecules 14, 591. Swalen, J. D. (1979). J. Phys. Chem. 83, 1438. Takenaka, T., Nogami, K., and Gotoh, H. (1971). J. Colloid Interface Sei. 40, 409. Tieke, B., and Lieser, G. (1982). J. Colloid Interface Sei. 88, 481. Tieke, B., Wegner, G., Naegele, D., and Ringsdorf, H. (1976). Angew. Chem. 15, 764. Tieke, B., Graf, H. J., Wegner, G., Naegele, D., Ringsdorf, H., Banerjie, A., Day, D., and

Lando, J. (1977). Coll Polym. Sei. 255, 521. Tieke, B., Lieser, G., and Wegner, G. (1979). J. Polym. Sei., Polym. Chem. Ed. 17, 1631. Tieke, B., Bloor, D., and Young, R. J. (1982). J. Mater. Sei. 17, 1156. Tieke, B., Lieser, G., and Weiss, K. (1983). Thin Solid Films 99, 95. Willis, H. A., Zichy, V. J. I., and Hendra, P. J. (1969). Polymer 10, 737.

8 FOURIER TRANSFORM INFRARED REFLECTION-ABSORPTION SPECTROSCOPY

William G. Golden IBM Instruments, Inc. San Jose, California

IV.

Introduction Reflection-Absorption Theory Survey of FT-IRRAS A. Conventional FT-IRRAS B. Polarization-Double-Modulation

FT-IRRAS Conclusion References

315 317 323 323

332 343 343

I. INTRODUCTION

Advances in the understanding of surface chemistry since the 1950s can be attributed to the development of many analytical methods that by virtue of their surface sensitivity have provided the experimentalist with the ability to monitor the concentration, ordering, and oxidation state of species adsorbed on surfaces. These techniques, due to advances in tech-nology, are routinely available and have been applied to a broad range of interfacial systems. Molecular structure and the type of bonding present at a surface can usually be deduced by their application.

Vibrational spectroscopy of surface species can provide information about structure and bonding directly and, when combined with the other surface analytical techniques available, yields a more complete picture of the chemistry and molecular structure of surfaces. One technique that provides vibrational spectra of surface species, electron energy-loss spec-troscopy (EELS), has been developed (Ibach, 1972) to the extent to which it is presently the one most universally applied to the study of adsorbates on low-area single-crystal metal surfaces. It has the advantages of good surface sensitivity and a wide spectral range but must be used in vacuum and suffers from relatively low resolution (—20 cm-1).


ISBN 0-12-254104-9

316 William G. Golden

The use of photon spectroscopies to obtain the vibrational spectra of surface species is desirable because of their inherently high-energy reso-lution and also because they can be applied not only in vacuum situations but also in systems in which the medium above the surface consists of either gas or liquid. Raman spectroscopy has these advantages and has been shown to be feasible in the study of adsorbates on low-area single-crystal surfaces (Campion, 1983); however, sensitivity is the principal limitation to wide application. Surface enhanced Raman spectroscopy (SERS), which has adequate surface sensitivity, is a potentially powerful tool in this regard, but the enhancement is limited to a small number of metals, and the mechanism of enhancement is not completely understood (Fleischman et al., 1974; Jeanmaire and Van Duyne, 1977).

The infrared (ir) is also hampered by low sensitivity, although not to the degree that Raman is. Improvements in instrumentation have by and large overcome the problems produced by low-intensity ir sources and intrinsi-cally inefficient ir detectors. Since the 1970s these methods have been developed with both dispersion and Fourier transform instruments to a level where they show promise as routine tools for the study of surfaces. In addition, because photon spectroscopy is not lmited to the vacuum, it is possible with ir and Raman spectroscopies to study a variety of surface and interfacial systems that are inaccessible to electron spectroscopies.

Eischens and co-workers (Eischens and Pliskin, 1958) were the first to demonstrate the applicability of ir spectroscopy to the study of submono-layer quantities of molecules adsorbed on surfaces. Their investigations were performed by transmission ir spectroscopy on small molecules ad-sorbed on high-area supported catalysts. The experiments were possible, in spite of low sensitivity, because of the very high surface area provided by the supported metal catalysts; that is, a large number of adsorbed molecules could be sampled by the ir beam. Supported metal catalyst surfaces are not well defined, however, and it was not until a number of years later that the ir reflection technique was used to study molecules adsorbed on well-characterized low-area single crystals (Pritchard and Simms, 1970). Since then, a number of techniques for doing infrared reflection-absorption spectroscopy (IRRAS) have been developed using dispersion ir spectrometers. Descriptions of the progress made in disper-sion IRRAS can be found in review articles by Bradshaw (1982) and Hoffman (1983).

High resolution, throughput, and the multiplex advantage of Fourier transform interferometry (FT-IR), combined with powerful computer-sup-ported data acquisition and handling, have introduced further advances in the study of the vibrational spectra of surface species. This chapter dis-

8 Reflection-Absorption Spectroscopy 317

cusses, by way of example, the contributions of FT-IR to this area. The examples chosen are not meant to be representative of the entire effort underway in Fourier transform infrared reflection-absorption spectros-copy (FT-IRRAS) but are intended to be illustrative of the contributions made by FT-IR in the area. In addition, a double-modulation-polarization modulation FT-IRRAS is described.

II. REFLECTION-ABSORPTION THEORY

From the work of Greenler (1966) and Francis and Ellison (1959) it is known that the ir reflection-absorption spectrum of a very thin film ad-sorbed on a metal substrate depends on the optical constants of the ad-sorbate and substrate as well as on the angle of incidence and state of polarization of the incident ir radiation. The details of this dependence can be determined by applying the boundary conditions governing the passage of electromagnetic radiation from one medium to another to ob-tain the classical reflection equations for a three-phase system (Born and Wolf, 1959). Such a system is illustrated in Fig. 1. This theory has been described by numerous workers and in several review articles (Bradshaw, 1982; Hoffman, 1983; Mclntyre and Aspnes, 1971); only a summary of the results is given here.

Incident l radiation ni

Fig. 1. Three-phase system considered in reflection calculations: 0, is the angle of incidence, d the adlayer thickness, and the A/'S and &'s are the dispersive and extinction coefficient terms in the complex refractive indexes, respectively.


In order for light of wavenumber v to be absorbed by a molecule ad-sorbed on a surface at the vacuum-metal interface, three conditions must be met:

(1) The molecule must have a nonzero dipole transition moment at v. (2) The magnitude of the electric field of the light at the surface must be

nonzero in the vicinity of the molecule. (3) There must be a nonzero component of the dipole transition mo-

ment of the molecule along the direction of the electric vector of the ir radiation present at the surface.

When light is reflected from a metal surface, the electric vector of the incident radiation experiences a phase change on reflection that depends on both the angle of incidence of the light as well as its state of polariza-tion. The parallel component/?, of the incident radiation is defined here as that component whose electric vector is in the plane of reflection. The perpendicular component s is perpendicular to the plane of reflection, or in other words, in the plane of the surface. The phase shift for the perpen-dicular component is not a strong function of angle of incidence, being nearly 180° for all angles of incidence. The phase shift for the parallel component is a strong function of the angle of incidence, however, and varies from nearly 0° at angles near normal incidence to 180° at grazing incidence. Figure 2 is a plot of the phase shift for the two polarizations 8P

and 8S as a function of angle of incidence.

-20 -

-40 -

</) Φ ω -60 -en Q)

"° - 80 -Ό

(D CT-100 -c 03

5 - 1 2 0 -

$ -140 -

-160 --180 -

0 10 20 30 40 50 60 70 80 90 Angle of Incidence, degrees

Fig. 2. Dependence of the phase change δ on reflection for the parallel (p) and perpen-dicular (s) polarizations of the electric field as a function of angle of incidence.


The consequences of this effect on the amplitude of the electric vectors at the surface are best illustrated by Fig. 3. Because the phase change on reflection for the perpendicular component is nearly 180° for all angles of incidence, the incident and reflected electric vectors sum up to almost zero at the surface, resulting in only a very small net electric field parallel to the metal surface. At small angles of incidence (near normal incidence) the phase change for the parallel component is nearly zero, and again the incident and reflected electric vectors nearly cancel. At larger angles of incidence, however, the vector sum of the incident and reflected electric vectors results in an electric field at the surface with a substantial compo-nent perpendicular to the surface normal. Because intensity is the square of the electric field amplitude, at grazing incidence there is an enhance-ment of the field intensity near the surface of nearly four times the inci-dent intensity. From this picture it is clear that only the parallel component of the incident radiation can be appreciably absorbed by an adlayer and that maximum absorption will occur at near grazing incidence.

Quantitative understanding of the reflection-absorption experiment re-quires examination of the reflection equations for the three-phase system (see Fig. 1) as a function of angle of incidence and state of polarization of the incident radiation. The complex reflection coefficients, which give the

PARALLEL COMPONENT 90° phase shift

Fig. 3 . Incident and reflected electric vector geometries at the metal surface at grazing incidence. Note that the sum of the incident and reflected electric vectors for the perpendic-ular component produces no field at the surface.


amplitude of the reflected electric vector with respect to the incident electric vector, are (Born and Wolf, 1959)

rv\2 + rm exp(2i)8) rv\23 — Z ; ; ; /*>·Λ\ ν 1 ^

1 + 12 23 exp(2*0) where u = s (perpendicular) or p (parallel), and

β = (2πά/λ)ή2 cos 03 (2) The caret (Λ) over the quantities in these and subsequent expressions indicates that they are complex.

The complex reflection coefficients rviJ for each boundary in Fig. 1 are given by the Fresnel equations,

'pij - "J c o s A* ~ h c o s ft/ ηϊ

hj cos Si + hi cos 6j hi COS 0; - ή; COS Θ;

r = (4) slJ hj cos dj + ni cos Θ,·

The /i/'s in these expressions are the complex refractive indexes of the /th medium, «/ + //:/, where rii is the refractive index and ki the extinction coefficient.

The intensity of the reflected radiation is given by It = I rvm | 2Vv = RUi (5)

where R^ is the reflectance of the system, and Γυ and /„ are the intensities of the incident and reflected radiation, respectively.

The determination of the absorbance of an adlayer is made convenient if the convention established by Greenler (1966) is adopted, that is,

Αυ=1- (Rdv/R°v) (6)

where Av is an absorption factor and R°v and R% are the reflectances of the bare and adlayer-covered substrates, respectively.

With Eqs. (l)-(6) and the set of optical constants for the three regions, the dependence of the reflection-absorption experiment on angle of inci-dence, adsorbate thickness, and state of polarization can be calculated. This calculation for a thin film of acetone on a typical metal has been carried out by Blanke et al. (1976). Reliable optical constants for liquid acetone in the region of the 1717 cm-1 carbonyl stretching mode were well known from attenuated total reflection data (Goplin, (1976)). The optical constants for the substrate were taken to be independent of wavenumber and were chosen to be representative of a typical metal (n = 3 and k = 30). The adsorbate-substrate system was taken to be in vacuum so that n = 1 and k = 0.


20 40 60

Angle of Incidence, degrees Fig. 4. Dependence of the absorption factors for the parallel (Ap) and perpendicular

(As) polarizations at the wavenumber of maximum adiayer absorption as a function of angle of incidence. The calculation was for a 10-Ä film of acetone on gold.

Figure 4 shows the calculated dependence of Ap and As, using Eqs. (1)-(6), at the wavenumber of maximum absorbance, on the angle of inci-dence. The parallel component goes through a maximum at —88°, whereas the perpendicular component decreases with incident angle.

At 88° Ap is ~105 times larger than As. Figure 5 shows the calculated spectrum of this system as a function of angle of incidence. As expected,

1650 1700 Wavenumber, cm

1750

ApX103

Fig. 5. Variation in the parallel absorption factor Ap with wavenumber and angle of incidence for 10-Ä film of acetone on gold. The enhanced reflectivity on either side of the absorption band is caused by anomalous dispersion (see text).


x <

2-\

1000

Fig. 6. Dependence of the parallel absorption factor Ap, at the wavenumber of maxi-mum absorbance, on film thickness.

the band intensity increases as the angle of incidence increases. The effects of anomalous dispersion can also be seen in this figure as enhanced reflectivity, producing a negative absorption away from the band center.

The dependence of absorbance Ap on film thickness is shown in Fig. 6. The dependence is essentially linear up to 100 A. It has been shown (Greenler, 1966) that, for thicknesses larger than —1000 A, Ap may go through a maximum; still, even at these thicknesses, As is at least 1000 times smaller than Ap .

Comparison of the frequency of the absorption maximum calculated for the reflection-absorption experiment with that of the transmission experi-ment gives a shift of the adsorbate band maxima to the blue of the trans-mission band maximum by about 4 cm-1 (Blanke, 1975). This shift is due to optical effects inherent in the reflection-absorption experiment and, because it is quite small, implies that any significant wavenumber shift observed on adsorption in an IRRAS experiment is due to perturbations induced by bonding to the surface.

In summary, calculations like those above yield the following general conclusions:

(1) The optimum angle of incidence for an IRRAS experiment is near grazing incidence.


(2) Only the parallel component of the incident radiation will be ab-sorbed and only at grazing incidence.

(3) The absorption of the parallel component of the incident radiation is linear up to film thicknesses of about 100 A.

(4) No significant frequency shift in the band maximum results from optical effects on reflection.

As will be seen later, these results are the driving considerations in the design of any IRRAS experiment and in fact lead to a number of modula-tion schemes that take advantage of the absorption of the parallel compo-nent and the nonabsorption of the perpendicular component (Shigeishi and King, 1976; Golden et al.y 1981, 1984a). Further, these results give rise to the so-called surface selection rules and have been extremely useful in the elucidation of adsorbate structure and orientation relative to the surface.

III. SURVEY OF FT-IRRAS

A. Conventional FT-IRRAS

This section gives some selected examples of conventional grazing inci-dence reflection spectroscopy that illustrate how FT-IR in general has had an impact on sensitivity and resolution beyond dispersive instrumenta-tion.

A number of surface systems have been examined by conventional grazing incidence FT-IR, ranging from Langmuir-Blodgett monolayer as-semblies to studies of adsorbed species on low-area single-crystal metal surfaces under ultrahigh-vacuum conditions. In most cases the surface selection rules (see Section II) can be used to determine the orientation and molecular ordering at the surface.

Greenler (1966) showed that, for multiple external reflections, a specific number of reflections provide maximum surface sensitivity (i.e., six re-flections for a typical metal at grazing incidence). Consequently, in order to facilitate the experimental setup, most routine external reflection appli-cations use only a single reflection. A typical optical arrangement is shown in Fig. 7. For conventional surface ir spectroscopy an ir polarizer is often placed in the beam with its passing axis oriented such that the parallel component of the ir radiation will either strike the surface or be collected after reflection. This arrangement can usually be placed inside the analysis chamber of the FT-IR spectrometer, although, as will be seen later, FT-IR optics can also be interfaced to experiments outside the spectrometer bench.


Fig. 7. Typical optical configura-tion used for conventional FT-IRRAS in which only one reflection at grazing inci-dence is used (Ishitani et ai, 1982). The optics are designed to be compatible with the sample compartment of the FT-IR spectrometer. Several types of graz-ing incidence reflection attachments are commercially available.

Krishnan and Ferraro (1982) gave an example of the polarized reflec-tion-absorption spectrum of a very thin film of silicone grease on an aluminum foil. A front aluminized mirror was used as the reference for their spectra to avoid obtaining polarization spectra of the spectrometer itself. Figure 8a is the reflection-absorption spectrum of the grease with the incident radiation polarized perpendicular to the plane of reflection; Fig. 8b is the reflection-absorption spectrum for the parallel component. It is clear that the parallel component of the radiation reflected from the surface provides useful spectra, whereas the perpendicular component does not. It should also be noted that, even though the silicone grease on the surface has no net molecular orientation with respect to the substrate, those dipole transition moments that have some component along the surface normal experience absorption of the parallel-polarized radiation.

Ottesen and Nagelberg (1980) demonstrated the analytical capability of FT-IRRAS by monitoring stainless steel surfaces with minor titanium additions exposed to low pressures of oxygen. The data were obtained at a 75° angle of incidence with unpolarized light. The authors were able to identify a number of oxides, confirming observations made by electron microscopy, x-ray diffraction, and Raman light scattering. In these stud-ies they concluded that the ir beam probed of the order of 1 to 2 μπ\ into the metal oxides. Their work involved fairly thin films, but they obtained a good signal-to-noise (S/N) ratio in a reasonable amount of time without using polarized light and with an ir detector (triglycine sulfate bolometer), which is not particularly sensitive by today's standards.

Using a fixed polarizer oriented parallel to the plane of reflection, Ishi-tani et al. (1982) attained surface sensitivity sufficient to measure the reflection-absorption spectra of thin layers down to 90 A for a number of samples without being restricted by the surface area of the sample. They

V Polarizer

Sample

Incident light


4000 3500 3000 2500 2000 1500 1000 500

Wavenumber, cm"1

Fig. 8. FT-IRRAS spectra of a thin film of silicone grease on aluminum foil using a front-surface aluminized mirror as the reference, (a) Incident electric vector perpendicular to the plane of reflection; (b) incident electric vector parallel to the plane of reflection. Both spectra are plotted on the same scale (Krishnan and Ferraro, 1982).

obtained spectra of thin films at 4 cm-1 resolution with a sample surface area of ~4 cm2. Figure 9 shows their FT-IRRAS spectra of thin films of poly(acrylonitrile co-styrene) adsorbed on aluminum. The film thick-nesses were verified by ellipsometry, and as is shown in Fig. 10 the correlation between film thickness and FT-IRRAS absorption is good. The 90-Ä film represents the limit of detection for this system, because only the more intense bands are discernible in the spectrum.

Langmuir-Blodgett monolayers have potential applications for surface lubrication and insulating layers of well-defined thickness and are also useful as model biological systems and synthetic membranes. These orga-nized assemblies have been studied since the first reports of Langmuir (1917) and Blodgett (1939, 1953) by a variety of techniques. The study of these systems by FT-IRRAS demonstrates the utility of the surface selec-tion rules because the spectra allow at least qualitative confirmation of the

2680 Ä

Jj).012

1 r 1 Uj

4000 3000 500 2000 1500 1000

Wavenumber, cm"1

Fig. 9. FT-IRRAS spectra of thin films of poly(acrylonitrile co-styrene) adsorbed on aluminum. The 90-Ä film represents the limit of detection for this experiment (Ishitani et al., 1982).

0.06

CC

3000 1000 2000

Film Thickness, Ä Fig. 10. Plot of the absorption factor for the band in Fig. 9 at 302 cm-1 as a function of

film thickness. Film thicknesses were determined by ellipsometry (Ishitani et al., 1982).


structure of these assemblies. A thorough discussion of the ir spectra of these types of monolayer assemblies is given in Chapter 7. Allara and Swalen (1982) took the ir spectra of 1 to 10 monolayer assemblies of Langmuir-Blodgett films of cadmium arachidate at grazing incidence (86°) with a fixed polarizer oriented parallel to the plane of reflection using a liquid-nitrogen-cooled mercury cadmium telluride detector. These spec-tra are shown in Figs. 11 and 12. Comparison of the observed relative intensities of the bands in the FT-IRRAS spectra with those observed for bulk cadmium arachidate led the authors to conclude that the cadmium arachidate molecules were oriented with the aliphatic chains normal to the surface. For example, inspection of the relative intensities of the symmetric methylene stretch at 2851 cm-1 with that of the symmetric methyl stretch at 2875 c m 1 in Fig. 12 indicates that the methylene band is less intense relative to the methyl band for the surface assembly. (In the bulk spectrum the symmetric methylene stretch is —10 times more intense than the symmetric methyl stretch.) Figure 13 depicts the proposed orien-tation of the cadmium arachidate with respect to the surface. Because the dipole transition moment for the symmetric methylene stretch is perpen-dicular to the aliphatic chain axis, whereas the dipole transition moment for the symmetric methyl stretch is parallel, it is reasonable to conclude qualitatively on the basis of the surface selection rules that the aliphatic backbone of the cadmium arachidate is oriented along the surface normal. This also holds true when the relative intensities of the antisymmetric methylene and methyl bands are compared (and again for the symmetric

0.10

rr £ 0.05 σ> o

x1

x2.5

x5

,. -*/N 1543

1

1432 I

Ml 14°° / / u '

1465/ \ ^

1

, 10 layers

2 layers

l ^ V . 1 layer

1 ^

1800 1600 1200 1100 1400 Wavenumber, c m 1

Fig. 11. FT-IRRAS spectra of layers of cadmium arachidate on evaporated silver for the region 1100-1800 cm"1 (Allara and Swalen, 1982).


0.050

5.0.025H

0.0

10 layers

Λ Λ K 2 layers '

1 layer

2920

2962 293Λ \ y I

A) M Ah 1 — ι 1

2875 / 8 5 '

K A* N \| \Λ

x1

x0.3

x5

x,0 ( *

An 4 υ

- i

3100 3000 2800 2700 2900 Wavenumber, cm-1

Fig. 12. FT-IRRAS spectra of layers of cadmium arachidate on evaporated silver as in Fig. II, but showing the region 2700-3100 cm ' (Allara and Swalen, 1982).

c \ H

H ?\ H - C A

Fig. 13 . Representation of the pro-posed structure of the cadmium arachidate chain orientation relative to the substrate surface. The angle φ was determined to be less than 5° from the surface normal (Allara and Swalen, 1982).


and antisymmetric carboxylate stretches). Allara and Swalen (1982) also compared their observed spectra with those they calculated from optical data for bulk cadmium arachidate and determined that the chain axis tilt away from the surface normal, within their experimental error, was no more than 5°.

Rabolt and co-workers (1983) studied cadmium arachidate Langmuir-Blodgett monolayers by grazing incidence as well as transmission ir spec-troscopy (see Chapter 7). By combining both techniques using a fixed polarizer, they were able to establish the orientation and crystal structure of these multilayer films. Figure 14a shows their reflection spectra for six monolayers of cadmium arachidate on silver. The bulk spectrum of cad-mium arachidate taken by transmission is also shown (Fig. 14b) to empha-size the characteristic relative intensities of the ir bands of the surface assemblies, which allows for the conclusion that the molecules are ori-ented with their chain axis perpendicular to the surface. Rabolt and co-workers were thus able to determine more exactly the structure of these Langmuir-Blodgett films; that is, they found that the cadmium arachidate layers pack in the orthorhombic form on the basis of the observation of crystal field splitting of the CH2 rocking and bending vibrations.

In addition to the study of thin-film and monolayer systems in which the samples can be placed in the FT-IR spectrometer sample chamber, appli-cation of the technique to studies of gas-solid adsorption under ultrahigh-vacuum conditions has been shown to be feasible. Figure 15 is a sche-

0.04

002

225

0.25 3200 3000 2800 2600 2400 2200 2000 1800

Wavenumber, cm-1 1600 1400 1200

Fig. 14. (a) FT-IRRAS spectrum of six monolayers of cadmium arachidate on silver; (b) transmission FT-IR spectrum of cadmium arachidate in a KBr pellet (Rabolt et al., 1983).


FT-IR Source

Fig. 15 . Optical configuration for performing FT-IRRAS on samples under ultrahigh vacuum (UHV). In this case the ir source is located externally, and the radiation is focused through the vacuum system onto the sample at grazing incidence. The reflected radiation is then collected into the interferometer. Note that the entire optical path, except for the part in the vacuum chamber, is under purge to minimize atmospheric absorptions (Kawai, et al., 1981). M, Mirror; P, polarizer.

matic of an optical system for IRR AS. In this case (Kawai et al., 1981) the optical path external to the spectrometer was purged with dry air to avoid the complications arising from gas-phase water vapor and carbon dioxide. Carbon monoxide adsorbed on transition metals has been studied by IR-RAS extensively because the oscillator strength of CO bound to metals is large. Figure 16 shows a spectrum obtained by Kawai et al. (1981) for CO adsorbed on a palladium foil. The data acquisition time was 40 min, and by using a polarizer, they obtained the ratio of the parallel and perpendic-ular components to produce the spectrum. In addition to the purge this also allowed for effective cancellation of the residual small absorptions due to water and carbon dioxide.

Figure 17 shows their plot of the frequency shift of the CO stretching band as a function of surface coverage. The surface species, by virtue of the band-center position, was assigned to bridged CO species; that is, the

Ί Γ 2100 2000 1900

Wavenumber, cm1

Fig. 16 . Example of an FT-IRRAS spec-trum of CO adsorbed on palladium (Kawai et al., 1981).


r/

/

1 9 7 0 — — i — i — i — i — i — i 1 1 1 1 1 1 1—

0.5 1.0

Fig. 17. Plot of the band-center frequency shift as a function of coverage for CO adsorbed on palladium. The structure of the adsorbed CO was assigned to bridge-bonded CO species by virture of the position of the band (Kawai et ed., 198I).

CO molecules are bound through the carbon atom to two surface palla-dium atoms. This type of frequency shift has been observed many times and is usually attributed to intermolecular dipole-dipole coupling be-tween the adsorbed CO molecules (Crossley and King, 1977). However, major factors contributing to the shift of the CO stretching frequency with coverage have been discussed by a number of authors (e.g., see Ortega et al„ 1982) and include not only dipole-dipole coupling but also dipole-self-image, dipole-other image, and through-substrate coupling, as well as competition for metal electrons by adsorbed molecules, which affects the degree of backdonation into the 2π* orbital of the adsorbed molecule (Mahan and Lucas, 1978; Scheffler, 1979; Moskovits and Hülse, 1978; Blyholder, 1964).

Baker and Chesters (1982), using a configuration similar to that in Fig. 15, obtained FT-IRRAS spectra of CO on platinum (111) (recrystallized foil) by taking ratios of single-beam spectra before and after adsorption of the CO on the surface. The ratios produced some miscancellation of the atmospheric carbon dioxide bands, but this did not interfere with the region of the spectrum in which the CO bands appeared. Figure 18 shows the spectra of CO as a function of coverage. The spectra show a splitting


RES2

J 0.20/0

RES 8

2200 1700 1900 2000 Wavenumber, cm1

Fig. 18 . FT-1RRAS spectra of CO adsorbed on a recrystallized platinum foil in ultra-high vacuum as a function of coverage. Both the linear (—2100 c m 1 ) and bridged (—1850 cm-1) CO surface absorptions are observed. The shoulder at the low-energy side of the linear CO stretching band demonstrates the high spectral resolution provided by FT-IRRAS.

of the band due to linearly adsorbed CO with coverage. The appearance of this shoulder at the low-energy side of the linear CO stretching band has been observed before, and the relative intensity of the shoulder and the main peak can be varied considerably by changing the conditions of CO adsorption (Hayden and Bradshaw, 1983).

B. Polarization-Double-Modulation FT-IRRAS

One of the methods developed for dispersion IRRAS (Golden et al.y

1981) utilizes both polarization modulation and double modulation such that it is possible to obtain both reference and polarization-modulated spectra simultaneously. These spectra then ratio to produce good discrim-ination against ir absorption of nonsurface species and experimental drift, which otherwise degrade spectral quality. The polarization modulation provides an improvement in the dynamic range of the measurement, al-lowing for higher surface sensitivity and therefore shorter data acquisition times.


The device used to modulate the polarization state of the ir radiation incident on the surface is known as a photoelastic modulator (PEM). The principles of operation of PEMs are well established (Chabay, 1972; Kemp, 1969), and PEMs that function in the ir are commercially available (Hinds International). Briefly, the PEM is an ir-transparent cubic crystal that, unstressed, has an isotropic index of refraction. Producing a periodic strain along one axis of the crystal by driving it at the frequency of its fundamental longitudinal mode with piezoelectric transducers will result in a periodic change in the refractive index for radiation polarized in that axial direction and thus produce a periodic phase retardation for radiation with that polarization. Because the polarization components of the ir radiation incident on the surface are to be modulated, the stressed axis of the PEM is oriented at 45° to the surface normal, and a fixed polarizer is positioned with its passing axis either parallel or perpendicular to the surface normal.

Dispersion IRRAS incorporates double modulation as follows. Infrared radiation from a conventional ir source is modulated by both a rotating blade chopper and a PEM. The rotating blade chopper produces a signal at the detector, which when demodulated is proportional to the total light intensity reflected from the sample surface (Ip + Is). The PEM modulates between the parallel and perpendicular components of the light striking the surface such that after demodulation a signal proportional to the dif-ference in the intensities of the two polarization components (Ip — Is) is obtained. These two signals can be demodulated separately, because there is a large difference in the modulation frequencies of the rotating blade chopper (-1000 Hz) and the PEM (-100 kHz). Because Ip and Is

are, on the average, attenuated to the same degree by randomly oriented gas- or liquid-phase molecules but only Ip is attenuated by adsorbed spe-cies (see Section II), production of the ratio (Ip - IS)I{IP + Is) allows cancellation of absorption by randomly oriented oscillators so that the final signal contains only the IRRAS spectrum of the surface molecules. Because the modulation scheme also effectively discriminates against sample substrate emission, it is also possible to obtain IRRAS spectra when the substrate is held at elevated temperatures (Golden et al., 1981, 1984a).

Several workers (e.g., Dowry and Marcott, 1982) have adapted the photoelastic modulation technique to FT-IR using the approach devel-oped for dispersion IRRAS (Golden et al., 1981) and vibrational circular dichroism (Nafie et al., 1979) and have demonstrated improvements in dynamic range and SIN over conventional FT-IRRAS. In this author's laboratory, additional signal handling refinements have been added so that it is also possible to obtain both the sum (Ip + Is) and difference


(Ip — Is) polarization spectra simultaneously, eliminating concerns over fluctuations in stability and other time-dependent factors that can degrade FT-IRRAS spectra (Golden and Saperstein, 1983). This version of FT-IRRAS is discussed in some detail here.

In FT-IR, an interferogram is produced by the motion of a mirror rela-tive to a fixed mirror in either a Michelson interferometer or in one of many variations (e.g., the Genzel design). This interferogram is mathe-matically demodulated by the Fourier transform to produce the ir spec-trum. By incorporating a PEM in the optical path of the FT-IR spectrome-ter, the modulation frequency of which is much larger than the interferometer modulation frequencies, it is possible to produce a double-modulation experiment. Because the PEM modulation frequency is of the order of 100 kHz, while the interferometer modulation frequency in the mid-ir can be less than 10 kHz, it is possible to emulate the dispersion double-modulation experiment without using a separate chopper.

Figure 19 depicts the optical arrangement used for a polarization-dou-ble-modulation FT-IRRAS experiment in which the surface sample can be placed in the FT-IR sample chamber. A gold wire grid polarizer and ZnSe PEM are combined in the usual fashion (Golden et aL, 1984a) to produce polarization-modulated ir radiation, which is then focused at near grazing incidence onto the sample by a standard variable-angle retro-mirror as-sembly.

Figure 20 is a schematic of the FT-IRRAS signal processing arrange-ment. The signal from the detector preamplifier is split into two channels,

PEM

L Sample

Polarizer

To detector From interferometer

Fig. 19 . Optical configuration for polarization-double-modulation FT-IRRAS. The photoelastic modulator and fixed-polarizer assembly modulate the polarization of the light incident on the sample surface. These optics fit into the sample chamber of an IBM Instru-ments, Inc. IR/98 spectrometer and were used to obtain FT-IRRAS spectra of Langmuir-Blodgett monolayers of cadmium arachidate (Golden and Saperstein, 1983).


and each of these channels is then demodulated separately. The signal that passes through the conventional FT-IR amplifier and filter circuits (Ip + Is), hereafter called the denominator, contains the normal single-beam interferogram of the spectrometer and sample. The other channel, called the numerator, is first demodulated with a phase-sensitive amplifier (lock-in) to produce an interferogram with an amplitude proportional to (Ip — Is). A high-pass filter is used to remove the large dc offset associated with this signal. Because the output of the lock-in amplifier is a filtered dc signal, the interferometer mirror is scanned slowly so that it can accu-rately track the amplitude changes in the interferogram. The interferome-ter mirror is also scanned slowly enough so that a sufficient number of PEM cycles can be accumulated for each digitized step in the mirror position.

Once the denominator and numerator channels have been dealt with in this way, both signals are passed to the FT-IR spectrometer computer with an analog switch. The state of the analog switch is changed at each digital data point of the interferometer mirror position such that the nu-merator and denominator signals are sent to the sample and hold and analog-to-digital circuit alternately. This yields, in one mirror scan of the interferometer, a data file containing both numerator and denominator interferograms in which both numerator and denominator are alternately stored in either even or odd data addresses. This switching technique allows for the simultaneous collection of both numerator (sample) and denominator (reference) interferograms. Finally, conventional software is used on this data file to split out the two interferograms, on the basis of whether the data address is even or odd, to produce two new files, each containing one-half the total number of data points of the original file. The

Detector

Lock-in amplifier

High-pass filter

FT-IR Ampii riers

Switch circuit

► T o

sample and hold and A/D

Fig. 20 . Schematic of the polarization-double-modulation FT-IRRAS signal process-ing arrangement. The signal from the detector preamplifier is split into two channels, and each channel is demodulated separately (Golden and Saperstein, 1983). A/D, Analog-to-digital converter.


numerator and denominator files are then Fourier transformed and ratioed to give an FT-IRRAS spectrum with an amplitude proportional to (Ip -IsWp + /,)·

Figure 21 shows the FT-IRRAS spectrum of six monolayers of cad-mium arachidate deposited by the Langmuir-Blodgett deposition method on a silver substrate taken by the aforementioned method using an IBM Instruments, Inc. IR/98 FT-IR spectrometer. The sample was mounted in the attachment shown in Fig. 19, and the interferograms were recorded at 4 cm"1 resolution and an optical velocity of 0.235 cm/sec. One hundred mirror scans were coadded, Fourier transformed, and then ratioed against the FT-IRRAS spectrum of a clean silver metal substrate. Data ac-quisition time was approximately 4 min. Because one of the goals of polarization-double-modulation FT-IRRAS is to produce insensitivity to gas-phase absorptions, this spectrum was obtained with the FT-IR spectrometer completely open to ambient atmospheric conditions (Golden and Saperstein, 1983).

Not only does this spectrum demonstrate the broad spectral bandwidth capabilities, resolution, and sensitivity of FT-IRRAS, but it is again illustrative of the surface selection rules that allow for the determination of the surface orientation of these types of monolayer assemblies (Allara and Swalen, 1982; Rabolt et al, 1983). The strong band at 1432 cm"1, the

I — t I I

5% I

4000 3500 3000 2500 2000 1500 1000 Wavenumber, cm1

Fig. 2 1 . Polarization-double-modulation FT-IRRAS spectrum of six monolayers of cadmium arachidate adsorbed by the Langmuir-Blodgett dipping technique on silver (Golden and Saperstein, 1983).


symmetric carboxylate stretch, the weaker band at 1577 cm-1, and the antisymmetric carboxylate stretch are consistent with the surface selec-tion rules (see Section II), and the relative intensities of these two bands indicated that the carboxylate groups are oriented with their C2 axes predominantly along the surface normal (see Section II and Chapter 7).

The strong downward spikes in the spectrum in Fig. 19 are due to water vapor and carbon dioxide present in the optical path of the spectrometer. These artifacts arise from the fact that, when the original interferogram file is split into numerator and denominator interferograms, the ampli-tudes are not equal. This amplitude difference, and the slight offset in the position of the center burst, is carried through the phase correction rou-tines and the Fourier transform to produce small differences in the calcu-lated spectra. When ratioed, these differences produce small errors cor-responding to water vapor and carbon dioxide absorption maxima.

One other experimental result, peculiar to FT-IR instrumentation, is discussed here. Due to the way in which the spectrum is calculated, the apparent sign of absorption bands of surface species is dependent on the sign of the quantity (Ip - Is). The sign of this quantity can be affected optically by the insertion of an ir-transparent plate, such as a KBr win-dow, into the optical path of the experiment. By rotating this plate so that the angle of incidence of the radiation transmitted through it changes, control over the amount of parallel- and perpendicular-polarized light transmitted to the surface can be obtained (Golden et al.y 1981). Because only Ip is absorbed by surface species, the sign of the quantity (Ip - Is) can be altered experimentally. The sign of the absorption bands are then affected because, in the computation algorithm, the absolute value of the interferogram (or the computed spectrum) is taken so that the single-beam spectra are stored and represented as positive energy spectra. Conse-quently, all absorptions that affect Ip and Is equally always show positive normal absorption in the spectrum, whereas when only Ip is absorbed, the sign of the apparent absorption peak will depend on the sign of (Ip - Is). Table 1 summarizes these sign rules for polarization-modulated FT-IRRAS.

The distinct advantage of double modulation is its capacity to discrimi-nate against gas-phase absorption in preference to absorption by surface species. This is helpful in the study of the vibrational spectra of monolay-ers absorbed on low-area surfaces in ultrahigh-vacuum conditions be-cause interfacing the FT-IR spectrometer with an ultrahigh-vacuum chamber requires the ir beam to travel some distance outside the optics bench. One approach, to eliminate interference from atmospheric absorp-tion, is illustrated in Section II; that is, the optical path can be purged either by dry air or an inert gas. This can be experimentally inconvenient,


TABLE 1

Sign of Absorption Bands for FT-IRRAS

Apparent sign of the absorption band for

Sign of (Ip - Is) Gas-phase

species Surface species

— + 0

+ + +

-+

Not observed

and so double modulation might be considered an advantage in this situa-tion. Figure 22 shows the optical arrangement for obtaining FT-IRRAS spectra of monolayers adsorbed on single-crystal surfaces under ultra-high-vacuum conditions using polarization-double-modulation FT-IR-RAS with an IBM Instruments, Inc. FT-IR spectrometer. The entire opti-

Sources-

Detector

Ultrahigh vacuum system

Fig. 22. Schematic diagram of the polarization-double-modulation FT-IRRAS spec-trometer in which one channel of the IBM Instruments IR/98 is used for ambient monolayer samples and the other optically incorporates an ultrahigh vacuum system for surface science applications (Golden et aL, 1984a).


cal path, except for that through the vacuum chamber, is open to air. Light from the interferometer is focused through the PEM-fixed-polarizer assembly and onto the sample substrate at grazing incidence. The light is then collected and focused onto a detector, which is located externally to the optics bench.

Figure 23 shows the FT-IRRAS spectrum of CO adsorbed on a plati-num foil at 300 K taken at 4 cm-1 resolution using the spectrometer configured as in Fig. 20. The platinum foil dimensions were approximately 0.5 x 1.0 cm, giving a substrate surface area comparable in size to the usual surfaces studied in ultrahigh-vacuum by other spectroscopies (e.g., Auger electron spectroscopy). The platinum foil was cleaned and an-nealed by first heating to ~900°C at 5.0 x 10"10 torr for 24 hr followed by

■ I I I I I ■

I i i i i i I 2200 2150 2100 2050 2000 1950 1900

Wavenumber, cm 1

Fig. 23. Polarization-double-modulation FT-IRRAS spectra of CO on platinum foil at 300 K at 4 cm"1 resolution. The upper trace is the spectrum of a saturation coverage of CO on the recrystallized platinum foil. The lower trace is the spectrum of a low coverage of CO obtained when the foil was only partially cleaned and annealed (Golden et al., 1984a).


heating in 1.0 x 10-6 torr oxygen for 24 hr. Carbon monoxide was ad-sorbed on the platinum foil at room temperature.

The bottom trace in Fig. 23 shows an FT-IRRAS spectrum of CO adsorbed on the platinum foil during the early stages of annealing and cleaning. From the frequency of the absorption maximum the coverage of CO was inferred to be less than 20% of saturation coverage (Shigeishi and King, 1976, 1977). Because the band is broad and there is a distinct shoul-der to the low-wavenumber side of this band, it is also possible to con-clude that there are several different ill-defined adsorption sites for CO on this surface (Baker and Chesters, 1982; Kawai et al., 1981; Ortega et al., 1982).

The upper trace in Fig. 23 is the FT-IRRAS spectrum of CO adsorbed at saturation coverage on a completely cleaned and annealed platinum foil. Because the band is singular and well resolved, with a bandwidth of less than 15 cm-1, it is concluded that the CO adsorption sites are well defined and that the surface of the platinum foil consists mostly of low-index single-crystal planes. Also, the shift in the band maximum with CO cover-age is consistent with previous observations of the ir spectrum of CO on single-crystal platinum surfaces (Hoffman, 1983; Bradshaw, 1982).

A final example of the versatility of polarization-double-modulation FT-IRRAS is given by its application to the study of monolayers adsorbed on electrode surfaces in the presence of electrolyte. Numerous workers have demonstrated the feasibility of obtaining ir spectra of monolayers on electrodes in situ by using a number of techniques (Bewick et al., 1980, 1984; Kunimatsu, 1982). The advantage of polarization-double-modula-tion FT-IRRAS in this case is that a spectrum of the electrode-electrolyte interface can be obtained under equilibrium conditions, that is, with the electrode held at fixed potential. In this way, band-center positions and intensities of ir absorptions of surface species can be obtained as a func-tion of electrode potential.

The spectroelectrochemical cell (Golden et al., 1984b; Bewick et al., 1980) and optical arrangement used to make these measurements are shown in Fig. 24. The entire assembly can be placed in the sample com-partment of the FT-IR spectrometer, and because polarization-double-modulation FT-IRRAS does not require the optical path to be in purge or vacuum, the electrochemistry can be dealt with easily. Light from the interferometer is passed through the fixed-polarizer-PEM assembly and is then focused through a 65° beveled CaF2 window onto the electrode-electrolyte interface. The electrode is pressed against the window with a piston arrangement until sufficient throughput to the detector is achieved. This gives good reflectivity from the electrode with approximately 1 to 2 μτη of electrolyte between the electrode surface and the window.


Cell

To detector Electrode

\ CaF2

window

Polarizer

, From I interferometer

I

PEM

Fig. 24. Optical arrangement used to obtain polarization-double-modulation FT-IRRAS spectra of monolayers adsorbed on an electrode surface in situ. The fixed polarizer and photoelastic modulator modulate the incident polarization state of the light focused on the electrode surface through the CaF2 window and electrolyte (Golden et al., 1984b).

Figure 25 shows a series of FT-IRRAS spectra of CO adsorbed on a smooth platinum electrode as a function of electrode potential in 1 N H 2 S0 4 . The surface of the platinum electrode was prepared by polishing with alumina until a mirror surface was obtained. Before adsorption of CO, the electrode was subjected to several anodic-cathodic potential sweeps to clean the surface. Carbon monoxide was then bubbled through the electrolyte to saturation. After 10 min the electrode potential was cycled between 0.0 and 1.5 V (NHE) in order to monitor the CO oxidation to carbon dioxide. For all the spectra shown, the carbon monoxide was initially adsorbed at 0.4 V (NHE).

A plot of the variation in band-center position and integrated band area with electrode potential is shown in Fig. 26. The wavenumber shift is essentially linear with electrode potential, and the integrated area remains constant until the onset of oxidation at —0.55 V (NHE), indicating that the coverage of the linear CO species is unaffected until oxidation takes place. The frequency shift with electrode potential (30 cm~VV) is identi-cal for 1 N HC104 and HC1 electrolytes as well (Golden et al, 1984). This indicates that the frequency shift of the linear CO stretch is not due to changes in the dipole moment of the molecule induced by Stark effects (Lambert, 1983) as a function of the electrode potential, because the local


5k

Ί 1 1 Γ 2100 2000 Wavenumber, cm-1

Fig. 25 . Series of polarization-double-modulation FT-IRRAS spectra of CO adsorbed on a smooth platinum electrode as a function of electrode potential in 1 N H2S04 (Golden et al., 1984; Kunimatsu et aL, 1984).

electric field in the double layer may vary substantially with electrolyte composition. Rather, it is more likely to be due to backdonation of surface metal electrons into the 2π* orbital of the adsorbed CO molecule (Bly-holder, 1964). This backdonation produces a change in the CO bond or-der, which consequently changes the value of the principle force constant associated with the CO stretching vibration. This is reasonable when one considers the relatively low polarizability of the CO molecule.

Although there does not appear to be a coverage-dependent frequency shift of the linear CO stretching vibration at potentials in which oxidation


r .1 .2 .3 .4 .5 .6 .7 .8

E,V(NHE) Fig. 26. Plot of the variation in band-center position and integrated area with electrode

potential for the spectra shown in Fig. 25 of CO on a platinum electrode (Golden et al., 1984b; Kunimatsu et al., 1984).

to carbon dioxide is occurring, this effect could be overwhelmed by the strong voltage dependence of the band-center position.

IV. CONCLUSION

It is the purpose of this chapter to show the impact of FT-IR on surface vibrational spectroscopy. Although many of the experimental and theo-retical concepts were originally developed with dispersion ir spectrome-ters, the advent of FT-IR has added substantial improvements in S/N, energy resolution, and spectral bandwidth.

In addition, even though polarization-double-modulation FT-IRRAS further enhances surface sensitivity, it is FT-IR that allows the experi-mentalist to study not only thin films, but, in favorable circumstances, monolayers adsorbed on low-area surfaces by the use of simple fixed-polarizer grazing incidence techniques.

REFERENCES

Allara, D. L., and Swalen, J. D. (1982). J. Phys. Chem. 86, 2700. Baker, M. D., and Chesters, M. A. (1982). In "Vibrations at Surfaces" (R. Cauduano, J. M.

Gilles, and A. A. Lucas, eds.), pp. 289-298. Plenum, New York. Bewick, A., Kunimatsu, K., and Pons, B. S. (1980). Electrochim. Acta 25, 465.


Bewick, A., Kunimatsu, K., Pons, B. S., and Russell, J. W. (1984). J. Electroanal. Chem. 160, 47.

Blanke, J. F. (1975). Ph.D. Dissertation, University of Minnesota, Minneapolis. Blanke, J. F. , Vincent, S. E., and Overend, J. (1976). Spectrochim. Acta, Part A 32A, 163. Blodgett, K. B. (1939). Phys. Rev. 55, 391. Blodgett, K. B. (1953). J. Am. Chem. Soc. 57, 1007. Blyholder, G. (1964). J. Chem. Phys. 68, 2773. Born, M., and Wolf, E. (1959). "Principles of Optics." Pergamon, Oxford. Bradshaw, A. M. (1982). Appl. Surf. Sei. 11/12, 712. Campion, A. (1983). In "Vibrations at Surfaces" (C. R. Brundle and H. Morawitz, eds.),

p. 397. Am. Elsevier, New York. Chabay, I. (1972). Ph.D. Dissertation, University of Chicago, Chicago, Illinois. Crossley, A., and King, D. A. (1977). Surf. Sei. 68, 528. Dowry, A. E., and Marcott, C. (1982). Appl. Spectrosc. 36, 414. Eischens, R. P., and Pliskin, W. A. (1958). Adv. Catal. 10, 1. Fleischmann, M., Hendra, P. J., and McQuilla, A. J. (1974). Chem. Phys. Lett. 26, 163. Francis, S. A., and Ellison, A. H. (1959). J. Opt. Soc. Am. 49, 131. Golden, W. G., and Saperstein, D. D. (1983). J. Electron Spectrosc. Relat. Phenom. 30, 43. Golden, W. G., Dunn, D. S., and Overend, J. (1981). J. Catal. 71, 395. Golden, W. G., Saperstein, D. D., Severson, M. W., and Overend, J. (1984a). J. Phys.

Chem. 88, 574. Golden, W. G., Kunimatsu, K., and Seki, H. (1984b). / . Phys. Chem. 88, 1275. Goplin, T. (1976). Ph.D. Dissertation, University of Minnesota, Minneapolis. Greenler, R. G. (1966). J. Chem. Phys. 44, 310. Hayden, B. E., and Bradshaw, A. M. (1983). Surf. Sei. 125, 787. Hoffman, F. M. (1983). Surf. Sei. Rep. 3, 107. Ibach, H. (1972). J. Vac. Sei. Technol. 9, 713. Ishitani, A., Ishida, H., Soeda, F., and Nagasawa, Y. (1982). Anal. Chem. 54, 682. Jeanmaire, D. L., and Van Duyne, R. P. (1977). J. Electroanal. Chem. Interfacial Electro-

chem. 84, 1, and references therein. Kawai, M., Onishi, T., and Tamura, K. (1981). Appl. Surf Sei. 8, 361. Kemp, J. C. (1969). J. Opt. Soc. Am. 59, 950. Krishnan, K., and Ferraro, J. R. (1982). In "Fourier Transform Infrared Spectroscopy"

(J. R. Ferraro and L. J. Basile, eds.), Vol. 3, p. 149. Academic Press, New York. Kunimatsu, K. (1982). J. Electroanal. Chem. Interfacial Electrochem. 140, 205. Kunimatsu, K., Seki, H., and Golden, W. G. (1985). Langmuir (In press). Lambert, D. K. (1983). Phys. Rev. Lett. 50, 2106. Langmuir, I. (1917). J. Am. Chem. Soc. 39, 1848. Mclntyre, J. D. E., and Aspnes, D. E. (1971). Surf Sei. 24, 417. Mahan, G. D., and Lucas, A. A. (1978). J. Chem. Phys. 68, 1344. Moskovits, M., and Hülse, J. E. (1978). Surf Sei. 78, 397. Nafie, L. A., Diem, M., and Vidrine, D. W. (1979). J. Am. Chem. Soc. 101, 496. Ortega, A., Hoffman, F. M., and Bradshaw, A. M. (1982). Surf Sei. 119, 79. Ottesen, D. K., and Nagelberg, A. S. (1980). Thin Solid Films 73, 347. Pritchard, J., and Simms, M. L. (1970). Trans. Faraday Soc. 66, 427. Rabolt, J. F., Burns, F. C , Schlotter, N. E., and Swalen, J. D. (1983). J. Chem. Phys. 78,

946. Scheffler, M. (1979). Surf Sei. 81, 562. Shigeishi, R. A. and King, D. A. (1976). Surf Sei. 58, 379. Shigeishi, R. A. and King, D. A. (1977). Surf Sei. 62, 379.

FOURIER TRANSFORM INFRARED PHOTOACOUSTIC SPECTROSCOPY OF CONDENSED-PHASE SAMPLES

J. A. Graham Hercules Inc. Research Center Wilmington, Delaware

W. M. Grim III Nicolet Analytical Instruments Burlington, Massachusetts

W. G. Fateley Department of Chemistry Kansas State University Manhattan, Kansas

IV.

Introduction Background Theory of the Photoacoustic Effect A. Qualitative Description B. Quantitative Description C. How FT-IR-PAS Differs from Dispersive

PAS D. Background Correction Cell Design A. Optimization B. Nonambient Conditions C. Photoacoustic Spectroscopy Cells Applications A. General B. Polymers C. Coal D. Adsorbed Species E. Quantitative Analysis F. Depth Profiling G. FT-PAS beyond the Mid-infrared Conclusions References

346 347 348 349 350

355 357 360 360 363 364 367 368 370 374 375 376 381 385 389 390

VI.


ISBN 0-12-254104-9

346 J. A. Graham, W. M. Grim III, and W. G. Fateley

Of course, the ear cannot for one moment compete with the eye in the examination of the visible part of the spectrum; but in the invisible part beyond the red, where the eye is useless, the ear is invaluable.

Bell, 1881

I. INTRODUCTION

As both dispersive and Fourier transform infrared instrumentation have been greatly improved, the most fruitful direction of spectral enhance-ment has been the introduction of new sample handling techniques. Al-though the acquisition of spectral data for samples in the gas phase, whether in a multipass cell or from the atmosphere, has become facile, adsorbed gases are still difficult to study.

Solid samples have continued to plague the infrared (ir) spectroscopist. Potassium bromide pellets, hydro-/fluorocarbon mulls, and solutions all interact with the sample to some extent and adsorb radiation at their own characteristic frequencies as well. Most techniques of solid sampling in-volve preparations that can markedly affect the sample morphology. Neat samples, although desirable, almost always yield transmission spectra with strongly absorbing, nondiagnostic bands. The ideal technique would produce spectra that retain all the characteristics of a conventional spec-trum, from a sample that has undergone a minimum of preparation and no interaction with any diluents. There would not be any restrictions on sample size or shape, aside from limitations imposed by normal detection limits.

So far, no single technique has fulfilled all of these specifications. Con-ventional transmission spectra are still by far the most common and are the standard to which spectra acquired by other techniques are compared. Diffuse reflectance (DR) spectroscopy (DRIFTS when Fourier transform instrumentation is used) has been a very satisfactory technique for most samples, but samples with smooth surfaces still present problems. Fur-thermore, in order to obtain a DR spectrum that is comparable to a trans-mission spectrum, the sample should consist of small, uniform particles (Griffiths and Fuller, 1982). Often, it is difficult to grind such samples as hard plastic buttons or strands of hair. Finally, the sample morphology can be irreversibly altered when it is prepared for DR; this is particularly so in the study of surface chemistry. In such cases as these, it is often worthwhile to try photoacoustic spectroscopy (PAS).

9 Photoacoustic Spectroscopy of Condensed-Phase Samples 347

II. BACKGROUND

The photoacoustic (PA) effect was first reported by Bell (1880). He observed that thin diaphragms emitted sounds when exposed to modu-lated sunlight that had been focused onto the diaphragms. It was not until later in 1880 that the effect was extended to other forms of solids and to liquids and gases. In his experiments (Bell, 1881), a sample was placed in a brass cavity with a glass window (Fig. 1). A brass tube was connected to one side and served as the sound conduction tube to the ear. Focused sunlight was modulated by a rotating wheel and directed toward the sam-ple. The samples that produced the loudest sounds were those of a loose or porous nature and those that had the darkest colors. At that time Bell realized that his invention might have its most important applications in the ir: ςΤ recognize the fact that the spectrophone must ever remain a mere adjunct to the spectroscope; but I anticipate that it has a wide and independent field of usefulness in the investigation of absorption-spectra in the ultra-red."

After Bell's discovery, the PA method was to remain in the background until a transducer more sensitive and selective than the ear could be found. With the introduction of the microphone, resonant PA cells were used in photometry, but only intermittently. It was not until the early 1970s, largely through the work of Rosencwaig, that a renaissance of the technique occurred. Rosencwaig (1973) presented the first nonresonant PA cell, the initial step in the development of the first commercial PA cell. Some of the parameters affecting PAS of condensed-phase samples were discussed by McClelland and Kniseley (1976).

The first ir studies of solid samples by means of PAS utilized tunable lasers as sources. Although lasers provided adequate power, their narrow range of spectral coverage limited their versatility. Broad-band sources

Fig. 1. Sample cell and sound conduction tube used by Bell to transmit signals to the first photoacoustic detector: his ear (Bell, 1881).


have been used to acquire PA spectra (Low and Parodi, 1980a), but the signal-to-noise ratio (S/N) was generally too low to produce satisfactory spectra. The throughput and multiplex advantages of Fourier transform interferometry (FT-IR) (Griffiths, 1975) made the utilization of a broad-band source in ir PAS feasible. As FT-IR became increasingly competi-tive with dispersive spectrometry, it was only a matter of time before someone coupled these two powerful techniques.

Mead et al. (1979) and Rockley (1979) independently reported FT-IR-PAS experiments performed on condensed-phase samples. The following year, Vidrine (1980) presented further FT-IR-PAS studies and demon-strated that the technique could easily handle samples that were other-wise intractable. Further development and use have led to the study of surface chemistry under a variety of conditions.

In the following sections, the theory of PAS is examined, cell design is discussed, handling of FT-IR-PAS data is explored, and various applica-tions of FT-IR-PAS are investigated. Finally, potential applications and the future of FT-IR-PAS are discussed.

III. THEORY OF THE PHOTOACOUSTIC EFFECT

The fundamental difference between PAS and conventional ir spectros-copy is the way in which the absorption of the incident radiation is de-tected. In conventional ir spectroscopy, the absorption of light is deter-mined by detecting the amount of light transmitted using an optical transducer and comparing this value with that obtained with no sample present. The linear detection range of this type of experiment is therefore limited to samples with medium-range absorption coefficients ß. Samples with small values of ß are excluded because the transmission of the inci-dent light is of nearly the same magnitude as that of the reference signal, so one must discriminate between two large signals. Conversely, samples with large values of ß are excluded beause the magnitude of the transmit-ted signal is very small compared with that of the reference signal and is easily lost in the background noise.

In contrast, the absorption of light in PAS is detected with an acoustic transducer that measures a pressure change in the surrounding gas. The pressure change is caused by periodic temperature fluctuations at the sample surface due to the conversion of the absorbed energy to heat. Therefore, the linear detection range of this type of experiment is greatly improved for samples with small values of ß, because the induced pres-sure change is directly proportional to the energy absorbed. The limit is determined by several factors, of which S/N is one. The linear detection range for samples with large values of ß is slightly better but is limited by


PA signal saturation problems. The inherent advantage of a PA signal that is directly proportional to the absorbed light should allow for the detec-tion of very weakly absorbing samples.

A. Qualitative Description

Radiation from a mid-ir source is passed through the interferometer, where it is modulated by the moving mirror. The modulated radiation is then focused onto the sample, which is placed inside an enclosed cavity (Fig. 2). The cell has an optical window for transmitting radiation in the region of interest (e.g., KBr, quartz, or polyethylene). The inside of the cell is filled with an ir-transparent gas the thermodynamic properties of which determine its usefulness. On penetrating the sample, the incident light is selectively absorbed, depending on the values of ß for the sample in that region.

The absorbed energy may then be released through two basic processes. One form of release is the emission of light, and the other form, by far the more common, is a nonradiative decay process or heat release. The heat is then transferred to the sample surface at a rate dependent on the thermal

MIRROR

ACOUSTIC_ WAVES

SAMPLE HEAT ^ t- ' HEAT

ABSORPTION

INSULATION \ MICROPHONE

Fig. 2. Schematic diagram of a photoacoustic cell.


characteristics of the sample. At the sample-gas interface, the heat is coupled into the surrounding fill gas, which subsequently produces a change in pressure. Because the incident light is modulated, the produc-tion of heat and the corresponding pressure change will also be modulated at a rate equal to that of the incident light. The acoustic wave is then propagated through a small connecting passage to the microphone cham-ber. The microphone detects the pressure change, and the supporting electronics convert the pressure change to a voltage. The resulting signal contains both magnitude and phase information, because the production and transfer of heat cause some delay in the detection of the acoustic signal. The value of the phase component is dependent on the conversion rate for the heat release of the sample as well as thermal properties of both the sample and the gas. The type of information that can be obtained from the phase component is discussed in Section III.C.

B. Quantitative Description

The quantitative description of the PA effect has already been devel-oped by several researchers working in the uv-visible region of the spec-trum. Parker (1973) developed a theoretical treatment of the weak signal that would be produced by the absorptions of ''transparent" windows to explain unusually large anomalies encountered in quantitative PAS stud-ies of gases. Although his theoretical treatment was a special case, it does lend some insight into the formulation of a quantitative treatment for condensed-phase samples. A few years later, a one-dimensional model for the PA effect in condensed-phase samples was developed by Rosencwaig and Gersho (1975, 1976). This theoretical treatment is commonly referred to as the R-G theory. The R-G theory uses exact equations to treat the periodic temperature development at the sample surface. The generation of the acoustic wave and its propagation are treated in a more simplistic manner. Further refinements of the theory have been made. McDonald and Wetsel (1978) developed a refined theory that takes into account the thermally induced mechanical vibrations of the sample. Quimby and Yen (1980) showed that the one-dimensional model applies to three-dimen-sional samples as long as the radius of the sample chamber is much greater than the thermal diffusion length of the gas. The signal depen-dence on modulation rates has been discussed for both one-dimensional and three-dimensional systems. Chow (1981) showed that at low and in-termediate modulation rates, deviation from the R-G theory may occur when the sample and backing materials differ in their thermal properties.

Even though the early theoretical models were developed for work in the uv-visible region of the spectrum, they are still applicable to FT-IR-


TABLE 1

Parameters Used in Section

Time (sec) Frequency of light in wavenumbers ( c m 1 ) Wavelength of light (μιτι)

x v Modulation rate (Hz) Intensity of incident light with wavelength λ Optical absorption coefficient of the sample (cm ') Optical absorption length of the sample (cm) Sample thickness (cm) Thermal conductivity (cal cm- 1 sec ' °C ') Density (g c m 3 ) Specific heat (cal g~' 0C~') Thermal diffusivity (cm2 s e c 1 ) Thermal diffusion coefficient ( c m 1 ) Thermal diffusion length (cm), where subscripts s and g

denote sample and gas parameters, respectively Depth below surface of sample (cm) Incremental pressure change as a function of time Complicated expression that describes the complex envelope

of the pressure variation Phase error as a function of wavenumber (radians) Distance from the sample surface to the optical window (cm)

Denotes sample parameters Denotes gas parameters Denotes background-material parameters

ω = (optical velocity) h ß Lß = \lß

P

c a = κ/pC a = (ω/2α)1/2

μ8 or μ8 = 1 la

x δΡ(/) Q

θ(ν) Y

Absence of prime (κ) Single prime (κ') Double prime (κ")

PAS. Some differences do exist, and these are primarily owing to the way in which the interferometer modulates the radiation. Additional devia-tions result from the way in which phase information is obtained. These topics are discussed in Section III.C. As an explanation of the theoretical aspects of PA signal generation, propagation, and detection, an overview of the R-G theory is presented here. For a more in-depth treatment, the reader is directed to studies reported by Rosencwaig (Rosencwaig and Gersho, 1976; Rosencwaig, 1980).

The parameters used in this section are listed in Table 1. The intensity of the incident light on the surface of the solid sample varies sinusoidally and is given by

£/0(l + cos 2πωί) (1)

where /0 is the intensity of the incident light with wavelength λ, and ω the modulation rate in cycles per second. Because the absorption of light as a function of sample thickness is an exponential process, the amount of


light penetrating to a depth x below the surface is given by

y{)eßx(\ + cos 2πωί) (2)

where β is the optical absorption coefficient in reciprocal centimeters for the sample at wavelength λ. Expression (2) represents the intensity of the incident light that penetrates to a depth x below the surface. The amount of heat that can be produced at any given depth x is dependent not only on this intensity, but on the fraction of this light that will be absorbed. There-fore, the heat density produced at a point at depth x is given by

hßIoeßx(\ + cos Ιπωί) (3)

Not all heat produced within the sample will be effectively transferred to the surface. As the heat is conducted within the sample, it is dampened so that only the heat generated within one thermal diffusion length μ8 of the surface is detected. The value of μ8 depends on the thermal diffusivity a of the sample as well as the modulation rate and is given by

(2α/ω)1/2 (4)

The heart of the R-G theory is the treatment of the buildup and diffusion of heat within the sample. It is believed that this heat causes a periodic temperature fluctuation at the sample-gas interface. Only the gas mole-cules within approximately one gas thermal diffusion length μδ will be affected by the heat flux. These molecules will undergo a pressure change and act on the rest of the gas molecules like a sinusoidally driven piston.

The thermal development and diffusion for the general case have been treated according to the R-G theory by solving the appropriate differential equations. By assuming ideal gas behavior, Rosencwaig and Gersho de-veloped an expression for the incremental pressure change as a function of time. The pressure change 3P(t) is given by

0βΐ(2ττωί-π/4) ί$\

where Q is a complicated expression that describes the complex envelope of the pressure variation. Owing to the complicated nature of Q, the expression is difficult to interpret and not of immediate interest for the spectroscopist, who is concerned primarily with the final result, rather than what produced that result.

Rosencwaig and Gersho described six different cases in which Q is greatly simplified by making a few approximations. These cases are dia-gramed in Fig. 3 (Rosencwaig, 1980). The samples are first divided into two different classes according to their optical opacity. Each class is subdivided into three separate cases based on the relationship between


1

1

I

Ms

Ms

Case T,

Case T2

Case T3

1 1 I

1 1 1

1-0=1/0 b

Fig. 3 . Illustration of the photoacoustic signal dependence on optical absorption length Lß, thermal diffusion length μ,, and sample length b. From Rosencwaig (1980), with permis-sion. Copyright John Wiley & Sons, Inc. Optically transparent: (Case T,) Q — [(1 - /)/ 2α']β*(μ"/κ")γ, (Case T2) Q - [(1 - ΐ)/2α']β*(μ"/κ")γ, (Case T3) Q - {-ιΙ2α')βμ,{μ,Ικ)γ\ optically opaque: (Case O,) Q - [(1 - ί)/2α'](μ"/κ")γ, (Case 02) [(1 - ΐ)/2α'](μ&/κ)γ, (Case 03) Q =* {-ΐΙ2α')βμ,{μ,Ικ)γ. For all cases: μ, = Ma = λ/2α/ω; Lß = \/ß.

the sample's thermal diffusion length μ8 and its optical absorption length

1. Optically Transparent Samples

The first class of samples to be considered are those that absorb only part of the incident radiation; that is, the optical absorption length Lß is greater than the sample thickness b. For these types of samples, radiation will be absorbed throughout the sample. This does not necessarily mean that the resulting PA signal will contain information from the entire sam-ple, because only the heat originating from within one thermal diffusion length from the surface is detected. The source of the signal within the sample depends on the relative values of the thermal diffusion length /zs, the optical absorption length Lß, and the sample thickness b.

a. Case T}: Thermally Thin. For these types of samples (μ8 > b\ μ8 > Lß), the R-G theory expression for Q reduces to

(1 - i)ßb(^lKn)yl2a' (6)

The PA signal has a modulation dependence proportional to ω ι because μ'Ία' reduces to 2α/ω. The PA signal will be proportional to ßb and should


be quantitative with sample thickness b. Because μ8 > b, the resulting PA signal will be strongly influenced by the thermal properties of the material used for the sample holder.

b. Case T2: Thermally Thin. For these types of samples (μ8 > b\ μ8 < Lß), the R-G theory expression for Q reduces to

(1 - i)ßb{ii"lK")yl2a' (7)

This expression is identical to that for case T j . The PA signal is propor-tional to ßb and has a modulation dependence proportional to ω"1.

c. Case T3: Thermally Thick. For these types of samples (μ8 < b\ μ8 < Lß), the R-G theory expression for Q reduces to

-ίβμ,(μ,/κ)γ/2α' (8)

This case differs from the two previous cases in that the PA signal is proportional to βμ8 rather than ßb. Only the light absorbed within the first thermal diffusion length will contribute significantly to the PA signal. Therefore, the material used for the sample holder does not contribute any signal, even though the sample is optically transparent. The PA signal will have a modulation dependence proportional to ω_3/2 because Q is proportional to μΐΐα'. The magnitude of the modulation rate and conse-quent value of μ8 will determine the depth below the surface from which heat is effectively transferred and detected. If the modulation rate is very fast, the spectrum will consist primarily of information from the surface layers. As the modulation rate is decreased, the thermal diffusion length will increase, and the spectrum will reflect information from the bulk of the sample. It is therefore possible to perform a depth-profile analysis on the sample.

2. Optically Opaque Samples

Samples of this type exhibit an optical absorption length Lß that is smaller than the thickness of the sample b. In this class of samples, most of the light is absorbed or scattered off the front surface, and very little light, if any, is transmitted. Most of the absorbed light is absorbed within a short distance of the surface. Spectra represent information primarily from the surface and exhibit bulk properties only for samples with inter-mediate values of ß and at low modulation rates.

a. Case Oj: Thermally Thin. For these types of samples (μ8 > b\ μ8 > Lß), the R-G theory expression for Q reduces to

(1 - ι){μΠκ")γΙ2α' (9)


This case is quite different from any of those found in the optically trans-parent class because the PA signal is independent of ß. The PA signal depends on the thermal properties of the material used for the sample holder and has a modulation dependence that varies as a function of ω_1. A good example of this type of sample is carbon black, which has a very large value for β and a. This is also why carbon black has been proposed as a good material for the collection of a background spectrum.

b. Case 02: Thermally Thick. For these types of samples (μ8 < b; μ* > Lß), the R-G theory expression for Q reduces to

(1 - i){pJK)ylla' (10)

The expression for Q is nearly identical to that in case 0\, except that the expressions for thermal properties of the sample holder material are re-placed by the expressions for the thermal properties of the sample. This type of sample is also independent of ß and is thermally as well as opti-cally opaque. The signal will have a modulation dependence proportional to ω- 1 .

c. Case 03: Thermally Thick. For these types of samples (μ8 < b\ μ* < Lß), the R-G theory expression for Q reduces to

-ίβμ,(μ$/κ)γ/2α' (11)

Samples of this type form a very important case. Even though they are optically opaque, the PA signal depends on β. This can be understood mathematically by comparing the values of μ8 and Lß. As long as μδ < Lß, only the light absorbed within the first thermal diffusion length will con-tribute significantly to the signal. The PA signal will be proportional to β and, as in case T3, will have a modulation dependence proportional to ω"3/2.

C. How FT-IR-PAS Differs from Dispersive PAS

1. Frequency Modulation

There are two basic differences between FT-IR-PAS and dispersive PAS. The first is the way in which the light is modulated before it encoun-ters the sample. In FT-IR-PAS, all frequencies are modulated simultane-ously by a moving mirror. In dispersive PAS, a monochromator is used to select a narrow band of radiation that is modulated with a mechanical chopper. Because the interferometer modulates all frequencies simultane-ously, the data collection time is drastically reduced (Griffiths, 1975). In addition, the absence of slits results in greater throughput of radiation for


interferometers (Griffiths, 1975). Some authors warned (Low et al., 1982), however, that the increased throughput enjoyed by the interferometer may subject the sample to enough energy to cause it to decompose. They also suggested that the value of increased throughput has been adequately stressed and that more emphasis must be placed on decreasing the noise level.

Owing to the nature of radiation modulation in the interferometer, all frequencies are not modulated at the same rate. The modulation rate, in hertz, can be calculated using

ω = OP. VEL. x v (12)

where OP. VEL. is the optical velocity of the interferometer, and v the frequency of the light source in wavenumbers. Because the thermal diffu-sion length μ8 is dependent on the modulation rate ω, the resulting spec-trum will not be representative of a single depth but of a depth that will vary linearly with wavelength. The spectrum may be corrected for modu-lation rates to a spectrum that more closely resembles a conventional transmission spectrum. Further discussion can be found in Section V.

2. Phase Information

The second fundamental difference between FT-IR-PAS and disper-sive PAS is the way in which phase information is collected. In dispersive PAS a lock-in amplifier is used to determine the phase of the PA signal. The use of a lock-in amplifier is precluded by FT-IR because a wide range of modulation frequencies are present. However, it is still possible to obtain phase information from an FT-IR-PAS experiment. Before the interferometric data can be transformed, a phase correction must first be applied. The phase correction represents a measure of the phase error as a function of wavelength. The phase angle Θ, in radians, is given by

arc Uin[q(v)/p(v)] + LTT (13)

where q{v) is the imaginary or sine Fourier transform, and p{v) the real or cosine Fourier transform (Chamberlain, 1979). Because the tangent func-tion is positive in the first and third quadrants, the quantity LTT denotes that only -π/2 to +πΙ2 resolution is possible. If we let

L = - i [ l - p{v)l | p(v) \\[q(v)l | q(y) |] (14)

then θ(ν) represents the phase error from -π to +7Γ. Some FT spectrome-ters use only a small portion of the interferogram located about the center burst for phase correction, so the resolution of the phase spectrum in these instruments may be too low for most applications. However, exper-


iments with low phase resolution may still be performed with these instru-ments.

D. Background Correction

As the application of PAS has spread to more and more areas, the need for a correction of the source and instrument profiles has become appar-ent. In visible PAS, the single-beam spectrum is often compared with the power spectrum of the source as it is measured by a photocell or some other power-sensing device. Another method that is increasing in popu-larity is the use of a carbon black spectrum as a background spectrum. Because carbon is essentially a blackbody absorber, the resulting spec-trum should provide a good representation of the source and instrument profiles.

A study of carbon black samples for use in background correction was performed using visible-dispersive PAS (Lochmüller et al.y 1981). These authors reported that the spectrum of Norit-A decolorizing carbon devi-ated very little from the spectrum of the source when detected by a pyroelectric detector. Low and Parodi (1980b) reported that reference

I 1 1 1 1 1 1 1—-—i 1 r - I - T -4000 3200 2400 1800 1400 1000 600

WAVENUMBER

Fig. 4 . Photoacoustic spectra of four different carbons at a velocity of 0.101 cm/sec ( , Norit-A (MCB); , Darco G-60; ---, charcoal wood powder; , Nuchar C-190-N; , response from a DTGS detector). Spectra recorded on a Nicolet 7199A FT-IR spectrometer. From Riseman and Eyring (1981), by courtesy of Marcel Dekker, Inc.


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Fig. 5. Single-beam photoacoustic spectra of Norit-A acquired at nine scanning velocities (centimeters per second), as follows: A, 0.059; B, 0.070; C, 0.083; D, 0.099; E, 0.118; F, 0.140; G, 0.166; H, 0.198; I, 0.236. Spectra were recorded on an IBM 98 spec-trometer.

materials commonly used for background correction in the visible region are not necessarily adequate for the ir. They examined several materials that had been charred under vacuum pyrolysis. Although these materials were adequate for background correction in the visible region of the spec-trum, they exhibited spectral features in the ir. The conclusions were that no one type of carbon would be adequate for background correction,

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Fig. 6. Single-beam background spectra acquired at nine scanning velocities (centi-meters per second) using a DTGS detector, as follows: A, 0.059; B, 0.070; C, 0.083; D, 0.099; E, 0.118; F, 0.140; G, 0.166; H, 0.198; I, 0.236. Spectra were recorded on an IBM 98 spectrometer.


especially because carbon has a large capacity for chemical and physical adsorption.

Another group (Riseman and Eyring, 1981) examined a number of com-mercial carbon samples to ascertain their suitability as background cor-rection materials. The single-beam response of each material was ob-tained at three different mirror velocities. Each material was compared with itself at the various velocities and with the other materials at the low and high mirror velocities. Figure 4 shows the comparison of the single-beam response for four different carbon samples at the low mirror veloc-

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(B)

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Fig. 7. (A) Photoacoustic spectrum of carbon acquired at a velocity of 0.099 cm/sec ratioed against carbon acquired at a velocity of 0.083 cm/sec. (B) Background spectrum acquired at a velocity of 0.099 cm/sec ratioed against background spectrum acquired at a velocity of 0.083 cm/sec using a DTGS detector. (C) Ratio of a carbon photoacoustic spec-trum against a DTGS background spectrum; both single-beam spectra were acquired at a velocity of 0.099 cm/sec. All spectra were recorded on an IBM 98 spectrometer.


ity (Riseman and Eyring, 1981). It was found that the spectral response curve for each material varied as a function of mirror velocity. The con-clusion was that no one carbon sample would be adequate for use as a background correction material. It was recommended that a deuterated triglycine sulfate (DTGS) detector be used for the background correction function.

We present the single-beam spectra of Fisher Norit-A at nine different mirror velocities in Fig. 5, and the single-beam spectrum of an empty spectrometer as recorded by a DTGS detector in Fig. 6. In Fig. 7, the ratioed spectrum of carbon at two velocities is shown, as is the ratioed spectrum of the DTGS detector at two different velocities, as well as the ratio of the spectrum of carbon black against the DTGS spectrum at the same velocity. It is clear that the response of the carbon black does not change significantly with mirror velocity. This is an indication that the broad Helmholtz resonance normally associated with the PA cell occurs at a modulation rate beyond the range used here. In addition, the carbon black spectrum obtained in the PAS cell is strikingly similar to that re-corded by the DTGS detector. Because fewer spectral artifacts are likely to arise when both the sample and the background correction material are collected in the same manner, it is our recommendation that Fisher Norit-A decolorizing carbon be used as the sample in the PAS cell for the collection of the source and instrument profiles used as background spectra.

IV. CELL DESIGN

A. Optimization

Many factors affect the construction and use of a PA cell. One of the first to be considered here is the elimination of the background signal that is due to the absorption of stray light by the cell walls. Because the amplitude of the stray-light signal is independent of the sample absorptiv-ity, this signal could theoretically be subtracted out as part of the back-ground. This has already been performed with satisfactory results (Kreu-zer, 1978). It is always best, however, to keep the background signal to a minimum, especially because analog-to-digital (A/D) converters have a limited dynamic range.

1. Materials

To reduce the magnitude of the background signal, the cell walls and sample holder should be made of a nonabsorbing material, preferably


stainless steel or brass. Nonabsorbing materials will prevent the cell walls from producing a signal that will contribute to the overall PA signal. The materials should be highly polished, because they will be easier to keep clean and the unavoidable background signal will be kept to a minimum. In addition, the material used for the sample holder should be chosen such that a minimum amount of heat is conducted by the sample into the sample holder. The sample holder should be relatively inert to most kinds of materials and have no significant absorption in the spectral region of interest. Stainless steel, with its relatively low thermal conductivity, fairly inert character, and essentially featureless ir absorption spectrum, is the best all-around choice for the sample holder material.

2. Volume

One of the major factors affecting the PA signal is the cell volume. Because an acoustic transducer is used to detect the absorbed radiation, the PA signal is inversely proportional to the cell volume. Usually, the cell consists of two chambers connected by a narrow passage. One cham-ber contains the sample, whereas the other chamber contains the micro-phone. The smaller the unoccupied portion of these two chambers, the greater the signal becomes. The cell should also be nonresonant in the region of interest. A nonresonant cell is constructed such that no optical or acoustic resonances will influence the performance of the detector. A nonresonant cell has a number of advantages over a resonant one. The mathematics relating the phase shift between the internal pressure varia-tion and the modulated source incident on the sample are greatly simpli-fied if a nonresonant detector is used. In addition, the cell does not have to be tuned every time the modulation rate is changed.

3. Lengths

Another variable that markedly affects the PA signal is the distance y between the sample and the optical window. If y is less than the thermal diffusion length μδ of the gas, some of the heat emanating from the sample will be conducted to the window and will be lost. Because μδ varies with the modulation rate, the separation y must be larger than /xg, even at low modulation rates. The internal volume of the cell, however, must be kept to a minimum, so that reduced microphone response at high modulation rates does not prevent detection of the PA signal. Therefore, a balance must be struck between the minimum internal volume of the cell and the separation distance y. In general, helium should be used as the fill gas because of its superior thermal coupling properties (McClelland, 1983). Because different gases have different thermal diffusion lengths, how-


ever, it is entirely possible that one gas may be more suitable for use in a particular cell than another. Kinney and Staley (1983) discussed some of the properties of suitable, available gases. In a cell of their construction, they tested the cell response for carbon at 550 nm with both nitrogen and hydrogen. They found that the nitrogen provided better low-frequency response owing to its shorter thermal diffusion length.

4. Microphone Types

Microphones are generally of two types (Rosengren, 1975): the air-condenser microphone and electret-condenser microphone. The air-con-denser microphone has a typical responsivity of 50 mV/Pa (Kinney and Staley, 1983) with a frequency response range of approximately 1 to 5000 Hz. The electret-condenser microphone has a similar responsivity at lower modulation rates, but its responsivity drops off faster at higher rates.

a. Front-Vented Microphone. During operation, some provision must be made to equalize the pressures on both sides of the microphone, or the efficiency of the detector will suffer. This can be accomplished in two ways. The first is through the use of a front-vented microphone. This type of diaphragm allows the pressure to equalize on both sides while still detecting rapid pressure changes. The sensitivity of this type of micro-phone is usually less than that of a back-vented microphone.

b. Back-Vented Microphone. If a back-vented microphone is em-ployed, two types of configurations can be used. In the first configuration, the front and back microphone areas are joined by a small connecting tube. This is similar to the front-vented design, but it allows the use of the more sensitive back-vented microphone. In the second common configu-ration, the microphone preamplifier coaxial cable is used to vent the rear microphone area. Unfortunately, noise is not excluded from the micro-phone area in this type of configuration. Thus, some type of baffling must be used to reduce noise.

5. Evacuation

If the PA cell is to be placed into an evacuable interferometer, some provision must be made for protecting the microphone. If a front-vented design is used, the microphone is relatively safe from damage. If a back-vented design with a microphone preamplifier coaxial cable for venting is used, however, the cable must be sealed from the exit of the cell to the exit from the spectrometer. Placing a PA cell in a vacuum environment can have several advantages. First, because there is no sound conduction medium for room noise to be conveyed to the cell, there should be a


reduction in the background noise. Second, when the PA cell is placed in an evacuated environment, it is no longer necessary to purge the instru-ment to remove atmospheric carbon dioxide and water. Care must be taken to ensure that electrical cables passed through the sample bench are not so rigid that they conduct room and on-board vibrations to the PA cell.

6. Ease of Use

The design of a PA cell should also be functional. The sample holder should be easy to remove. The placement of the sample holder should be such that its position deviates as little as possible. The cell should be designed so that the operator can vary the composition of the gas inside the sample and microphone chambers. This can easily be accomplished by including inlet and outlet valves that can also evacuate the sample chamber. In this way the sample chamber can be evacuated and then flushed with the desired gas. The optical window should be constructed so that it can be removed with little effort. This allows the window to be replaced or polished when it becomes dirty, a very important capability, because any material deposited on the optical window will generate a PA signal. A removable window also allows the operator to change spectral ranges by simply changing the window material: quartz for the visible, KBr or ZnSe for the mid-ir, and polyethylene for the far-ir region.

B. Nonambient Conditions

1. Pressure

We have yet to consider the effects of the pressure and temperature of the gas inside the cell. An increase in pressure of the fill gas should enhance the sensitivity of the PA cell, whereas a decrease in pressure should decrease its sensitivity. The exact nature of the variation of the PA signal intensity with pressure has not been reported, so the linear dynamic range and responsivity enhancement are not known. For quantitative work to be performed at a reduced or increased pressure, the exact pres-sure has to be monitored and maintained.

2. Temperature

The initial studies of low-temperature PAS were performed by re-searchers working in the visible region of the spectrum. Shaw and Howell (1982) demonstrated that low-temperature PA spectra of solids are both sharper and simpler than room-temperature spectra. Pelzl and co-workers (1982) concluded that the PA signal of solids was enhanced by three


orders of magnitude when the cell was cooled from room temperature to liquid-helium temperature. The increase in signal at low temperatures is due primarily to the reduction of the Brownian motion of the fill gas. The sharper spectra are a result of the Boltzmann distribution favoring the lower states at reduced temperatures.

Low-temperature studies using FT-IR-PAS have not progressed rap-idly. This might be due in part to the high cost of low-temperature equip-ment added on to the already moderately to high cost of FT-IR spectro-photometers. One group (Kinney and Staley, 1983), however, has described a cell arrangement that allows temperature control from -60° to + 100°C. The temperature is controlled by passing a warmed or cooled gas through tubes in the bottom of the cell, the materials of which limit the temperature range. It is clear that more work must be done in the area of temperature and pressure effects in FT-IR-PAS.

C. Photoacoustic Spectroscopy Cells

The applicability of FT-IR-PAS to condensed-phase systems has led to several cell designs, both commercial and homemade. Although the cells differ widely in construction, they are all conceptually the same. Each cell has a mirror at a 45° angle to the sample to translate the modulated source radiation from a horizontal to a vertical direction. Each cell con-tains an optical window that can be changed to study different regions of the spectrum. One of the more important features of all of these designs is that the preamplifier is located as close as possible to the microphone.

One of the first cells used in condensed-phase sampling by PAS was developed by Rosencwaig (1973). This cell is shown in Fig. 8. The cell operated in a nonresonant mode and was used by Rosencwaig to show that PAS in the uv-visible region is a viable method for the study of condensed-phase samples. It was used by Vidrine (1980) to demonstrate that FT-IR-PAS is a feasible method for examining solid samples.

The cell depicted in Fig. 9 was developed from the Rosencwaig design at the Gilford Laboratories using the Helmholtz oscillator principle and modified by Nicolet for use with FT-IR. Improvements were made in the microphone mounting, the window mounting, and the sample loading method. The preamplifier utilizes an FET on board the microphone to reduce electronic interference and impedance. These modifications have led to significant improvements in sensitivity enhancement and noise re-duction.

The cell depicted in Fig. 10 is commercially available from EG&G Princeton Applied Research. The cell is mounted on a shock-absorbing platform that helps reduce the background noise due to onboard instru-

Fig. 8. Photograph of Rosencwaig's original photoacoustic cell. Courtesy of Jeffrey Christenson, Nicolet Analytical Instruments.

Fig. 9. Photograph of the Gilford-Nicolet photoacoustic cell. Courtesy of Jeffrey Christenson, Nicolet Analytical Instruments.


Fig. 10. Photograph of the EG&G Princeton Applied Research Photoacoustic cell. Courtesy of Martin Norton, EG&G Princeton Applied Research.

ment vibrations. In addition, the cell is equipped with purge valves so that the sample chamber and microphone area may be filled with any gas that is desirable. This is a valuable feature when performing analysis on air-sensitive samples.

One of the drawbacks of the first cells used in FT-IR-PAS was that they were not capable of being placed into an evacuable environment. The cell shown in Fig. 11 was developed by Eyring's group (McKenna et al., 1984) and can be operated under vacuum conditions.

One of the more recent designs (Fig. 12) is commercially available from IBM Instruments, Inc. The main features of this cell are its small sample

Fig. 1 1 . Photograph of an evacuable photoacoustic cell. Courtesy of Edward Eyring, Department of Chemistry, University of Utah.


Fig. 12. Photograph of an evacuable photoacoustic cell. Courtesy of John Casper, IBM Instruments, Inc.

volume, purge capabilities, and capacity to operate under vacuum. The cell can be removed from its mount and replaced with less than 1% error (Gerson et al.y 1984). Another feature of this cell that is not available with other designs is that the sample and microphone chambers can be evacu-ated before flushing with the appropriate fill gas.

V. APPLICATIONS

Photoacoustic and DR spectroscopies have been well received by ir spectroscopists. Both techniques have benefited from the advantages of FT spectrometers. Because both are usually applied to the same types of difficult samples, it is only natural that their spectral acquisition charac-teristics are frequently compared. For samples with a diffuse surface, particularly powdered samples, DR provides spectra with better SIN than FT-IR-PAS. For samples with a smooth surface, however, FT-IR-PAS may be the only method that will yield suitable spectra. Indeed, it has been stated that "although DR or PA FT-IR spectrometry will usually permit spectra of many solid samples to be measured, each technique has its strengths and weaknesses [Yeboah et al., 1981]."


z z

It z <

1900 2100 2300 2500

2000 2100 2200 2300 2400

WAVENUMBER

Fig. 13 . (A) Infrared transmission spectrum of AgCN in a KBr matrix; (B) photoacous-tic spectrum of AgCN powder. From Royce et al. (1980), with permission. Copyright 1980 IEEE.

Ever since Rockley (1979, 1980) and Mead et al. (1979) demonstrated the feasibility of using FT-IR-PAS for condensed-phase samples, use of the technique has greatly accelerated. Vidrine (1980) demonstrated that the band ratios for a nitrile-containing resin subjected to four types of sample preparation were independent of sample morphology. Figure 13 illustrates the capacity of FT-IR-PAS to yield ir spectra that are free of artifacts such as the Christiansen effect (Royce et al., 1980). It has been demonstrated (Krishnan, 1981) that PAS produces spectra that are very similar to DR spectra and that spectral subtraction of PA spectra is possi-ble (Fig. 14). Krishnan discussed the role of saturation effects in PAS. Teng and Royce (1982) described a procedure whereby an FT-IR-PA spectrum can be corrected for modulation rate effects that are inherent in FT-IR. With their correction, the PA spectrum resembles very closely a transmission spectrum. It is necessary, however, to collect the spectrum of the sample at two different mirror velocities. A simpler approach to the correction of variable modulation rates that requires collection of the PA spectrum at only one mirror velocity has been described (Graham, 1985). An example of this type of correction is shown in Fig. 15.

A. General

The applicability of PAS to routine problems has been demonstrated by a number of authors. Rockley et al. (1980) demonstrated the feasibility of using FT-IR-PAS to study biological samples. FT-IR-PAS has also been


4000 3500 3000 2500 2000 WAVENUMBER

1500 1000 500 400

Fig . 14 . Photoacoustic spectra of (a) acetylsalicylic acid-phenacetin mixture; (b) ace-tylsalicylic acid; (c) spectrum (a) minus spectrum (b); (d) phenacetin. From Krishnan (1981), wi th permission. Copyright Society for Applied Spectroscopy.

used to obtain ir spectra of cotton and nylon fibers without troublesome sample preparations that have for so long plagued conventional spectros-copy (Teramae and Tanaka, 1981). Lloyd et al. (1982) described a proce-dure for measuring the PA spectra of samples in situ on thin-layer chro-matography plates. By measuring the spectrum of the thin-layer Chromatographie plate with no sample present, they were able to subtract the absorption due to the plate itself. It was necessary to purge the sample for about 15 min with helium to remove all traces of organic solvent. One group utilized PAS in conjunction with Raman and EPR spectroscopy to determine the nature of the organic matter in Carlin-type gold ores (Nel-son et al., 1982). They determined that the organic content was of a carbonaceous nature and not in the form of humic acid, as commonly believed. The in situ study of a β-diketone was performed with FT-IR-PAS (Kendall et al., 1982). The authors described an experiment whereby the ir spectrum of the product of a reaction immobilized on silica was obtained. King and Davidson (1983) demonstrated the applicability of PAS to the study of fluorescent whitening agents on wool and wool-


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polyester blends. It was shown that subtraction methods used on the spectra of wool-polyester blends yielded an identifiable spectrum of the polyester.

B. Polymers

One of the greatest advantages that PAS holds over conventional ir spectroscopic methods is its capacity to obtain usable spectra of optically thick samples. As described in Section III.B, a sample may be optically thick without being acoustically thick. Therefore, as long as the sample is not thermally saturated, a usable ir spectrum of the solid sample can be obtained. Except for attenuated total reflectance (ATR), PAS may be the only way to examine optically thick polymeric materials without some type of sample preparation. Even ATR may be unsuitable if the sample cannot be made to conform to the reflection crystal. In addition, placing the polymer against a solid surface such as a reflection crystal in an ATR accessory may disturb the cure characteristics of the sample.


Spectra obtained by conventional methods of polymeric materials un-dergoing the curing process may not provide accurate information about the curing characteristics of the polymer surface. If transmission spec-troscopy is used, the sample is usually placed between two salt plates and allowed to cure while being examined. In this type of experiment, the presence of the salt plates may influence the polymerization reaction, and the information obtained derives mainly from the bulk of the sample. In ATR, one side of the polymeric material may be free from contact with a solid surface; unfortunately, the side examined is the side that is in con-tact with the reflection crystal. Therefore, because PAS sampling meth-ods do not entail any interaction with the polymeric material, PAS can be a superior method for obtaining surface spectra of polymers undergoing curing processes.

Characterization of a resol-type phenolic resin undergoing the cure process by heating was performed by FT-IR-PAS (Teramae et al., 1982). The authors observed the disappearance of the band due to the C—O—C stretching mode and the corresponding increase in a carbonyl species as a function of time. The same types of samples were run using a KBr trans-mission technique. In both experiments, the authors ratioed the intensity of the increasing carbonyl band to that of the unchanging band resulting from the C=C aromatic stretch. When they plotted this ratio as a function of time, they obtained an approximation of the degree of curing. The information from both experiments provided similar data for the curing curves. The extent of curing determined in the PAS experiment, however, leveled off more rapidly than did the extent of curing observed in the transmission experiment. This result is probably due to the different chemistry on the surface as opposed to the bulk of the material. The ir spectra of butadiene-acrylonitrile rubber with various amounts of car-bon filler were also presented. The spectra show that, as the amount of carbon filler increases, the bands from the rubber species become lost in the background. This phenomenon actually occurs because the carbon is a blackbody absorber and raises the signal level at all wavelengths. Be-cause the dynamic range of the A/D converter is limited, the relatively small signal of the rubber compared with the carbon is eventually lost in the noise.

The curing process of an acrylate base mixture was described by Royce et al. (1981) and is shown for various stages of the curing process in Fig. 16. They compared the intensity ratios of the 1509 cm"1 peak of bisphenol A, which was virtually unchanged with curing, and the 1407 cm-1 peak of the CH bending vibration of the CH2 chromophore. Because the carbon atom in the CH2 chromophore is a doubly bonded species that reacts during the curing process, it is a good standard for the measure of curing.

J. A. Graham, W. M. Grim III, and W. G. Fateley

16B0 1640 1600 1560 1520 14Θ0 1440 1400 1360

WAVFNUMBFR

Fig. 16. Photoacoustic spectra of 2% dimethylphenylacrylate during uv cure: (a) 0 sec, (b) 10 sec, (c) 20 sec, (d) 30 sec. From Royce et al. (1981), with permission. Copyright 1981 IEEE.

By examining the extent of curing for a sample as a function of time for two different gaseous environments, Royce et al. were able to observe the inhibition of the reaction by oxygen.

One of the important uses of FT-IR has been the study of the amor-phous and crystalline phases in polymer samples. Balizer and Talaat (1983) used FT-IR-PAS for the study of the crystallization kinetics of polyethylene terephthalate (PET) and blends of PET and poly(butylene terephthalate) (PBT). In Fig. 17 the spectra of PET at various stages of the crystallization process are shown. The band at 632 cm-1 was identified as the C—C—C bending mode of the benzene ring in the backbone of the PET and should not change appreciably with crystallization (Escala, 1978). Boerio and Koenig (1971) assigned the band at 848 cm-1 to the rocking mode of the trans conformation of the ethylene glycol segments. An indication of the change in crystallinity is obtained from the relative intensities of the two bands at 848 and 632 cm-1. In Fig. 18, plots of this ratio as a function of annealing time are shown for PET at 150°C and for an 80/20 blend of PET/PBT at 115°C. The authors reported that the PAS technique was not as sensitive as transmission spectroscopy. However, the estimation of the point at which the material was half-cured, the halftime, varied only a few percent between the two methods. In addition, the PAS experiment was used to measure samples of the order of 250 μιη thick, whereas the transmission experiment was performed with samples — 10 μτη thick. The crystallization kinetics for these two thicknesses may


AMORPHOUS 60 sec 3 0 0 sec

1000 500 5 0 0 1000 500 1000

WAVENUMBER Fig. 17 . Photoacoustic spectra of polyethylene terephthalate (PET) showing the in-

crease in intensity of the 848 cm ' band as a function of time. (1983), with permission. Copyright Les Editions de Physique.

From Balizer and Talaat

be different. Photoacoustic spectroscopy is especially suitable for the study of the crystallization kinetics of samples of different thicknesses.

FT-IR-PAS was also used to obtain the ir spectrum of an air-sensitive sample of «-doped polyacetylene (Riseman et aL, 1981). In this paper, the ir spectra of an undoped and a doped sample were presented. Because the PA cell can operate in an inert environment, it is ideally suited for these

-Q

<

, ^

y^ 10 100 1000

TIME (sec)

Fig. 18 . Crystallization kinetics determined from FT-IR-PAS data, x , H5°C; · , 150°C. From Balizer and Talaat (1983), with permission. Copyright Les Editions de Phy-sique.


types of samples. The authors were able to assign some of the bands, but interference due to water vapor and lack of a deuterated sample pre-vented them from completing the assignments. It is clear that further studies in this area are needed.

For hot-rolled polymer samples, the dichroic ratio of the surface may be different from that of the bulk. One paper described the procedure for obtaining the dichroic ratio of a sample of PET (Krishnan et al., 1982). The study showed that PAS may be capable of intermediate surface spec-troscopy. The authors reported dichroic ratios of greater than unity for both PAS and ATR measurements, and the dichroic ratio for the ATR experiment was higher than that for the PAS experiment. It is possible that there is a higher degree of orientation for the surface layers, because the depth penetration in ATR may be less than that in conventional FT-IR-PAS (Krishnan, 1981). Because both PAS and ATR are capable of performing depth profiling of the surface, it should be interesting to com-pare dichroic ratio studies from these types of experiments.

C. Coal

Routine acquisition of ir spectra of coal surfaces has been very elusive. Conventional solid sampling methods require some form of sample prepa-ration during which the surface molecules may be chemically altered or diluted with the bulk material. The relatively recent application of FT-IR-PAS to the study of the surface species of coal has for the first time made possible the careful examination of the surface of an unaltered chunk of coal. The qualitative nature of PA spectra is virtually independent of sample morphology (Vidrine, 1980). Therefore, the irregular surface of a chunk of coal will not seriously affect the PA spectrum. Vidrine (1980) also showed that the PA spectra of coal provide better spectral features at lower wavenumbers than do DR spectra of coal. Photoacoustic spectra also appear to have flatter baselines.

The ir spectra of aged and freshly cleaved coal samples were reported by Rockley and Devlin (1980). Three coal samples weighing —100 mg each were exposed to the atmosphere for 1 year or longer. Their ir spectra were recorded. Each sample was then cleaved, and the spectra of the fresh surfaces were recorded. The largest changes occurred in samples that contained mineral-rich components. One reason for this may be that, as the surface ages, the volatile components evaporate whereas the min-erals remain on the surface. Therefore, it is anticipated that the aging of coal materials can be correlated with the mineral content.

A study of the effect of the organic make-up of coal on its volatility was


described by Gerson et al. (1984). Using FT-IR-PAS, they measured the relative amounts of aliphatic and aromatic content. The percentage of volatility was measured for each sample by proximate analysis. Some of the spectra were adjusted to correct for a broad interference due to ad-sorbed water. The ratio of aromatic to aliphatic character was then plot-ted versus the percentage of volatility. The method produced a curve that demonstrated precision within a few percent.

D. Adsorbed Species

Until relatively recently, transmission spectroscopy using a thin, pressed disk composed of the adsorbent and supporting material was the conventional method for acquiring ir spectra of adsorbed species. Reflec-tion spectroscopy helped to provide some of the missing data, but neither method was completely satisfactory, and both preparations involve po-tential alteration of the sample. Transmission spectroscopy suffers from strong absorptions and scattering effects, and certain spectral regions are inaccessible owing to absorptions from the support materials. Neither method yields strong signals (Grim, 1981). Photoacoustic spectroscopy, which is an especially surface-sensitive technique, should facilitate mea-surements in this area.

Van Every et al. (1981) investigated the ir spectrum of pyridine ad-sorbed onto silca gel using both DR spectroscopy and PAS. In general, the DR spectrum exhibited bands that were narrower than those found in PAS. The PA spectrum had interferences from adsorbed water and CC14 that had desorbed into the purge gas. Because gas-phase molecules have more efficient coupling than condensed-phase molecules, the PA signal is stronger for gas-phase molecules, and the effect of desorbed gases can be quite devastating.

Investigations of surface species on silica powder by FT-IR-PAS proved successful (Kinney and Staley, 1983). Spectra of palmitic acid deposited on silica powder compared very favorably with spectra of the pure material when the spectrum of the silica support was digitally sub-tracted. The adsorbed palmitic acid spectrum had approximately the same SIN as the reference material, even though the surface coverage of the adsorbed material was only 1 x 10~10 mol/cm2, owing to the better ther-mal coupling provided by the increased surface area of the deposited material. The spectra of CO and (CN)2 gases adsorbed on platinum and silver on an alumina support were also reported. Good spectra were ac-quired, even though the catalyst materials were pellets. The effects of the thermal and physical properties of catalyst supports were also discussed.


It was concluded that quantitative work may be difficult owing to the distortions of the spectral features caused by light scattering from the support.

A quantitative study of the active sites occupied by CO in an Ni/Si02 catalyst was performed by FT-IR-PAS (Gardella et al, 1983). The re-searchers were able to circumvent the light-scattering problem by using a band from the silica support as an internal standard. This work is dis-cussed in more detail in Section V.E.

A study of bis(cyclopentadiene)chromium (chromocene) anchored on silica provided information about olefin polymerization (McKenna et al., 1984). The authors reacted chromocene with silica, and the material was then treated with ethylene at 150 psi with no heating for —30 min. The resulting ir spectrum shows that the cyclopentadiene moiety remains at-tached to the catalyst in the presence of this olefin catalyst intermediate. The effect of 2,3-dimethylbutene on the chromocene-silica catalyst was also studied. The ir spectrum suggested an ethylene complex as the first step in the polymerization process. The study exemplifies the role that FT-IR-PAS might well play in catalyst studies in the future.

E. Quantitative Analysis

Owing to the relatively recent development of FT-IR-PAS, little at-tempt has been made to perform quantitative analysis on condensed-phase samples. The diminished SIN compared with that of other methods has made quantitative studies difficult. Continued improvements in cell design and a better understanding of the PA effect, however, have led to some initial quantitative studies.

When first introduced, PAS was believed to be independent of sample surface morphology (Vidrine, 1980). It has since been learned that sample morphology directly affects the PA signal (Heiander et al., 1980; Rockley et al., 1984), although for qualitative studies, the assumption that the PA signal is independent of sample surface morphology is still generally valid. The spectra (Rockley et al., 1984) for fine, medium, and coarse particles of /7-nitrophenol are shown in Fig. 19. Exclusive of saturation effects, the relative intensities of bands in a spectrum are virtually unaffected by the sample form. As the particle size decreases, however, the PA signal in-creases. For quantitative experiments, the sample morphology plays a large role in the results of the experiment.

The PA signal increases as the particle size of a sample is decreased for two reasons. First is the increase in the amount of heat generated near the sample surface. This effect is caused by increased scattering of the light waves near the surface of the sample. The increase in light absorbed


2000 1500 _ i 1000 600 cm

Fig. 19. Photoacoustic spectra of p-nitrophenol. (A) Fine-grained; (B) medium-grained; (C) coarse-grained. From Rockley et al. (1984), with permission. Copyright Society for Applied Spectroscopy.

translates into a greater heat density at the sample surface. It has been suggested that "this increase in average surface temperature may be one of the contributing factors causing the known increase in photothermal signal for powders and other surface-textured specimens in contrast with the specimens having smooth untextured surfaces [Aamodt and Murphy, 1982]." The second factor is the increased coupling efficiency of the sample-gas interface. Because the sample has a larger surface area, the exchange of heat from the sample to the fill gas is more efficient. Therefore, the particle size and the packing of the sample in the PA cell will have to be stringently controlled, or any attempt to quantitate the sample will be fruitless. Avoiding saturation effects by using a nonsatu-rated peak or increasing the mirror velocity to decrease the signal from a saturated peak will also be necessary.

One method of correcting for scattering effects has been to employ a correction similar to that developed by Kubelka and Munk for diffuse reflectance (Burggraf and Leyden, 1981). This requires that phase infor-mation be obtained and used in the correction equations. The collection of phase information from FT-IR-PAS has yet to be thoroughly studied. Until a complete study is made, it may not be possible to correct for scattering effects by this method.

As long as saturation effects are avoided, the effects of scattering may be compensated for by the use of an internal standard. Rockley et al. (1981) showed that the percentage of K15NC>3 in an isotopic mixture can be determined by FT-IR-PAS. The intensities of the bands due to both the K1 4N03 and K1 5N03 were recorded. The ratios of these intensities


were plotted against the percentage of 15N. The plot produced a straight line that intercepted the ordinate at a relative intensify of 0.24. One possi-ble reason for the apparently large amount of 14N in ,5N is the incomplete resolution of the two peaks in the spectrum.

Another group measured the concentration of vinylsilane that reacted with dried silica (Kinney and Staley, 1983). Five mixtures of vinylsilane and methylsilane were prepared and reacted with the silica. The mixtures ranged from 100% vinylsilane to 100% methylsilane. The intensity of the band due to the vinyl CH stretching frequency was measured and normal-ized against the intensity of the band due to the Si—O stretching mode. When the normalized intensity was plotted against the vinyl concentra-tion, a correlation coefficient of 0.9993 was obtained for the five points. Cobalt tetracarbonyl was reacted with alumina in a similar manner. The relative absorbance of the band due to the C—O stretching mode was measured for three different samples by transmission spectroscopy. These values were then plotted against the PA signals for the same sam-ples, and the graph was linear. It was demonstrated that quantitative analysis is possible if appropriate corrections for scattering and support characteristics are made.

A quantitative study of the catalytic surface adsorption sites on an Ni/ Si02 catalyst was performed by Gardella et al. (1983). Because the amount of CO adsorbed by this catalyst is small, an indirect method of measuring the CO adsorption had to be devised. It is known that gas-phase samples produce a PA signal that is at least an order of magnitude larger than condensed-phase samples, and they took advantage of this effect. A sample of dehydroxylated Si02 was placed into the PA cell, and the ir spectrum recorded. Successive additions of CO gas were introduced into the cell, and the ir spectrum was recorded for each addition of gas. The dehydroxylated Si02 showed no signs of CO adsorption, as expected. Figure 20 shows the spectrum of dehydroxylated Si02 before and after injection of 350 μΐ of CO gas (Gardella et al, 1983). The intensity of the P branch of the CO gas was ratioed against the intensity of the Si02 absorp-tion at 800 cm-1. This ratio was then plotted against the volume of CO injected into the cell. A linear curve was detected up to —300 μ\ (Fig. 21; Gardella et al., 1983), at which point the PA signal became saturated. Next, the Ni/Si02 catalyst was placed in the PA cell, and 1 ml of CO was introduced. Measurement of the P branch of the CO gas indicated that 260 μί was present in the gas phase. This implied that 740 μΐ of CO had been adsorbed. Performing the appropriate calculations yielded a figure of 40% active sites. This study shows the powerful capabilities of FT-IR-PAS for quantitatively measuring gaseous and adsorbed surface species. An ex-


2 8 0 0 2 4 0 0 2 0 0 0 1600 1200 WAVENUMBER

Fig. 20 . Photoacoustic spectra used to construct calibration curve. (A) Dehydroxyl-ated S i0 2 ; (B) dehydroxylated Si02 with 350 μ\ of CO gas injected into cell. From Gardella et al. (1983), with permission. Copyright Society for Applied Spectroscopy.

periment of this type could be utilized to detect the actual coverage of an adsorbed species on a catalyst.

It may not always be possible to normalize the PA signal against an internal standard. In cases of this type it would be beneficial if a method could be developed whereby the effects of scattering were minimized. It

Fig. 2 1 . Calibration curve used in the calculation of the amount of residual CO gas [CO(g)] on a catalyst surface. Maximum photoacoustic intensity for the P branch of CO(g) absorption (ACo) ra-tioed to maximum photoacoustic inten-sity for the Si02 absorption between 866 and 767 cm"', plotted as a function of the volume of CO(g) injected at 0.98 atm and 27°C. Error in each point is ±9% relative standard deviation. From Gardella et al. (1983), with permission. Copyright Soci-ety for Applied Spectroscopy.

I00 200 300 400 500

Vco (MD


was shown that careful control of the packing of the sample into the sample cell can lead to improved results in DRIFTS (Yeboah et al., 1984).

A similar study was performed for FT-IR-PAS (Graham, 1985), except that the effects of pressure on the packing were not studied. Vari-ous mixtures of CaC03 in KBr were prepared, ranging in concentration from 0.5 to 100% in CaC03. The amount of sample weighed into the sample holder was kept constant for each measurement. After the sample was weighed into the sample holder, a flat piece of stainless steel was used to smooth out the surface of the sample. The intensity of the band at 878 c m 1 was measured and plotted against the percentage of CaC03 in the mixture. The plot can be seen in Fig. 22. The curve is reasonably linear in the region from 5 to 50%. Nonlinearity is exhibited both above and below this region. It was also discovered that if the amount of sample weighed into the sample holder was decreased, the linearity was translated to the region of higher concentration.

The nonlinearity in the higher concentration range is due to the de-crease in scattering. Because CaC03 is not as dense as KBr, the volume of the sample increases as the concentration of CaC03 increases. Because the volume is larger, the sample must be packed more tightly into the sample cell, resulting in a smoother sample surface that scatters less. For instance, the PA signal for 100% CaC03 is less than that for 65% CaC03. When the sample size was decreased, the region of linearity shifted to higher concentrations, because the packing was not as dense. The nonlin-

0.20

0.15

Έ* 0.10

II

< 0.05

Ϊ I 1 J I I I I I I I

0 10 20 30 40 50 60 70 80 90 100 CaC03 (%)

Fig. 22 . Graph demonstrating the relationship between concentration of CaC03 in KBr and the intensity of the photoacoustic signal at 878 cm"1. The intensity of the photoacoustic signal reaches a maximum at —65% and then decreases at higher concentrations, at which point increased scattering begins to predominate.


earity in the lower concentration range is partially attributed to scattering effects. Because KBr is much denser, at low concentrations of CaC03 there was not enough sample to fill the sample cell, which prevented uniform packing.

It has been demonstrated that, if saturation effects are avoided and some form of correction is made for scattering effects, quantitative analy-sis of condensed-phase samples is possible with FT-IR-PAS. It is also clear that more information is needed to improve the understanding of the PA effect with light-scattering samples.

F. Depth Profiling

In Section III.B the parameters affecting the PA signal were discussed. It was noted that the PA signal for thermally thick samples consists only of information from the first thermal diffusion layer (μδ). Because the value for JHS depends on ω, it is possible to vary the depth from which heat generation will propagate to the surface before being effectively damp-ened out. Therefore, by obtaining spectra at low, intermediate, and high modulation rates, it should be possible to depth-profile thermally thick samples.

1. Thermal Diffusion Method

Depth profiling by PAS was first demonstrated in the uv-visible region of the spectrum (Rosencwaig, 1980; Kirkbright, 1978). Krishnan et al. (1982) discussed the possibility of depth profiling using FT-IR-PAS and pointed out that care must be taken not to mistake saturation effects for changes in chemical composition with different layers in the sample. Us-ing FT-IR-PAS, Vidrine (1981) performed a depth-profiling study of a catalyst system, and his results are shown in Fig. 23. Changes are found in the spectrum as the modulation rate is increased, especially in the region around 700 cm 1 .

The application of depth-profiling studies to other systems has met with little success (Mehicic et al., 1981). One reason for this is that only ther-mally thick samples can be effectively examined in a depth-profiling study. Structural layers can also be too thick to allow depth profiling. If the thickness of the top layer is equal to or greater than μ,Η at the lowest obtainable modulation rate, depth profiling will not be possible for that sample. Each system under investigation will have different thermal prop-erties, as will each layer in the system. This makes it difficult to find the ideal system to study, because the thermal properties and thickness of each layer would have to be controlled.


2000 1800 1600 1·*00 1200 1000 WRVENUMBER

800 600 400

Fig. 23. Effect of modulation rate on photoacoustic signal. From Vidrine (1981), with permission. Copyright Society of Photo-Optical Instrumentation Engineers.

2. Phase Lag Method

As described in Section III.C, FT-IR-PAS differs from dispersive PAS in that all frequencies are detected at the same time, preventing the use of a lock-in amplifier. In conventional FT-IR, this presents no problem, because all frequencies arrive at the detector at the same time. In PAS, however, the heat liberated by the sample must first be transported through the sample. This allows the surface-generated or fast heat to be detected before the bulk-generated or slow heat. Figure 24 shows a dia-gram depicting the time delay created by the increased detection time for bulk-generated heat. Because a lock-in amplifier is not used, a phase error in the FT spectrum will occur whenever the phase difference between the fast and slow heats becomes too large for the FT phase correction routine to handle. The phase error produces a nonsymmetric peak. If the phase error reaches 180°, the peak will be completely inverted. Some instruments use only a small portion of the interferogram located around the center burst for phase correction. A spectrum computed from this lower-resolu-tion phase spectrum suffers from larger phase errors than one computed from a higher-resolution phase spectrum obtained from a double-sided interferogram. The use of a power spectrum will help to reduce the larger phase error but will not be as effective as a double-sided interferogram.

In our laboratory, it has been found that most layered materials exhibit


REGION F

ABSORPTION

REGION S

SURFACE-GENERATED (FAST) HEAT

T ,

BULK-GENERATED (SLOW) HEAT

T 2

Fig. 24 . Diagram illustrating phase delay in an ideal laminar film. Signals from region F are fast or surface-generated heat, whereas signals generated from region S are slow or bulk-generated heat. Tx> T2.

a fairly large phase error before effective depth profiling can be performed (Graham and Fateley, 1985). Results of this type for a five-layer laminar polymer film are shown in Fig. 25. It can be seen that, as the modulation rate is increased, the general features across most of the spectrum remain unchanged. This, however, is not the case for the carbonyl band at 1750 cm 1 . As the modulation rate is increased the peak becomes nonsym-metric. When the modulation rate is increased to the point where the phase error reaches 180°, the peak completely inverts.

When there is a large phase error, it becomes impossible to obtain any meaningful information from the various spectra, because subtraction of the spectra results in noisy peaks. If the phase spectrum instead of the amplitude spectrum is used, it still may be possible to perform a depth-profiling experiment. The use of phase information has already been dem-onstrated by researchers working in the uv-visible region. Several papers describing the use of phase information to determine the relaxation rates in fluorescent compounds are in print (Kaya et al., 1974, 1975; Keller et


Fig. 25. Illustration of the use of phase error to perform depth-profiling studies. Spectra of a five-layer laminar polymer film at the following velocities (centimeters per second): A, 0.059; B, 0.070; C, 0.083; D, 0.099; E, 0.118; F, 0.140; G, 0.166. Spectra recorded on an IBM 98 spectrometer.

al., 1983). Adams and Kirkbright (1977) used phase information to deter-mine the absolute value of a sample's thermal diffusivity. Still another group (Teng and Royce, 1980) used phase information coupled with am-plitude information to determine the absolute value of a sample's optical absorption coefficient.

As described in Section III.C, it is possible to obtain phase information from an FT-IR-PAS experiment. This information could help to elucidate the structural make-up of a particular sample in a depth-profiling study. Because the heat generated from within the sample must travel a greater distance than heat generated near the surface, a time lag or phase error will exist between the two liberated heat energies (see Fig. 24). Thus, at high mirror velocities and consequent high modulation rates, the phase error for a signal emanating from within the sample will increase com-pared with a signal generated near the surface. By measuring the phase error of a sample at a sufficiently low mirror velocity and consequent low modulation rate, one can measure the phase error of the instrument. Subsequent measurement of the phase error for the same sample at much higher modulation rate will yield the combined phase error for the instru-ment and the PA signal from the sample. Subtraction of the phase spec-trum acquired at a sufficiently low modulation rate from the spectrum acquired at a high modulation rate eliminates the instrumental phase er-ror. By obtaining the phase error for a sample as a function of wavenum-ber and modulation rate, it should be possible to depth-profile a sample.


o z a Hi

a -180

1850 i8bo 17^0 WflVENUMBER

17bo 16^0

Fig. 26. Phase-error spectra as a function of modulation rate recorded at the follow-ing velocities (centimeters per second): A, 0.059; B, 0.070; C, 0.083; D, 0.099; E, 0.118; F, 0.140; G, 0.166. Spectra recorded on an IBM 98 spectrometer.

The phase-error spectrum as a function of modulation rate for a five-layer laminar polymer is shown in Fig. 26.

G. FT-PAS beyond the Mid-infrared

Although the main purpose of this chapter is the discussion of FT-IR-PAS in the mid-ir region, some attention should be paid to the use of FT-PAS in adjacent regions of the spectrum. The FT interferometer was first utilized in the far ir, whereas the PA spectrometer was first developed for use in the visible. The principal confluence of these two techniques has been in the mid-ir. Some applications in these other regions have been found despite the difficulties involved. Operation of an FT-IR-PA spec-trometer in the far-ir region is hindered by low energy per photon and large background noise due to the low-frequency vibrations. Operation in the near-ir and visible regions is affected by the critical alignment neces-sary for the shorter wavelengths and the very high modulation rates. Because these rates are too high for most PA cells, the resulting signal is generally too small to measure. Some of the present applications in these two areas are discussed here.

1. Far-Infrared FT-PAS

Vidrine (1980) briefly discussed the use of FT-PAS in the far ir. He displayed a spectrum of carbon black obtained by FT-PAS in the far ir. The S/N appears to be acceptable over a limited range that extends from 500 to 100 cm-1. A solid sample, however, would have an acoustic signal


of around one order of magnitude smaller than that for carbon black. If PAS is to be successful in this region, improvements in cell sensitivity and reduction of background noise will be necessary.

One way to improve sensitivity is to decrease the cell volume as much as possible. The limiting factors are the beam size and the required dis-tance from the sample to the optical window, as discussed in Section IV.A. Background noise can be reduced in several ways. Room noise can be reduced by the use of an appropriately designed microphone venting system, as discussed in Section IV on cell design. Operation of the cell in a vacuum environment should also reduce the background noise. Care must be exercised to isolate the cell and electronic cables properly. The cell should be placed on a vibrational isolation mount, and the elec-tronic cables should not be so rigid that they conduct on-board vibrations to the cell. Operation in a vacuum system also helps to reduce interfer-ence from troublesome water vapor bands.

In Fig. 27, the far-ir carbon black spectrum is shown (Graham and Fateley, 1985). The spectrum was obtained using a 3.5-/zm Mylar beam splitter and a mirror velocity of 0.096 cm/sec. The coaddition of 500 scans at 8 c m 1 resolution was performed before the data were Fourier trans-formed. The spectra in Fig. 28 are the single-beam and ratioed spectra of benzoic acid (Graham, 1985). Two thousand scans were collected to acquire this spectrum. To our knowledge this is the first publication of a far-ir spectrum of a condensed-phase sample collected by FT-IR-PAS. For comparison, Fig. 29 depicts the DRIFT spectrum of the same com-pound.

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Fig. 27. Far-ir single-beam spectrum of carbon black.


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Fig. 28 . Spectra of benzoic acid. (A) Single-beam; (B) ratioed against carbon black.

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Fig. 29 . Kubelka-Munk plot of a DRIFT spectrum of benzoic acid ratioed against polyethylene.


2. FT-PAS in the Near-Infrared and Visible Regions

The first FT-PA spectra of condensed-phase samples were reported by Farrow et al. (1978). A Michelson interferometer phase-modulated the light before it was sent to the PA cell. Phase modulation allows the use of a lock-in amplifier, because all frequencies are modulated at the same rate. The modulation rate can be kept at a reasonable level compared with the rates encountered in conventional amplitude modulation. Thus, if the energy absorbed is converted to heat through nonradiative decay rather than emitted by fluorescence and saturation effects are avoided, the re-sulting PA spectrum should resemble the transmission spectrum of that sample. In this paper, the FT-visible-PA spectrum of a neodymium(III)-doped laser glass was compared with the spectrum of the same sample obtained by dispersive PAS. The authors found that the broad features compared very well. Although the FT-PAS spectrum had a slightly lower SIN, it had been collected in only 4 min compared with 90 min for the dispersive PA spectrum. Furthermore, the source was only a 100-W tung-sten-iodide lamp, compared with the 450-W xenon arc lamp used for the dispersive setup.

A comparison between FT-visible-PAS and dispersive PAS has been performed (Lloyd et al., 1980). The visible spectra of Ho203, Nd 203, and hemoglobin were collected by both dispersive PAS and FT-PAS methods, with the exception of hemoglobin, which was examined with a uv-visible spectrometer instead of by dispersive PAS. In all cases, the band shapes were very similar for all three samples. The clear advantage of FT-visi-ble-PAS over dispersive PAS is that the data collection times are much shorter. The disadvantages are certainly the higher cost of instrumenta-tion and the alignment required by the interferometer, which is even more critical for the near-ir and visible regions than it is for the mid-ir region.

An FT-PA spectrometer operating in the visible and near-ir regions has been described (Debarre et al., 1981). Increased SIN and improved spec-tral coverage over previous FT instrumentation are the main points of interest. Spectra of several rare-earth oxides were compared with disper-sive spectra of the same samples. Although the dispersive spectra were generally of higher SIN, the FT-PAS spectra covered a wider spectral range. Because a lock-in amplifier was used, both in-quadrature and in-phase spectra could be obtained. The importance of these types of mea-surements is discussed in Section V.F. The authors concluded that their FT-PA spectrometer operating in the visible and near-ir regions provided better SIN and greater resolution than spectra produced by other FT-PA spectrometers but cautioned that the high throughput of the interferome-ter may cause some samples to decompose.


VI. CONCLUSIONS

Until PAS was coupled with FT-IR, the collection of mid-ir PA spectra of condensed-phase samples was not possible with a broad-band source. It has since been shown that dispersive PAS with a broad-band source is possible but not as effective as FT-IR-PAS. The ease and speed with which condensed-phase samples can be examined by FT-IR-PAS have expanded the possibilities for use of the method as a routine laboratory diagnostic tool.

The FT-IR-PAS spectra of most condensed-phase samples resemble spectra obtained by transmission methods. Exceptions are those samples that produce signals that saturate the detector and those that are affected by varying modulation rates in the interferometer. Saturation effects are not as great a problem for ir instrumentation as they are for uv-visible instrumentation. These effects can be overcome by increasing the veloc-ity of the moving mirror and compensating for the varying modulation rates by software correction.

One of the main advantages of PAS has been its capacity to obtain usable ir spectra of all sample morphologies. The inherent nature of the method requires virtually no sample preparation. This allows for collec-tion of spectra of samples that are not chemically or physically altered by sample preparation. In addition, spectral artifacts produced by light-scat-tering samples are not as prominent with PAS as with DRIFTS.

Because PAS signal generation is limited to the first thermal diffusion length of the solid sample, the method has often been referred to as a surface technique. In reality, however, PAS is a quasi-surface technique, with sampling depths ranging from a few micrometers to 1 ml.

Because all sample morphologies can be examined, in situ reactions may be studied without deleterious effects from sample holder materials. In addition, the method is ideally suited to the examination of air-sensitive samples. FT-IR-PAS is also ideally suited for the study of the surface chemistry of a variety of samples.

The use of FT-IR-PAS for quantitative studies has thus far been lim-ited. This can be accounted for by unexpected effects resulting from particle size and sample packing. In spite of this problem, some quantita-tive work has been performed by the use of an internal standard. It may also be possible to perform quantitative studies without an internal stan-dard if extreme care is taken to reproduce the particle size and the way in which the sample packed into the PA sample cell.

It is most likely that there will be an increase in the application of FT-IR-PAS to the examination of condensed-phase samples. Continued im-provements in PA cell design should increase the S/N and enhance the


overall sensitivity. This improvement will lead to lower detection limits and shorter data collection times.

No matter how condensed-phase sampling methods in ir spectroscopy continue to develop, FT-IR-PAS is certain to become an invaluable diag-nostic tool. Any researchers interested in acquiring spectra of condensed-phase samples that do not readily submit to conventional sample handling techniques without sample preparation procedures should consider in-vesting in FT-IR instrumentation with a PAS accessory.

The field of PAS is certainly not without uncertainties and frustrations. Nevertheless, it can be comforting to approach PAS today with Alexan-der Graham Bell's (1881) attitude of yesteryear: "It is often more interest-ing to observe the totterings of a child than to watch the firm tread of a fullgrown man; and I feel our first footsteps in this new field of science may have more of interest to you than the fuller results of mature re-search."

ACKNOWLEDGMENT

The authors express their appreciation for the support for this work provided by NSF Grant CHE-8109570.

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INDEX

A

Absorbance and correlation matrix, 31 Gram-Schmidt chromatogram, 109 high, of coal, 196 infrared, 140 and poor purging, 110, 111 ratio method, 3 and spectral subtraction, 159 spectrum, true, 6-7

Absorbed light in optically opaque sam-ples, 354-355

Absorbed species of zeolites, 268 Absorbing systems, 291 Absorption

adlayer, 320-321 bands

sign, 338 reflectivity, 321

coefficient, 194, 210, 216, 237, 348 optical, 352

dependence of measurements, 84 and diffuse reflectance, 254-255 factor, 320 frequencies and intensities, 3 gas-phase, 336 length, optical, 353 peak

aromatic, 220 sign, 337

scale, absolute, 199 and solvent, 91 spectra

aqueous sugars, 92 d-3-bromacamphor, 91 dimethyl tartate, 90 epoxypropane, 94 infrared, 92 tartaric acid, 92, 93

stray light, 360 surface, 264 water, 193, 288

A/D, see Analog-to-digital Adsorbate-adsorbant interactions, 264

Adsorbate band maxima, 322 Adsorbed species, photoacoustic spectros-

copy of, 375 Adsorption, gas-solid, 329 Acetate exchange, 210 Acetone

liquid, 320 thin film, 320, 321

Acetyl group, 200 Acetylation, 197, 198, 199, 200-202, 211 Acetylsalicylic acid, 369 Acetylated coal, 203 Acid, 301, 341, 342 Acid washing for COO" group identifica-

tion, 186 Acoustic signal detection, use of heat for,

350 Aery late base, 371 Air-condenser microphone, 362-363 Alcoholic groups, 157, 158 Alcoholic OH, 206 Aldrin, diffuse reflectance spectra of, 131 Aliphatic CH

groups in coal, 213-235 and hydrogen, 235 modes

bending, 184 rocking, 188-189, 224 stretching, 199

percent carbon in, 238 vibrations, rocking, 224, 329

Aliphatic chain of arachidate molecules, 327 of Langmuir-Blodgett monolayer, 301,

304 Aliphatic groups, 157, 158, 189

CH, 158, 189, 213-235 and esters, 157

Aliphatic hydrogen-to-carbon ratio, 238 Alkali halide matrix, 190, 195, 203, 258-

259, 264 Alkali halide pellets, 191-194 H-Alkanes

crystal modification, 302 odd, 307

393

394 Index

Alkyl esters, 203 OH, 201, 203, 206-207

Aluminum-coated substrate for grazing incidence reflection spectra, 309

Aluminum as film substrate, 325 Amidization reactions, 150-152 Amino groups, 159-162, 177, 201 Angle of incidence, 318-322, 337 Anion and inorganic species identification,

278 Anisole, 117, 121-122 Anisotropie reflection, 299-300 Anthracite, see Coal Apodization function, 4, 23, 73, 74 Analog-to-digital converters, 142, 335, 360,

371 Arachidic acid, 301 Arachlor

capillary spectra of, 137 flame ionization chromatogram of, 136

Aromatic CH groups, 189, 213 and hydrogen, 235 stretching modes, 195

Aromatic absorption peaks, 220 Aromatic bending mode, 189 Aromatic groups, 157, 158 Aromatic materials, 184 Aromatic rings, 182-183, 236-237 Aromatic stretching mode, 371 Ash, see Low-temperature ash Assemblies

Langmuir-Blodgett monolayer, 300-308 molecular orientation of, 301

ATR, see Attenuated total reflectance Attenuated total reflectance, 190, 370, 374

B

Band absorption, 338

factor, 326 assignments for coal, 178 and double bonds, 307 envelope, 14, 16 intensity,

coal, 191-192 normalized, 204

maxima, 322-323 overlapped, 12-13 in region, 182-196 shape analysis, 3, 13-17 splittings, 302 and tilt angle, 284 water vapor, 386

Barium exchange, 210 Baseline

correction of spectral data, 21 determination of vibrational circular

dichroism, 80-85 distortion of mid-infrared region,

264 of photoacoustic spectroscopy, 374 shift and plotting, 155

Beam component, polarized, 285 Beam splitters, 247, 248-251, 386 Beer's law, 2, 6, 21, 107 Beer-Lambert law, 192, 193, 194 Bending mode

of benzene ring, 372 CH, 184, 186

aromatic, 216-223 OH, 186 out-of-plane, 224, 228 symmetric, 237

Bending vibration, CH, 371 Benzene ring, 372 Benzoic acid, spectra of, 386-387 Bessels function, 64 BHC, 131-134 Biological samples, 368 Biological systems, model for, 325 Birefringent plate

optical, 72 for vibrational circular dichroism spec-

tra, 83 Bisphenol A, 371 Bituminous coal, 187, 196, 211, 219-220,

222, 228-229 Bolometer, 251 Brass, 361 Brown-Ladner equation, 236, 238

C

Cadmium arachidate band assignment for monolayers of, 303

Index 395

infrared spectra of, 301-302, 306, 326-329

Raman spectra of Langmuir-Blodgett films of, 304

Calcium carbonate, 380-381 Calcium fluoride, 340 Calibration

curve, 73-74, 78, 83, 84, 379 far-infrared, 252

Capillary systems, 115-117, 118 Carbon

in coal, 215, 233, 235 and hydrogen, 238 photoacoustic spectra, 357, 359, 360

Carbon black for background correction, 357,

360 spectrum, 385-386, 387

Carbon dioxide evolution, 159, 160 Carbon monoxide, 330-332, 339, 340-342,

375, 378, 379 Carbon tetrachloride, 375 Carbonyl

band, 371, 381 groups, 182-183, 211-212 stretching mode, 320

Carboxyl groups, 204, 207-210 Carboxylate

groups, 222 stretch, 337

Carboxylic acid, 197 Carboxylic acid groups, 182-183 Catalysts

adsorbed species, 375-376, 378-379

depth profiling, 381 diffuse reflectance, 258-264

Cation and inorganic species identification, 278

CB, see Circular birefringence CC stretching, 186 CD, see Circular dichroism Cell, photoacoustic, see Photoacoustic cell CH

bending aliphatic, 184 vibrations, 186, 189, 329, 371

groups, 189, 213-235, 301-302, 305 rocking vibrations, 224, 329 spectrum, 163

stretching frequency, 378 mode

aliphatic, 179, 199, 219, 225, 229 aromatic, 195-225

region, 162, 163, 179, 224-225 Chain, folded, 311 CH2 groups, 301-302, 305 Chemistry, surface, 315, 348, 371, 389 Chromium-coated substrate, 309, 310 Chromium reflectivity ratio, 299 Chemigram, 152 Chlorophenols, 133-138 Chromatogram

flame ionization, 102, 109, 110 functional-group, 105, 106, 112, 123,

127 geranium oil, 105-112 Gram-Schmidt reconstruction, 107, 109,

111, 112, 116, 119, 123, 125, 127 infrared, 98 and interferogram, 105, 106, 108 reconstructed, 105-112

Chromatography plates, 369 Circular birefringence, 83-84 Circular dichroism, 61-95 C NMR, see Spectroscopy, C-NMR Coal

acetylated, 203 anthracite, 195, 196 bituminous, 187, 196, 211, 219, 220, 222,

228-229, 237 boghead, 226 characterization studies, 191 CH groups, 213-235 chromatogram of liquefied, 112 combustion product, 138, 139 COOH content, 197 functional groups, 169-241 infrared spectrum, 175-182 and isomeric compound spectra, 117 OH content, 197 oxygen content, 183, 196-213 oxidized, 207 photoacoustic spectroscopy, 374-375 structural parameters, 235-240 subbituminous, 221, 222 and surface age, 374 versus vitrinite, 233, 234 volatile components, 374

396

Cobalt emission and cross-correlation spectra, 42-43

Cobalt molybdate, 259 Compressed plot, 153 Computer

analog switch, 335 vibrational spectra data, 316

Condensed phase and photoacoustic signal, 378 spectra

diffuse reflectance, 131 samples, 368, 386, 387, 389-390

Convergence criteria, least squares itera-tion, 28

Convolution product functions, 7-19, 20

Cosine, 356 CO stretching, 186 Cotton spectra, 369 Cross-correlation function, 38-43 Cryogenic detectors, 284 Crystal

expitaxially grown, 312 mixed phase, 278 plane, 340 polymer, 311-313 reflection, 370, 371 single phase, 276, 278 surface, 338

Crystallization kinetics, 372, 373 Curing process, polymer, 371, 372 Curve-fit

band, 224 data, 228-229 least squares, 225, 227 spectra, 208, 209

Curve-resolved bands, 186, 188, 189 Curve-resolving procedures, 185, 200, 201,

207, 209, 237

D

DAC, see Diamond anvil cell Damping factors, 16 Data collection for vibrational circular

dichroism spectra, 85-89 DDT, diffuse reflectance spectra of, 130 Deconvolution product function, 17-19,

20

Index

Depth-profile analysis of samples, 354 Derivative

second, 185-189, 201, 208-209, 223 discussion of, 200

Derivative spectroscopy, 12-13 Destination file, 153 Detector

cryogenic, 284 deuterated triglycine sulfate, 287, 301,

357, 358, 359, 360 in far infrared, 251-252 mercury cadmium telluride, 301,

327 photoacoustic, 347 pyroelectric, 357 thermal, 251

Diacetylene units in fatty acid monolayers, 304-308

Diamond anvil cell, 252 Dichroic measurements, 66 Dichroic ratio, 372 Difference spectrum, 200, 202, 207, 223,

225 Diffraction, x-ray and electron, 305 Diffuse reflectance

and coal spectra, 194-196 of cobalt molybdate, 262, 263 of DDT, 130 definition of, 346 general theory, 194-195 spectroscopy, 243-281

Diffuse reflectance, infrared Fourier trans-form spectrometry, 252-258, 346, 380

and alkali metal halides, 270-278 of benzoic acid, 386, 387 measurements and zeolites, 265, 268,

270-279 of sodium chloride powder, 272, 273 zeolites and measurements, 265-271 zeolite spectra, 269, 271

Digital smoothing polynomial, 20-21 Dipole-dipole coupling, 331 Dipole moment, 306-307, 310 Dipole transition moment, 318, 327 DISPO, see Digital smoothing polynomial DMSO-</6 as solvent, 92 DR, see Diffuse reflectance DRIFT, see Diffuse reflectance infrared

Fourier transform spectrometry

Index

DTGS, see Detector, deuterated triglycine sulfate, 287

DuPont 310 curve resolver, 15

E

EELS, see Electron energyrloss spectros-copy

E field, 292-297 EGA, see Evolved-gas analysis Eigenspectra, 34-36, 40 Eigenvalues and error analysis, 39 Eigenvector

error, 39 matrix of, 31-34 and mixture spectrum reconstruction,

35,40 Electrode potential, 340-341, 342, 343 Electromagnetic radiation, 317

polarized, 284-291 Electrolyte, 340-342 Electret-condenser microphone, 362-363 Electric field, 285, 318 Electric field vector, 284, 318-320 Electron energy-loss spectroscopy, 315 Ellipsometry, 325, 326 Emission spectra and cross-correlation, 42 Enantiomers and vibrational circular di-

chroism, 81, 82, 83, 88, 93, 94 Energy

absorbed, 348, 349 spectrum, 288 levels, vibrational, 186

Error in beam convergence, 8 in Brown-Ladner equation, 238-239 eigenvalues, 39 experimental and real, 32 and factor analysis, 33 in oxygen content determination, 213 phase, 74, 356, 382-383, 384, 385 random, 26 real and experimental, 32

Ether, 197, 199, 201, 203, 212, 213 Ethylene glycol, 372 Evolved-gas analysis, 148, 149-163 Evolved gas and temperature, 158 Exinites, 238 Extinction coefficient, 197, 201, 317

397

F

Factor analysis, 3, 30-38, 41 Far-infrared and diffuse reflectance spec-

troscopy, 243-281 instruments, 245 optical layout, 245-247

Fermi resonance complex, 177, 180 FGC, see Functional group chromato-

gram Fiberglass fillers, 156-163 FID, see Flame ionization detector Field

crystal, 311-313 E, 292-297 electric, 292, 294, 295, 298, 318, 319,

342 H, 292-294 magnetic, 294, 295 optical, 291, 292, 295, 298, 299, 304 polarized electric, 310

Film absorbing dielectric, 297 anisotropic, 299-300 applications, 300-313 doctor blading of, 289 hydroscopic problems of, 288 Langmuir-Blodgett, 287, 289-291, 300-

308, 329 monolayer phase, 290 polymer, 288-289, 308-313, 323 submicrometer, 283, 284 thin, see Thin film ultrathin, 286

Filters, far-infrared, 252 Flame ionization detector chromatogram,

109-111, 119, 131, 132, 135 Fluorescence, 369, 388 Fluorescent compounds, 383 Fourier series, 64 Fraction aromaticity, 236-240 Fractions, gas chromatography, 100-101,

104-105 Fresnel

equations, 291, 293, 320 coefficients, 292-295

Functional-group chromatogram, 105, 106, 112, 123, 127

Functional-group-identification algorithms, 46

398 Index

Functional groups oxygen containing, 171, 196-213 and temperature, 152

G

Gas adsorbed, 346 evolved, 149-163 ideal, 352 inert, 337 phase and photoacoustic signal, 378 for photoacoustic cell, 362, 363 thermal diffusion length, 361

Gas chromatography alternative approaches to, 142-143 and Fourier transform interferometry,

97-145 peak identification, 142 resolution, 142 spectra of, 124 mass spectroscopy, 97

GC-FT-IR, see Gas chromatography, and Fourier transform interferometry

GC-MS, see Gas chromatography, mass spectroscopy

Gaussian instrument line shape, 18, see also Lorentzian and Gaussian

Gaussian and Lorentzian, see Lorentzian and Gaussian

Geometry of vector, 319 Geranium oil chromatogram, 126 GIFTS search routine, 113 Gilford-Nicolet photoacoustic cell,

365 GIR, see Grazing incidence reflection Globar source, 247 Golay detector, 251 Gold for thin film, 321 Gram-Schmidt chromatogram, 105-113,

116, 119, 123, 125, 127 Graphics digitizers, 43 Grazing incidence, 319, 322-323,

339 Grazing incidence reflection

accessory, 287 band shifts, 311 measurements, 311

of polymerized hexacosa-10,12-diynoic acid, 308

reflectivity ratios and, 310 spectra of, 309 technique, 286-288 and thin film, 284, 285

Grinding conditions, 192 instruments, 191

Group frequency identification by CH vibration, 189

GSC, see Gram-Schmidt chromatogram

H

Heat detection, 382

acoustic signal, 350, 352 exchange, 376-377 sample conduction, 361 signal, 383

Helium, 361 Helmholtz oscillator, 364 Helmholtz resonance, 360 Hemoglobin, 388 Heterocyclic groups, 211, 212, 213 Hewlett-Packard power supplies,

288 H field, 292-294 Hit quality index, 113, 115 Homologous compounds, 138 HQI, see Hit quality index Humic acid, 369 HVA, see Vitrinites, high volatile A Hydroaromatic structures, 181 Hydrochloric acid, 341 Hydrogen

alicyclic, 236 carbon ratio, 238 concentrations and weight percent car-

bon, 235 functional groups

coal, 216-217, 230 vitrinite, 231

Hydrogen bond, 183, 211 Hydroxyl groups and zeolites, 268 Hygroscopic potassium bromide,

193

Index 399

I

ILS, see Instrument line shape function Imide

band, 166 group, 165

Imidization, thermally induced, 165-167 Incidence

angle, 318-322, 337 grazing, 319 normal, 318

Infrared polarizer, 323 reflection-absorption spectroscopy, see

Reflection-absorption spectroscopy spectral data processing, 1-59 vibrational circular dichroism, 61-95

Inorganic compounds, 26, 258, 259 Inorganic species, 255, 278-279 Instrument line shape function, 4 Integration, 11 Interferogram

calibration experiments, 68-75 and chromatograms, 105, 106, 108 double-sided, 382 experimental, 71, 72, 76 and Fourier transformation, 86 and gas chromatography, 98 group-specific, 46 mirror motion, 334-337 noise level, 86 normal transmission, 70 out-of-phase, 142 phase correction, 356 and reference subspace, 106 sample, optically active, 75-78 and sampling interval, 88 single-sided, 288 theoretical, 71, 77 tilting modification method, 8, 9 transmission, 72, 73, 75, 76

Interferometer evacuable, 288 Fourier transform, 385 optics, 245-246

Interferometry coal, 169-241 gas chromatography, 97-145 and photoacoustic spectroscopy, 348

systems, 138-142 and thermal analysis, 147-167 thin film, 283-313 and vibrational circular dichroism, 6 1 -

62, 79, 80, 85, 88, 89-90 Interpolation procedures, 19-20, 43 Inverse peaks, 274, 277 Ion, 138-140 Ion exchange, 197, 207, 258 Ionic

materials, 256 solid state exchange, 264

IRRAS, see Reflection-absorption spec-troscopy

Iron emission and cross-correlation spec-tra, 42-43

Iron oxide spectra, 278, 279 Isomers, 131-132 Isotherms, monolayer transfer, 291 Isothermal program operation, 165 Isotropie

reflection, 291-292 sample, 312

J

Jasmine oil, 118, 123, 124

K

Kaolinite, 192 Kinetic crystallization, 372, 373 Kramers-Kronig analysis, 278 KRS-5

polarizer, 90 as substrate for monolayer, 305 transmission measurements, 289

Kubelka-Munk equation, 255 function, 195 plot, 387

L

Lamellar, folded chain, 311 Laminates, thin film, 283

400 Index

Langmuir-Blodgett, 287, 289-291, 300-3 biological systems, 325 cadmium arachidate, 334 monolayer, 323, 329

Lasers, 347 interferometer, 86, 87

Lattice aluminosilicate, 264 orthorhombic, 307 structure

catalyst, 264 bands, 270

vibrations, 313 L-B, see Langmuir-Blodgett LD, see Linear dichroism Least squares

curve fitting, 3, 8, 15, 24-30, 200-201

iterative process, 225, 226, 228 Library

algorithm, unknown spectra, 44 compilation, 43-44 search, 35, 43, 45-46 user-created, 135 vapor phase, compound identification,

113 Light

absorption optically opaque samples, 354-355 stray, 360 vacuum-metal interface, 318

and diffuse reflectance, 254 incident, 350-352 scatter

inorganic species, 278 Raman, 324

wavelength, 255, 256, 277 Light pipe, 99-100, 113, 118, 148, 149 Lignite, 207, 209-210 Linear dichroism, 83-84 Lock-in amplifier and output, 69, 85, 87 Lorentzian and Gaussian

curves and integration, 10-11 forms, 12 interpolation, 20 least squares, 180, 185 peaks, 12, 14, 24, 37, 38 perturbation, 15 profiles, 16 sum, 17, 225

Lower Kittaning seam, 229 Low-temperature ash, 221-224 LTA, see Low-temperature ash Lyddane-Sachs-Teller equation, 274

M

Macerals, 172 Mass spectra, 138-142 Maxima

plot pattern, 162-163 temperature, 157 thermal conductivity, 151-152

Maxwell's equations, 292, 294 MCT, see Mercury cadmium telluride Mean square fit, 20 Mercury cadmium telluride, 327

detectors, 85, 89, 90, 101-103 Mercury lamp, 247 Metal halides, 256, 270-276 Metal mesh beam splitters, 248-251 Methyl

band, 327 groups, 236-237 plot, 153 stretch, 327

Methylene, 327 Methylsilane, 378 Micelles, 174 Michelson interferometer, 67-78, 86, 103,

142, 283, 287, 334 Microelectronics, thin film, 283 Microphone, 347, 350, 361, 364, 366-367,

386 types of, 283

Mid-infrared spectrum diffuse reflectance, 260 photoacoustic, 261

Mineral content, coal, 374 Mineral matter

and bands, 221-222, 224 coal fraction, 203 corrections for, 227, 233

Minimum-intensity threshold, 10 Mirrors, diffuse reflectance, 257 Mirror velocities, 359-360 Mixture

and pure-component extraction of, 23 -24, 25, 30

Index

spectra error analysis, 39 factor analysis, 30-38

Modulation photoelastic technique, 333 Mole fraction changes, 165 Molybdate catalysts, 258-264 Monochromator, 355 Monolayer

assemblies, Langmuir-Blodgett, 300-308 phase, thin film, 290

MS, see Mass spectra Mulls

hydro-/fluorocarbon, 346 Nujol, 213

absorption bands, 214 Multiple correlation coefficient, 27 Mylar beam splitter, 386

N

Near-infrared spectra diffuse reflectance, 267 transmission, 266

NH groups, see Amino groups NH stretching region, 177 Nickel, catalyst, 378 Nicolet 7199 spectrometer, 149 Nitric oxide gas, 269 Nitrogen, 222, 378 Nitrogen gas, dry, 288, 289 /?-Nitrophenol, 376, 377 Noise

background, 188, 348, 386 high-absorbance, 248 spectrum, 79-80

Norit-A spectra, 357, 358, 360 Nujol

absorption bands, 214 mulls, 213

Nylon fiber spectra, 369

O

OH bending modes, 211 content

band intensity, 198 coal, 235

401

groups, 183, 197-207 stretching region, 177, 265

Oil shale, 226-227 Oils, spectra of, 117-127, see also specific

oils Optical absorption

coefficient, 352, 384 length, 355

Optical effects, spectral data, 6-9 Optical electric vectors, 296 Optical fields, 291, 292, 295, 298, 299, 304 Optical samples

opaque, 354-355 transparent, 353-354

Optical velocity, 356 Optics

interfaced, 323, 324 interferometer, 245-246 polarization-double modulation, 334

OP. VEL., see Optical velocity Organic matter, 222 Orthogonal dielectric functions, 294 Oxidation

carbon monoxide, 342, 343 coal, 195 low-temperature, 198 state, 315

Oxidized coal, 207 Oxygen

calculation, hydroxyl, 205 in coal

content, 183 functional groups, 196-213 high content spectra, 205

plot, 204 for sample curing, 372

P

PA, see Photoacoustics Palladium, 330, 331 Palmitic acid, 375 Particle size, 255-256, 261, 277 PAS, see Photoacoustic spectroscopy PBT, see Polybutylene terephthalate Peak

frequency, 275 intensity, 9-11 inverse, 274, 277

402 Index

noise, 383 shape, 11

PEM, see Photoelastic modulator Peppermint oil, 123 Perchloric acid, 341 Perfume, 117-127 Perturbation, Lorentzian and Gaussian,

15 Pesticide, 127-138 PET, see Polyethylene terephthalate Petroleum distillate, 117 Phase

change, 318-319 information, 356, 383, 384 shift

correction, 73, 288 mathematics, 361 vector components, 319

Phenacetin, 369 Phenol, 186-187

esters, 203 groups, 172, 182, 184 resins, 184

Phenolic OH, 201, 203, 206-207 Photoacoustic cell

evacuable, 366, 367 Gilford-Nicolet, 365 modulation rate, 360-363 Rosencwaig's original, 365

Photoacoustic effect, 347 detector, 347 model, 350 parameters, 351 quantitative description, 350-

355 theory, 348-350

Photoacoustic signal, see Signal, photoacoustic spectrum, 261 studies, 190

Photoacoustic spectroscopy adsorbed species, 375-376 applications, 367-368 background correction, 357-360 carbon, 357, 359 cell

design, 360-367 pressure, 363 temperature, 363

coal, 374-375

condensed-phase sample, 386, 387, 389-390

depth profiling, 381-385 dispersive, 355-357 far-infrared, 385-388 near-infrared, 388-390 parameters, 351 phase lag, 382-385 polymers, 370-374 and quantitative analysis, 376-381, 389 thermal diffusion, 381 visible, 357, 388-390

Photoelastic modulation technique, 333, 339, 340

Photoelastic modulator, 62, 63, 65-66, 68, 70, 81-82, 84, 90

Photometry, 347 Photon, 284, 316 Plane of incidence, 292-293 Platinum, 339, 342, 343, 375 Plot patterns, 152-156 PMMA, see film, polymer Polar compounds, 111 Polar materials, 115 Polarization

absorption factors, 321 directions, 292 double-modulation, 332-343 electric field, 318, 319 incoming beam, 285 measurements, 305 perpendicular, 291 state, 320

Polarized electric field, 310 Polarized reflectance-absorption spectrum,

324 Polarized vectors, 63 Polarizing interferometer, 90 Pollutants, environmental, 127-138 Polyacetylene, 373 Polyacrylonitrite co-styrene, thin film of,

325, 326 Polybutylene terephthalate, 372 Polyester, 159, 370 Polyethylene, 349, 363, 387

crystallization, 311-313 Polyethylene terephthalate, 372, 373 Polymerization, topochemical, 304, 307 Polymer

amorphous, 308-313

Index 403

film, 308-313 laminar, 383 semicrystalline, 311-313

Polynomial fitting, 19-20, 43 Pore structure, catalyst, 264 Potassium bromide, 191-193, 197, 337,

363, 371, 380 integrated absorption, 191 hygroscopic, 193 pellets, 211, 232, 346, 349 spectra, 276, 277

Potassium chloride and coal samples, 194-195 spectrum, 276, 277

Potassium nitrate, 377 mid-infrared spectra of, 278

Power spectrum, 382 /?-polarized radiation, 292 Pure component extraction, 23-24, 25, 41

factor analysis, 30-38 Pyridine, 175, 375 Pyrite, 222 Pyroelectric detector, 357 Pyrolysis, 148, 154

0

Quadratic interpolation, peak location, 10 Quantitative analysis

diffuse reflectance spectra, 195 photoacoustic spectroscopy, 376-381

Quartz, 349, 363 Quinones, 184, 197, 210, 211, 212, 213

R

Radiation diffuse reflectance, 254 electromagnetic, 317 incident, 318, 348 modulation, 349 polarized electromagnetic, 284, 291 polychromatic versus monochromatic,

5 Radiochemical analysis, coal, 201, 204 Raman

cross sections, 304 light scatter, 324 spectroscopy, 305, 316

Random-access files, unknown spectra, 45 Rare earth oxides, 388 Ratio method, mixture spectra, 23-24 Real-time graphics, 23 Reconstructed chromatograms, 105-112 Reflectance

absolute, 195, 255 diffuse, see Diffuse reflectance

Reflection anisotropic, 300 complex coefficients, 320 dielectric-metal interface, 291-300 Fresnel coefficient, 292-295 index, 277, 278 isotropic, 291-299

Reflection-absorption spectroscopy conventional, 323-332 dispersion, 333 and grazing incidence reflection, 285 polarization-double modulation, 332-334 theory of, 315-323

Reflectivity ratios grazing incidence reflection, 310 Greenler, 297-299

Reflectometer design, 257 Refractive index, 8, 317, 320 Regression analysis, 220, 221 Residuals and thermal analysis, 164-167 Resin, 368 Resolution, spectral, 142 Resonance, 360, 361 Reststrahlen effect, 255-256, 270-278 R-G theory, see Rosencwaig and Gersho

theory Ring

aromatic, 182-183, 236-237 stretching mode, 184

Root mean square deviation, 28, 227 Rosencwaig

condensed-phase sampling, 364 photoacoustic cell, original, 365

Rosencwaig and Gersho theory, 350-355 Rose oil, 125 Rubber, 371

S

Sample air-sensitive, 354, 372, 389 optically thick, 370

404 Index

Sampling techniques in far infrared, 252-253 theorem, 86-89

Sandal wood oil, 125, 127 Saturated fatty acids and thin film, 300-

304 Saturation effects, 368, 388, 389 Scan speed, 104 Scatter

background, 193, 217 coal spectra, 198 condensed-phase samples, 389 diffuse reflectance, 194-195, 254-

255 effects, 277 elemental hydrogen analysis, 235 fraction aromaticity, 236 lightwave, 376 nonlinear, 380-381 particle size, 265 Raman, 304, 324

Search gas chromatography, 113-115 spectral, 113-115, 121, 122

Second derivative, 185-189, 208-209 Semiconductor materials, 258 SERS, see Surface enhanced Raman

spectroscopy Signal

background, 360-361 heat-generated, 383 photoacoustic, 361, 363, 375, 376-377,

382, 384, 385 Signal-to-noise ratio, 5, 6, 12-13, 17, 20-

21, 23, 32, 34, 89, 90, 104, 105, 108, 170, 245, 254, 255, 256, 287, 299, 301, 309, 348, 375, 385, 388, 389

Silica, 369, 375, 378 Silicon dioxide, 378-379 Silicone grease, 324, 325 Silver

reflectivity ratio, 299 substrate, 309, 329, 336

Silver bromide, 289 Silver cyanide, 368 Single-phase crystal, 276, 278 Smoothing, 20-21 SIN, see Signal-to-noise ratio Sodium chloride, 212-215 Sodium hydroxide, 208-209

Sol/gel ratio, 175 Space-filling models, 173 Specific extinction coefficient, 197-

199 Spectral degradation, 309 Spectral function groups, 2 Spectral profile, 188 Spectral subtraction, see Subtraction,

spectral Spectroelectrochemical cell, 340 Spectrometer, 323-325 Spectrometry, dispersive, 348 Spectrophotometer for Digilab system,

148 Spectroscopy

Auger electron, 339 C-NMR, 235-237 coal, 184, 190 electron energy-loss, 315 fast, 244 photon, 284, 316 Raman, 316 transmission, 375 vibrational, 283, 301, 302, 305, 306, 309,

315 Spectrum

background, 355 difference, 200, 202, 207, 223, 225 in-phase, 388 power, 382 surface species, 316

Spline functions, 20 s-polarized radiation, 292 Stainless steel, 361, 380 Standard deviation, calibration curve,

379 Steric strain, 181-182 Stretching band, carbon monoxide, 330,

331 Stretching frequency, CH, 378 Stretching modes, 176-182, 184, 186

aliphatic, 199, 219 aromatic, 195, 371 methyl groups, 237

Stretching region carbonyl, 200, 201 CH, 224-225, 306, 307 C0 2 , 306, 307 OH, 177

Stretching ring, 177, 184, 186

Index 405

Stretching vibration, CO, 342 Subtraction

scaling parameters, 30 spectral, 22-23, 159-160, 221, 285, 368,

370, 383-384 techniques, 268

Sulfur, 222 Sulfuric acid, 341, 342 Surface

molecular ordering of, 323 morphology and photoacoustic signal,

376-377 species, 315-317, 340

Surface chemistry, see Chemistry, surface Surface enhanced Raman spectroscopy,

316

T

TE, see Transverse electric polarization Temperature, 147, 148 Thermal analysis, 147-167 Thermal conductivity, 150-152 Thermal detector, 251 Thermal diffusion

length, 352-353, 361,389 method, photoacoustic spectroscopy,

381 Thermal structural changes, 149 Thermal vibration, 350 Thermogravimetric analysis, 147-167, 192-

193 Thin film

of acetone, 320, 321 orientation and lateral order of, 283-313 spectra, 284 studies, 285 ultra, 286 water, 288, 291

Time independent signal, 64 TM, see Transverse magnetic polarization Transducer, acoustic and optical, 348,

361 Transition dipole moment, 284, 301 Transition spectrum, conventional, 356 Transmission

Fresnel coefficient, 292-295 measurements, 285, 301, 308, 310 methods, 189, 190

spectra cobalt molybdate, 263 conventional, 346 versus photoacoustic, 388-389 polymers, 371, 372 silver cyanide, 368

zeolite techniques, 265 Transverse electric polarization, 292 Transverse magnetic polarization, 292 Tungsten-iodide lamp, 388

U

Ultraviolet irradation, 307 visible region, 350

UV, see Ultraviolet UVH, see Vacuum, ultrahigh

V

Vacuum operation, 246 photoacoustic cell, 366-367, 386 pyrolysis, 358 ultrahigh, 323, 329, 330, 337-339

Van der Waals intermolecular interactions, 304

Vapor phase, 113, 148 VCD, see Vibrational circular dichroism Vector, electric, 319-320 Vibration, see also Spectroscopy, vibra-

tional bending, 189, 329, 371 mechanical, 350 rocking, 224, 329 stretching, 342

Vibrational circular dichroism, 61-96 Vibrational modes, methyl, 237 Vibrational spectra, surface species, 316 Vibrational spectroscopy, see Spectros-

copy, vibrational Vibromilling, 193 Vinylsilane, 378 Viscosity, Langmuir-Blodgett, 291 Vitrinite

versus coal, 233, 234 concentrates, 176, 179, 185, 186, 187,

188, 211, 212, 219

406 Index

fraction aromaticity, 236, 237 high-volatile A, 224-225, 228 hydrogen-containing functional groups,

231 low-volatile, 227 plot, 237

W

Water absorption, 192, 288 adsorption, 195, 196, 197 evolution, 155 free, in diffuse reflection, 268 OH bending mode, 211 vapor, 246 purity, 291

Wavelength and phase error, 356 Wavenumber of maximum adlayer absorp-

tion, 321

Wave propagation, 296 Wig-L-Bug, for grinding, 190, 191, 192 Wiser model, 174 Wool, 369-370

X

x ray crystallography, 278 scatter curves, 172

Z

Zeolites, 264-270 Zero-displacement correlation, 46 Zero path difference point, 70, 73-75, 76,

77, 78, 87 Zinc selenide, 90, 289, 363 ZPD, see Zero path difference point

Fourier Transform Infrared Spectra. Applications to Chemical Systems

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