Theoretical study on fulvic acid structure, conformation and … - 030217... · 2016. 5. 20. ·...

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Science of the Total Environm

Theoretical study on fulvic acid structure,

conformation and aggregation

A molecular modelling approach

R.A. Alvarez-Pueblaa, C. Valenzuela-Calahorrob, J.J. Garridoa,*

aDepartment of Applied Chemistry, Public University of Navarra, Campus Arrosadı́a, E-31006 Pamplona, SpainbDepartment of Inorganic Chemistry, Faculty of Pharmacy, University of Granada, E-18071 Granada, Spain

Received 17 February 2003; received in revised form 29 October 2004; accepted 12 November 2004

Available online 26 May 2005

Abstract

The ubiquitous presence of humic substances (HS), combined with their ability to provide multiple sites for chemical

reaction, makes them relevant to numerous biogeochemical processes such as mineral weathering, nutrient bioavailability,

and contaminant transport. The reactivity of HS depends on their functional group chemistry and microstructure, which

are in turn influenced by the composition of the surrounding media. In order to help towards an understanding of

structure conformations and aggregation process of HS in soils and waters and to get a better knowledge of these kinds of

materials, a fulvic acid (FA) has been modelled as a function of its ionic state under different conditions. Our proposed

theoretical model based on the Temple-Northeastern-Birmingham (TNB) monomer fits well with experimental observations

on the solubility (dipolar moment) and electronic and vibrational spectra of FAs. The presence of water molecules has a

great stabilization effect on the electrostatic energy; this effect is greater as ionized rate increases. In vacuum, the non-

ionized aggregated species are more stable than monomers because of the increase in their interaction due to H-bonding

and non-bonding forces. When the molecules are ionized, no aggregation process takes place. In solution, the FA

concentration is a critical factor for the aggregation. The system containing two FA molecules probably did not form

aggregates because its equivalent concentration was too low. When the concentration was increased, the system gave rise

to the formation of aggregates. The ionic state is another critical factor in the aggregation process. The ionized FA has a

0048-9697/$ - s

doi:10.1016/j.sc

Abbreviation

optimized poten

annealing; SE, s

UV–vis, ultravio

* Correspondi

E-mail addre

ent 358 (2006) 243–254

ee front matter D 2005 Elsevier B.V. All rights reserved.

itotenv.2004.11.026

s: FA, fulvic acid; FTIR, Fourier transform infrared spectroscopy; HS, humic substances; MM, molecular mechanics; OPLS,

tials for liquid simulations; PM3/tm, parameterization model 3/transition metals; QD, Quenched Dynamic; SA, simulated

emi-empirical; TIP3P, transferable intermolecular potentials with three point charges; TNB, Temple-Northeastern-Birmingham;

let–visible spectroscopy; ZINDO/S, Zerner’s intermediate neglect differential overlap; MD, molecular dynamics.

ng author. Tel.: +34 948 169601; fax: +34 948 169606.

ss: [email protected] (J.J. Garrido).

R.A. Alvarez-Puebla et al. / Science of the Total Environment 358 (2006) 243–254244

higher electric negative charge, which increases the energetic barriers and inhibits the approximation of FA caused by the

Brownian movement.

D 2005 Elsevier B.V. All rights reserved.

Keywords: Fulvic acid; Structure; Conformation; Aggregation; Molecular modelling; Molecular dynamics; Electronic spectrum; Vibrational

spectrum

Table 1

Experimental and theoretical selected chemical properties for FA

Experimental data FA (I)

C (%) 40.1 42.26

H (%) 3.57 3.16

N (%) 0.67 1.33

S (%) 0.65 3.05

O (%) 55.0 50.2

Formula C35H38N1S0,3O36 C37H33N1S1O33Carboxylic groups 6 6

Phenolic groups 3 4

hMwia (g mol�1) 1,058 1,051a Average molecular weight estimated from Mw=3.99q280+490

according to Chin et al. (1994).

1. Introduction

Humic substances (HS) can affect soil fertility,

mineral weathering, and water acidity; they are

involved in the transport, sequestration, and mitiga-

tion of contaminants; and may even have an impact on

atmospheric chemistry through the carbon cycle, in

which carbon is constantly recycled among plants,

animals, soil, air, and water (Stevenson, 1982).

A special characteristic of HS is its capacity to

show spontaneous changes in their conformation and

aggregation state as a function of solution conditions

like pH and ionic strength (Senesi, 1999). The eluci-

dation of the aggregation mechanism of HS is impor-

tant because these processes have a great influence on

their interaction with nutrients and contaminants in

soils and waters (Wershaw, 1999; Alvarez-Puebla et

al., 2004a). The aggregation process has been studied

using various techniques such as ultraviolet–visible

spectroscopy (Senesi, 1999), cross-polarization

magic-angle-spinning nuclear magnetic resonance

(Tombacz, 1999), light and X-ray scattering (Chin

et al., 1998; Manning et al., 2000) microscopy

(Senesi et al., 1997; Myneni et al., 1999; Alvarez-

Puebla et al., 2004b), size exclusion chromatography

(Swift, 1999) and dialysis and ultrafiltration (Jones

and Bryan, 1998). However, little direct testing of

this phenomenon has been carried out.

Systematic studies of the structures of these sub-

stances are the first step towards understanding how

they interact with other elements and compounds.

Such knowledge will be necessary to predict and

control the impact of chemical and biological

changes in the environment. Developments in soft-

ware and hardware permit an advance of theoretical

approximations in the study of FA aggregation pro-

cesses using molecular modelling (Bruccoleri et al.,

2001). In recent years the number of papers in which

molecular modelling is used to study HS structures

has increased (Schulten, 1995a,b; Sein et al., 1999).

This technique permits the study of physical, chemi-

cal (Schulten and Leinweber, 2000) and electronic

properties (Bruccoleri et al., 2001) at the molecular

level and some interaction mechanisms between HS

and other molecules present in the environment:

mineral phases (Schulten and Schnitzer, 1997; Shev-

chenko et al., 1998), pesticides (Kubicki and Apitz,

1999; Schulten, 1999) and transition metals (Davies

et al., 1997; Kubicki et al., 1999).

Most of the published models have been carried

out under vacuum conditions. This approximation has

been widespread (Schulten, 1995a,b, 1998; Schulten

and Schnitzer, 1997; Shevchenko et al., 1998; Sein et

al., 1999). However, some authors have suggested that

this approximation is unreliable because the natural

media of HS is aqueous solution (Kubicki, 2000;

Schulten and Leinweber, 2000; Bruccoleri et al.,

2001). Therefore, studies have been performed in

which the HS has been solvated with a small number

of water molecules in order to simulate the hydration

effect on their geometry and stability (Davies et al.,

1997; Schulten, 1999; Kubicki, 2000).

The aims of this research are: (i) to propose a

model for FA based on experimental results (ele-

R.A. Alvarez-Puebla et al. / Science of the Total Environment 358 (2006) 243–254 245

mental composition, number of acidic groups and

hMwi); (ii) to validate the proposed model againstthe experimental UV–Vis and IR spectra; and, (iii) to

use computational techniques to study the structure,

conformations and aggregation of the FA model as a

Fig. 1. Molecular modelling methodology for the TNB modified FA (I): (i

conformational space exploration (graph); and, (iv) geometry optimization

function of the ionic state in various modelling

conditions. In order to reach these aims the FA

fraction of a commercial HS was extracted, purified

and characterized. Computational techniques were

used to propose the FA model based on the TNB

) 3D structure (II); (ii) geometry optimization with OPLS (III); (iii)

with OPLS of the structure number 7 (IV).


model (Sein et al., 1999) and additional character-

ization data. This proposed FA model was used to

study the structure, conformations and aggregation

process.

2. Experimental

2.1. Extraction, purification and characterization of

the FA

The FAwas fractionated from a commercial HS by

Acros Organics (Geel, Belgium) by adjusting the pH

of a 40 g L�1 HS solution to 1.0. The obtained FA

was purified using a XAD-8 resin column, converted

to the protonated form by passing it through a proton-

saturated resin and freeze-dried, in accordance with

Fig. 2. Structures obtained by geometry optimization with PM3/TM, for th

with the carboxylic and phenolic groups ionized (VII).

the procedure proposed by the International Humic

Substance Society (IHSS) (Swift, 1996). The C, H, N

and S contents were determined by an elemental

analyser CHNS EA1108 (Carlo Erba Milan, Italy).

The UV–Vis spectrum was recorded by a Lambda 3B

spectrophotometer (Perkin Elmer, Norwalk, USA) in

the range from 900 to 200 nm in accord with Chen et

al. (1977). The IR spectrum was recorded by a FTIR

Avatar 360 spectrometer (Nicolet, Madison, USA)

co-adding up to 200 scans with 4 cm�1 of resolution

(Niemeyer et al., 1992). The COOH groups and the

total acidity of FA were determined by calcium acet-

ate and barium hydroxide methods (Stevenson,

1982), respectively. The phenolic acidic groups

were calculated as the difference between the total

acidity and that of the carboxylic acidic groups. The

acid–base constants were estimated from the end

e non-ionized FA (V), with the carboxylic groups ionized (VI) and


points obtained from a potentiometric acid–base titra-

tion in a Metrohm Titrino 702SM autoburette.

2.2. Molecular modelling of the FA in vacuum

conditions

Molecular modelling was carried out with

HyperChem 7.01 Software (Hypercube, 2002a). The

model design was based upon the TNB model (Sein et

al., 1999), in accordance with the elemental composi-

tion, number of acidic groups and hMwi (Table 1). Themodified TNB model (I) (Fig. 1) was optimized by

using the OPLS force field (Jorgensen and Tirado-

Rives, 1988) with the Polak-Ribiere algorithm. In the

present study, the convergence limit was set by a max-

imum acceptable gradient of 0.042 kJ mol�1 nm�1.

Quenched Dynamic (QD) cycles at 700 K with a step

size of 1 fs, were performed on the optimized structure

(III) in order to explore the conformational space

(Balbuena and Seminario, 1999). The most stable

local minima were re-optimized in the same conditions

and the most stable minimum (IV) was optimized by

using the SE method PM3/TM (Hypercube, 2002b)

with a convergence limit of 0.418 kJ mol�1 nm�1

(Young, 2001) (Table 2). For the modelling of the FA

as a function of the ionic state, the carboxylic groups

from structure (I) (Fig. 1) were deprotonated first fol-

lowed by the phenolic groups. The procedure followed

for the molecular modelling was equal to that which

was followed for the protonated FA.

The electronic and vibrational spectra were calcu-

lated on structure (V) (Fig. 2). The electronic spec-

trum was calculated by using the microstates method

(Merchan et al., 1998) carrying out a single point

calculation with ZINDO/S SE method (Zerner,

1991) using the configuration interaction. The vibra-

tional spectrum was calculated by solving the Hessian

matrix formed by the second derivatives of the energy

with respect to atomic Cartesian coordinates by using

the PM3/TM method (Seeger et al., 1991).

Every proposed molecular model for FA should be

taken as the representation of a small portion of the real

mix (Bruccoleri et al., 2001). Since this mix exhibits a

near continuum of exemplars of the typical organic

functional groups it is possible to perform a Gaussian

distribution around every theoretical absorption line,

convoluting the obtained peaks to get a theoretical

spectrum with a continuous absorption in a similar

way as is obtained in an experimental spectrum. The

theoretical electronic and vibrational spectra bands

were built by entering the position and intensity of

the most relevant absorption lines in the Gaussian

distribution equation

y ¼ a0e�1

2

�x�a1a2

�2h i

where a0, a1 and a2 are the intensity, position and band

broadness, respectively; a0 and a1 were entered while

a2 was allowed to vary until it formed a continuous

spectrum due to band overlapping. This convolution

procedure was carried out using a Fourier algorithm

with the PeakFit software (SSPS, 1995) for analysis

and deconvolution.

2.3. Molecular modelling of the FA in aqueous

solution and its aggregation process

For molecular modelling in an aqueous media, a

91,125-nm3 cubic box was built with approximately

2950 TIP3P water molecules (Jorgensen et al., 1983),

enough to avoid the severe edge effects. The geometry

was optimized by using the OPLS force field with

convergence limit of 0.42 kJ mol�1 nm�1. Simulated

annealing (SA) cycles from 700 K to 298 K with a step

size of 1 fs, and a new geometry optimization were

performed on the optimized structure. Aggregation was

studied through the docking of two, four and eight

molecules with the same ionic state, applying SA and

constant temperature molecular dynamics simulations

at 298 K.

3. Results and discussion

3.1. FA structure as a function of ionic state

Fig. 2 shows the results obtained for the FA struc-

ture, simulating its behaviour at a pH ranging from

3.41 to 941 in accordance with the end points

obtained from the titration curve, by geometry opti-

mization with the PM3/TM method (Kubicki, 2000).

The conformation for the protonated FA (V) folds

itself over, maximising Van der Waals, electrostatic

and H-bonding energetic terms. The H-bonds show an

Table 3

Calculated properties for FA model in vacuum by the SE method

PM3/TM as a function of the ionic state

V VI VII

Charge 0 6 10

DHf (kJ mol�1) �5.05d 103 �4.04d 103 �7.43d 102

Gradient

(kJ mol�1 nm�1)

1.55 1.94 2.15

l (D) 8.12 16.8 19.9


extraordinary structural flexibility and variability, act-

ing in a similar way as in Schulten and Leinweber

(2000). As the carboxylic (VI) and phenolic groups

(VII) become ionized, the molecule tends to expand

because of the electrostatic repulsion generated by the

charge increment. These results agree with the con-

clusions obtained from the application of the fractal

theory to the aggregation study (Senesi, 1999), since

the charge rises as the ionization increases and, in

consequence, the inter- and intra-molecular repulsion

increases.

Some of the calculated properties for the FA

model as a function of ionic state are shown in

Table 3. The formation enthalpy, DHf, gives informa-tion about the conformational stability in the model-

ling conditions. An increase in this parameter with

the ionic state means that the deprotonation global

process is endothermic. The dipolar moment, l, andmolecule polarity are related. As the ionic state

increases, the charge increases, and so does the dipo-

lar moment. This increase means that the conforma-

tion has a greater tendency to solvate when it is

dispersed in a polar solvent. In the case of FA,

which is in continuous contact with aqueous solu-

tions in the environment, the dipolar moment increase

involves a solubility increase or a colloidal stabiliza-

tion in solution.

3.2. Computational model validation

The electronic spectrum simulation of the structure

(V) yielded 26 theoretical absorption lines, where

only 8 of them had relevant absorption intensity.

The theoretical peaks, obtained from Gaussian dis-

Table 2

Energy and gradient values obtained for the molecular modelling of

the FA

Structure I II III IV

Ebonda 6.53d 104 4.04d 102 7.24 8.99

Eanglea 7.66d 103 79.1 33.1 35.4

Edihedrala 66.1 28.4 195 178

EVan der Waalsa 4.18d 1021 3.41d 107 �29.3 �63.6

Eelectrostatica 0 0 33.9 �14.8

ETotala 4.18d 1021 3.41d 107 240 144

Gradientb 8.91d 1023 3.26d 108 0.042 0.042

a kJ mol�1.b kJ mol�1 nm�1.

tribution over each of the theoretical lines and the

spectrum obtained by peak convolution show close

agreement with the experimental spectrum (Fig. 3a).

The experimental spectrum has a maximum at 231

nm, which is located at 249 nm in the theoretical

spectrum. In the region between 360 and 900 nm,

spectra differ slightly, probably due to the presence of

other atomic groups that have not been considered in

this model.

The vibrational spectrum simulation of structure

(V) resulted in 152 theoretical absorption lines,

where 47 of them had relevant absorption intensity.

Absorption at negative wavenumbers was not found,

confirming the energetic minimum of (V) (Kubicki et

al., 1999). Fig. 3b shows the experimental and theo-

retical spectra and the peaks found by the Gaussian

distribution noted over each absorption line. The

values calculated by the PM3/TM method show a

close correspondence to the experimental values.

The error percentage in the wavenumber of every

band does not exceed 10% in any case. The method

predicts O–H and N–H stretches in the interval from

3157 to 3954 cm�1. The C–H stretches of aromatic

carbons and symmetric and asymmetric aliphatic car-

bons have higher wavenumbers: 3096, 3035 and 2975

cm�1, respectively. The two bands at 2719 and 2521

cm�1 are due to the O–H stretches for H-bonds,

which agrees with Davies et al. (1997). The band at

2418 cm�1 is due to the N–H deformations of the

amine group. The carbonyl bands, from acidic groups,

esters, and ketones, and those of aromatic and alkene

C–H deformation, are shifted about 150–200 cm�1,

which is in line with the usual error of the method.

The band at 1408 cm�1 is caused by the C–O defor-

mations, which are experimentally located at approxi-

mately 1200 cm�1. From this point onward, there is a

multitude of low intensity bands caused by bending

and rotational modes of the molecule.

Fig. 3. Experimental and theoretical spectra and theoretical bands calculated using a Gaussian algorithm around the theoretical absorption lines,

(a) electronic and (b) vibrational spectra.


Table 4

Energy and gradient values obtained for the FA model in vacuum and solution as a function of the ionic state

COOH/OH COO�/OH COO�/O�

Solution Vacuum Solution Vacuum Solution Vacuum

Ebonda 7.11 8.996 13.43 28.58 20.59 52.26

Eanglea 36.32 35.44 72.51 79.20 86.44 105.9

Edihedrala 220.0 177.8 197.7 184.5 205.5 201.9

EVan der Waalsa 93.01 �63.60 49.62 52.84 162.8 34.60

Eelectrostatica �837.5 �127.6 �1,961 1,758 �2,466 3848

ETotala �481.1 31.06 �1,628 2,103 �1,990 4243

Gradientb 0.042 0.042 0.042 0.042 0.042 0.042

H2Oc 14 – 32 – 48 –

a kJ mol�1.b kJ mol�1 nm�1.c Number of H2O molecules in the 1st hydration sphere.


3.3. Modelling in solution

The properties shown in Table 4 are determined by

a single point calculation with OPLS on the models

Fig. 4. (a) Aggregation process in vacuum for non-ionized FA. Variation

molecules for: (b) non-ionized FA and (c) with the carboxylic groups ion

obtained by simulation in vacuum and in solution.

The water retention in the first hydration sphere

increases with the ionic state in the FA. This increase

in electrostatic retention of water involves a change in

of the potential energy as a function of the distance between the

ized and with the carboxylic and phenolic groups ionized.

Fig. 5. Aggregation in aqueous solution for a system containing (a) two; (b) four; (c) eight molecules after 12.34 ps of constant temperature MD

simulation; and, (d) eight molecules after 83.5 ps of constant temperature MD simulation.



the molecule conformation and more negative poten-

tial energy values. The variation of the total energy

values shows that the presence of water molecules has

a great stabilization effect on the conformations due to

the great decrease in electrostatic energy, which

becomes more important as the ionic state rises. The

conformation stability in aqueous solution increases

as the ionic state rises, which is in line with the

determined values for the dipolar moment (Table 3).

3.4. Aggregation of FA in vacuum and solution

Fig. 4 shows the initial and final systems for two

FA molecules placed in vacuum at a distance of 1.600

nm without ionization. The non-ionized FA

approached to form an aggregate (Fig. 4a). Stabiliza-

tion is due to the interaction of both molecules by

weak-bonding forces and the formation of four inter-

molecular H-bonds, which makes the system much

more stable when it is aggregated than when it is

separated. The variation of the potential energy, V,

with respect to distance was tracked during the SA

cycles. For the non-ionized FA (Fig. 4b), the decrease

in potential energy, V, can be divided into 5 seg-

ments: (i) from 1.6 to 1.5 nm, the curve shows a

steep slope as a result of the increase in electrostatic

attraction as the distance decreases; (ii) between 1.5

and 1.2 nm, the decrease of the slope shows an

endothermic conformational change in both mole-

cules as a result of their approach; (iii) from 1.2 to

0.95 nm, the curve increases its slope again due to

the electrostatic attraction; (iv) from 0.95 to 0.52 nm,

the slope decreases as a result of a new conforma-

tional change; and, (v) from 0.52 to 0.22 nm, the

slope increases due to the formation of intermolecu-

lar H-bonds. The ionized FA shows a wide separa-

tion because of the great charge they support. The

separation increases with the charge values and the

potential energy decreases as the distance between

the molecules increases due to the decrease of repul-

sive electrostatic interaction (Fig. 4c).

The simulation of the aggregation process in solu-

tion is shown in Fig. 5. The system containing two

FA molecules (an equivalent concentration in FA of

0.035 M) did not lead to any aggregation process

(Fig. 5a). Both molecules showed conformational

changes in order to increase their electrostatic inter-

action with water molecules. When the FA concen-

tration was increased to 0.070 M (4 FA Molecules)

the aggregation process began (Fig. 5b). After 19.21

ps of constant temperature molecular dynamics simu-

lation, three of the four molecules of FA came closer

so as to form a single particle where the different FA

molecules were linked by means of intermolecular

H-bonds (Fig. 5c). Although the simulation contin-

ued until 40 ps, one of the four molecules of FA

remained stable in solution. The behaviour of the

system containing eight molecules of FA (an equiva-

lent concentration of 0.140 M) was similar. At a

simulation time of 12.34 ps, two particles composed

by two and three FA molecules were formed. This

system progressed and gave rise to a compact parti-

cle formed by five FA at a time of 83.49 ps of

simulation (Fig. 5d). Nevertheless, although the

simulation continued until 150 ps, three molecules

of FA remained stable in solution. Systems contain-

ing the same equivalent concentration of ionized FA

did not lead to any aggregation processes.

The behaviour of these systems shows that in

solution the FA concentration is a critical factor for

the aggregation. The system containing two FA mole-

cules probably did not form aggregates because its

equivalent concentration was too low (Wershaw,

1999). When the concentration was increased, the

system gave rise to the formation of aggregates.

Nevertheless, in all the systems there were stable FA

molecules in solution. This can be explained by the

aggregation of some of the system molecules decreas-

ing the effective concentration of FA in the solution to

levels lower than the minimum concentration neces-

sary to give rise to an aggregation process. The ionic

state is another critical factor in the aggregation pro-

cess. The ionized FA has higher electric negative

charge, which increases the energetic barriers and

therefore inhibits the approximation of FA caused by

the Brownian movement (Buffle and Leppard, 1995).

4. Conclusions

The proposed theoretical model based on TNB

monomer fits in well with some properties of FA:

solubility (dipolar moment) and electronic and vibra-

tional spectra. The presence of water molecules has a

great stabilization effect on the electrostatic energy.

This effect is greater as ionic state increases. The ionic


state is one of the most important factors in the

aggregation process in vacuum. The non-ionized

aggregated species are more stable than single ones

because of the increment in their interaction due to H-

bonding and non-bonding forces. In solution, the FA

concentration is a critical factor for the aggregation.

The system containing two FA molecules probably

did not form aggregates because its equivalent con-

centration was too low (0.035 M). When the concen-

tration was increased (0.070 M), the system gave rise

to the formation of aggregates. Nevertheless, in all the

systems there were stable FA molecules in solution

because the aggregation of some of the system mole-

cules decreases the effective concentration of FA to

levels lower than the minimum concentration needed

to give rise to an aggregation process. The ionic state

(i.e. pH) is another critical factor in the aggregation

process. The ionized FA has higher electric negative

charge, which increases the energetic barriers and

inhibits the approximation of FA caused by the Brow-

nian movement.

In summary, the power of molecular simulations to

link experimental data with detailed chemical inter-

pretation could provide a useful insight into the beha-

viour of HS and their interactions with nutrients and

contaminants in the environment.

References

Alvarez-Puebla RA, Valenzuela-Calahorro C, Garrido JJ. Retention

of Co(II), Ni(II) and Cu(II) on a purified brown humic acid

Modeling and characterization of the sorption process. Lang-

muir 2004a;20:3657–64.

Alvarez-Puebla RA, Garrido JJ, Aroca RF. Surface-enhanced vibra-

tional microspectroscopy of fulvic acid micelles. Anal Chem

2004b;76:7118–25.

Balbuena PB, Seminario JM, 1999. Molecular dynamics.

Amsterdam7 Elsevier; 1999.

Buffle J, Leppard GG. Characterization of aquatic colloids and

macromolecules: 1. Structure and behaviour of colloidal mate-

rial. Environ Sci Technol 1995;29:2169–75.

Bruccoleri AG, Sorenson BT, Langford CH. Molecular modeling of

humic substances. In: Gabbour EA, Davies G, editors. Humic

substances: structures, models and functions. Cambridge7 The

Royal Society of Chemistry; 2001. p. 193–208.

Chen Y, Senesi N, Schnitzer M. Information provided on humic

substances by E4/E6 ratios. Soil Sci Soc Am J 1977;41:258–352.

Chin Y-P, Aiken G, O’Loughlin E. Molecular weight, polydisper-

sity, and spectroscopic properties of aquatic humic substances.

Environ Sci Technol 1994;28:1853–8.

Chin WC, Orellana MV, Verdugo P. Spontaneous assembly of main

dissolved organic matter in polymer gels. Nature 1998;391:

568–71.

Davies G, Fataftah A, Cherkasskiy A, Ghabbour EA, Radwan A,

Jansen SA, et al. Tight metal binding by humic acids and its

role in biomineralization. J Chem Soc Dalton Trans 1997;21:

4047–60.

Hypercube. HyperChem 7.01. Hypercube Inc., Gainsville; 2002a.

Hypercube. HyperChem Computational Chemistry. Hypercube Inc.,

Waterloo; 2002b.

Jones ML, Bryan ND. Colloidal properties of humic substances.

Adv Colloid Interface Sci 1998;78:1–48.

Jorgensen WL, Tirado-Rives J. The OPLS potential functions for

proteins Energy minimizations for crystals of cyclic peptides

and crambin. J Am Chem Soc 1988;110:1657–66.

Jorgensen WL, Chandrasekhar JD, Madura JD, Impey RW, Klein

ML. Comparison of simple potential functions for simulated

liquid waters. J Chem Phys 1983;79:729–37.

Kubicki JD. Molecular modeling of humic and fulvic acid. Prepr

Ext Abstr ACS Natl Meet, Am Chem Soc Div Environ Chem;

2000. p. 617–9.

Kubicki JD, Apitz SE. Models of natural organic matter and

interactions with organic contaminants. Org Geochem 1999;

30:911–27.

Kubicki JD, Sykes D, Apitz SE. Ab initio calculation of aqueous

aluminium and aluminium–carboxilate complex energetics

and 27Al NMR chemical shifts. J Phys Chem A 1999;103:

903–15.

Manning TJ, Bennett T, Milton D. Aggregation studies of humic

acid using multiangle laser light scattering. Sci Total Environ

2000;257:171–6.

Merchan M, Serrano-Andres L, Slater LS, Roos BO, McDiarmid R,

Xing X. Theoretical spectroscopy of organic compounds. J Mol

Struct Theochem 1998;463:201–10.

Myneni SCB, Brown JT, Martinez GA, Meyer-Ilse W. Imaging of

humic substance macromolecular structures in water and soils.

Science 1999;286:1335–7.

Niemeyer J, Chen Y, Bollag JM. Characterization of humic acids,

composts and peat by DRIFT. Soil Sci Soc Am J 1992;56:

135–40.

Schulten HR. The three-dimensional structure of humic substances

and soil organic matter studied by computational analytical

chemistry. Fresenius J Anal Chem 1995a;351:62–73.

Schulten HR. The three-dimensional structure of soil organo-

mineral complexes studied by analytical pyrolisis. J Anal

Appl Pyrolysis 1995b;32:111–26.

Schulten HR. Molecular modeling of humic substances, soil organic

matter and soil particles: potential and limits. Mineral Mag

1998;62A:1358–9.

Schulten H-R. Interactions of dissolved organic matter with xeno-

biotic compounds: molecular modeling in water. Environ Tox-

icol Chem 1999;18:1643–55.

Schulten HR, Leinweber P. New insights into organic–mineral

particles: composition, properties, and models of molecular

structure. Biol Fertil Soils 2000;30:399–432.

Schulten HR, Schnitzer M. Chemical model structures for soil

organic matter and soils. Soil Sci 1997;162:115–30.


Seeger DM, Korzeniewski C, Kowalchyk W. J Phys Chem 1991;

95:68–71.

Sein LN, Varnum JM, Jansen SA. Conformational modeling of new

building block of humic acid: approaches to the lowest energy

conformer. Environ Sci Technol 1999;33:546–52.

Senesi N. Aggregation patterns and macromolecular morphology

of humic substances: a fractal approach. Soil Sci 1999;164:

841–56.

Senesi N, Rizzi FR, Dellino P, Acquafredda P. Fractal humic acids

in aqueous suspensions at various concentrations, ionic

strengths and pH values. Colloids Surf A 1997;127:57–68.

Shevchenko SM, Bailey GW, Akim LG. The conformational

dynamics of humic polyanions in model organic and organo-

mineral aggregates. J Mol Struct 1998;460:179–90.

SSPS. PeakFit 4.05. SSPS Inc., Chicago; 1995.

Humus chemistry: genesis, composition and reactions. In: Stevenson

FJ, editor. New York7 John Wiley & Sons; 1982.

Swift RS. Organic matter characterization. In: Sparks D.L., editor.

Method of soil science analysis: chemical methods Part 3.

Madison7 Soil Science Society of America; 1996. p. 1011–69.

Swift RS. Macromolecular properties of soil humic substances: fact,

fiction and opinion. Soil Sci 1999;164:790–802.

Tombacz E. Colloidal properties of humic acids and spontaneous

changes of their colloidal state under variable solution condi-

tions. Soil Sci 1999;164:814–24.

Wershaw RL. Molecular aggregation of humic substances. Soil Sci

1999;164:803–11.

Young D. Computational chemistry. New York7 Wiley-Interscience;

2001.

Zerner M. Semi-empirical molecular orbital methods. In: Lipkowitz

D.B., Boyd D.B., editors. Reviews of computational chemistry.

New York7 VCH Publishing; 1991. p. 313–66.

Theoretical study on fulvic acid structure, conformation and aggregationIntroductionExperimentalExtraction, purification and characterization of the FAMolecular modelling of the FA in vacuum conditionsMolecular modelling of the FA in aqueous solution and its aggregation process

Results and discussionFA structure as a function of ionic stateComputational model validationModelling in solutionAggregation of FA in vacuum and solution

ConclusionsReferences

Theoretical study on fulvic acid structure, conformation and … - 030217... · 2016. 5. 20. ·...

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