Ž .Sensors and Actuators B 62 2000 143–147www.elsevier.nlrlocatersensorb

Determination of the composition of NO and NO mixture by thin film2

sensor and back-propagation network

J.C. Chen, C.J. Liu, Y.H. Ju )

Laboratory for Thin Film Sensor Research, Department of Chemical Engineering, National Taiwan UniÕersity of Science and Technology, 43 Keelung Rd.,Sec. 4, Taipei 106-07, Taiwan

Received 27 August 1999; received in revised form 25 October 1999; accepted 25 October 1999

Abstract

Ž .In this work, vacuum deposited thin films of lead phthalocyanine PbPc were employed as gas sensor to detect mixtures of NO and2Ž .NO. Data collected from sensor responses were used to train a two-stage back-propagation network BPN for the determination of the

gas mixture composition. The success rate for the two-stage BPN in identifying categories of gas mixture approaches 100%. Themaximum error for the two-stage BPN in predicting NO concentration in a gas mixture with fixed NO concentration is 14.7%. q 20002

Elsevier Science S.A. All rights reserved.

Keywords: Organic thin film; Gas sensor; Back-propagation network; Gas mixture

1. Introduction

The analysis of a mixture of gases represents one of themain objectives of current research in the sensor field. Thetask can be accomplished by adopting highly selectivesensors or using arrays of sensors, which are scarcelyselective, matched with a suitable data-analysis technique.Pattern-recognition techniques, developed in other researchfields, are now widely used in chemical sensing to extractqualitative information on the chemical environment under

w xanalysis from the sensor arrays. Natale et al. 1 used aquartz-microbalance-based sensor array and a hybrid net-work to identify binary mixtures of organic mixtures. Thehybrid network consists of generalized perceptron networkand self-organized map. The binary gas mixture is eithern-octane–chloroform, or n-octane–n-propanol, or chloro-form–n-propanol. A success rate of identification of more

w xthan 80% has been achieved. Endres et al. 2 showed thatthe simultaneous detection of CO and NO using a special-2

ized SnO sensor array is possible in a laboratory environ-2

ment. The combination of different SnO sensors, operated2

at different temperatures and combined with a signal eval-uation system based on a specially trained neural forwardnetwork solved this problem. The runtime version of the

) Corresponding author. Tel.: q886-2-27376612; fax: q886-2-27376644; e-mail: ju@ch.ntust.edu.tw

neural net is a small program, compatible with customarymicrocontrollers. A classification rate of at least 99.5%and a response time less than 20 s of the signal evaluation

w xsystem were achieved. Sundgren et al. 3 reported a gassensor array exposed to two types of multiple-componentgas mixture. The sensor array consists of six metal-oxide-semiconductor-field-effect-transistors. The mixture is hy-drogen, ammonia, ethanol and ethylene in air or hydrogenand acetone in air. Signals from the sensors were analyzedwith both partial least squares and artificial neural networkŽ .ANN models. Their results showed that mixture of hy-drogen and acetone was best predicted by an ANN model.

w xWang et al. 4 presented a multicomponent gas and vaporanalysis system. The system consists of eight nonselectiveand nonlinear Taguchi gas sensors, a temperature sensor,and multivariate calibration algorithms such as modifiedANN. The application of this system to analyze two- andthree-component mixtures of benzene, toluene, acetoneand trichloroethane was demonstrated. The neural net-works performed the predictions with a lowest average

w xroot mean square error of 22.8 ppm. Althainz et al. 5reported that the recognition of different air contaminantswas achieved by pattern recognition of the signals ob-tained, using temperature gradients and inhomogeneousceramic membranes to tune the selectivity of the sensor

w xelements. Moore et al. 6 carried out an investigation onthe ability of ANN to quantify the concentrations of

0925-4005r00r$ - see front matter q 2000 Elsevier Science S.A. All rights reserved.Ž .PII: S0925-4005 99 00378-0

( )J.C. Chen et al.rSensors and Actuators B 62 2000 143–147144

individual gases and gas mixtures in air from patternsgenerated by an array of chemically modified sinteredSnO sensors. The aim of their study was to design a2

neural paradigm that could compute the concentration ofŽ .three gases H , CH and CO in simple gas mixtures. The2 4

maximum prediction error was 10% for H , CH and CO2 4

gases. It was also possible to quantify H :CH gas mix-2 4

tures to a similar accuracy with no interference effectobserved from humidity changes. The CO concentrationcould also be predicted in H :CH :CO gas mixtures but to2 4

a much lower degree of accuracy. The implementation of afuzzy neural network with an array of tin oxide based gassensors for both qualitative and quantitative gas sensing

w xwere demonstrated by Vlachos and Avaritsiotis 7 . Theypresented experimental results for the detecting of CO,ethanol and methane. Only one gas was present at eachtime and distinction between gases and identification oftheir concentrations have been achieved. The instabilitiesand degradation of signals associated with tin oxide sen-

w xsors were tackled by Vlachos et al. 8 . They showed thatthe effect of sensor degradation may be compensated by ahybrid neural network.

In this work, signals from lead PbPc thin film sensor inresponse to a mixture of NO and NO in nitrogen were2

Ž .processed by a two-stage back-propagation network BPN .The aim of this work is to determine the composition ofthe gas mixture.

2. Experimental

Ž .PbPc Aldrich, 80% was sublimation-purified twice ina vacuum at ca. 20 mTorr and 4508C. Purified powder wasthen vapor-deposited at 2=10y5 Torr system pressure on

Ž .to alumina plate 48.8 mm=48.8 mm=1 mm withscreen-printed gold interdigitated electrodes with 200 mmelectrode spacing. Depositions were carried out under thefollowing conditions: substrate temperature at 258C, depo-

˚ ˚sition rate at 1 Ars and film thickness at 1000 A. Deposi-tion conditions were chosen based on a previous study thatunder these conditions, the film obtained has an amor-phous structure with optimal sensing characteristics to-

w xwards NO 9 . Mixture of NO and NO diluted with high2 2

purity N was passed through the test chamber at a flow2

rate of 500 mlrmin, controlled by a Sierra mass flowŽ .controller Model 840 . The design of the test chamber and

w xgas flow system was described elsewhere 10 . The dcconductivity of the PbPc sensor was measured using aKeithley 236 multimeter with a source voltage of 10 V. AnIBM micro computer was used to record the time profilesof the dc current flowing through the sensor in response toadsorption and desorption of a gas mixture of NO and2

NO.Since it has been proved that BPN needs only one

hidden layer to perform learning satisfactorily for mostw xfunctions 11,12 , a three-layer neural network was

employed in this work. Fig. 1 shows the topology of a

Fig. 1. Schematic diagram of a two-stage BPN with one hidden layer ineach stage.

two-stage BPN with one hidden layer for each stage.Details of the BPN and the related training algorithms can

w xbe found elsewhere 13 . As shown in Fig. 1, the inputvariables from the sensor, x , are multiplied by a matrix ofl

Ž .parameters W ls1, 2, . . . , L; ms1, 2, . . . , M ,lm

where L and M are the number of input units and thenumber of the hidden neurons, respectively. Similarly thesignals in the hidden layer are multiplied by another matrix

Ž .of parameters W ns1, 2, . . . , N , where N is them n

number of output units. The output value can be expressedas:

M L

y s W f W xÝ Ýn m n lm lms0 ls0

Ž w yx x.f is a sigmoid function s1r 1qe in this work. Thetraining of network aims at minimizing the error functionE, defined to be the sum of the squares of the errorsbetween the output y of the network and the correspond-n

ing target value ta, summed over all the patterns:P N

w xEs y y taÝ Ý nps1 ns1

where P is input vector numbers. The principle of steep-est-gradient-descent is applied and the weights W andlm

W are interactively changed to minimize the error func-m n

tion as:

DW r sW r yW ry1 syh EErEW W r qaDW ry1Ž .ji ji ji ji ji ji

where h and a are the learning rate and the momentumfactor, respectively. The optimum number of neurons in

( )J.C. Chen et al.rSensors and Actuators B 62 2000 143–147 145

the hidden layer is a variable that must be determined bytrial and error. Back-propagation technique is the basis forthe algorithm used in this work.

The neural network was trained as follows. All theweights and threshold were set to some small randomvalues. Data obtained from the sensors were presented asinput vectors and the output was specified. The networkwas trained by initially selecting small random weightsand internal thresholds and then adjusted until the speci-fied convergence criteria were met.

In this work, PbPc sensor was subjected to continuousexposure of a mixture of NO and NO for a period of 102

min. Twenty equal spaced data points from a section of theresponse curve between 300 and 600 s were selected andnormalized with respect to the maximum response currentfor the training of BPN.

3. Results and discussion

3.1. Sensor behaÕior in response to a mixture of NO andNO2

To characterize the sensor behavior, its response to astep change in gas concentration was measured. Fig. 2

˚shows typical response curves of a 1000-A thick PbPc filmupon exposure to 70 ppm NO gas. Except for an initial2

decay, the steady-state sensor current remains constantwithin a few percent in repetitive measurements. Typicalresponse curves of a PbPc sensor to various concentrationsof NO and NO mixtures are shown in Fig. 3. Note that at2

the same gas concentration, the response current of PbPcsensor to NO is one order of magnitude larger than that2

of NO. This poses serious obstacle in the effectiveness ofthe PbPc sensor system for the determination of the com-

˚Fig. 2. Typical response curves for a 1000-A thick PbPc film on exposureto 70 ppm NO gas.2

Fig. 3. Typical response curves of a PbPc sensor to mixtures of NO and2ŽNo gas. Deposition condition: substrate temperature at 258C, deposition˚ ˚rate at 1 Ars, thicknesss1000 A. Detection parameter: gas flow rates

.500 mlrmin, operating temperature at 1808C.

position of a NO and NO mixture, as will be discussed2

later.

3.2. Determination of the gas mixture composition

A two-stage BPN, as shown schematically in Fig. 1,was used for the determination of composition of NO andNO mixture in this work. Sensor responses were obtained2

with 6 categories of mixtures of NO and NO in nitrogen2

covering a concentration range from 1 to 100 ppm andfrom 30 to 350 ppm for NO and NO, respectively. Table2

1 shows the six categories of mixtures of NO and NO2Ž .used in this work. In the first identification stage, the

network recognizes the mixture’s category. The outputvector has six components representing the six categoriesof gas mixtures shown in Table 1. Only one of the sixcomponents of the output vector has a value other thanzero, which is set to 1. The position of that componentcorresponds to the category that the gas mixture belongsto. Thus, the numbers of input and output neurons are 20

Table 1Composition of the six categories of gas used in this study

Ž .Category Composition ppm NO :NO2

numbera a1 0:50 0:75 0:100 0:125 0:150 0:175 0:250a2 50:30 50:80 50:120 50:180 50:250 – –a3 30:30 30:80 30:120 30:180 30:250 – –

a4 10:50 10:100 10:200 10:250 – – –a a5 1:50 1:100 1:150 1:200 1:250 0:300 –

a a6 10:0 20:0 30:0 50:0 60:0 80:0 100:0

a Data points used for the determination of the success rate of thenetwork. Those without the asterisks were used for the training of thenetwork.

( )J.C. Chen et al.rSensors and Actuators B 62 2000 143–147146

Table 2The optimal network structure and parameters for the quantification stageas well as errors for the prediction of concentration

Mixture No. of neurons in a h Error forcategory hidden layer concentration

Ž .prediction %

1 3 0.5 9.0 5.962 3 0.5 9.0 4.363 3 0.5 9.0 14.424 3 0.5 7.0 14.675 1 0.5 9.0 4.606 1 0.5 5.0 4.67

and 6, respectively. The optimal number of neurons in theŽ .hidden layer, the learning rate h and the momentum

Ž .factor a , as determined from the network training, are 9,0.5 and 0.33, respectively. The success rate for categoryidentification approaches 100%. It should be mentionedthat from the output of the 1st stage, the concentration ofone gas species is known while the other is yet to bedetermined.

Output from the first stage was used as input for theŽ .second quantification stage. In the second stage, the

numbers of input and output neurons were set at 20 and 1,respectively. Since the mixture’s category has been speci-fied in the 1st stage, the concentration of one of the gasspecies in the mixture is automatically identified. Theconcentration of the other species is determined from themagnitude of the output vector of the 2nd stage. Table 2shows the optimal network structure and parameters forthe quantification stage as well as errors for the predictionof gas concentration. From Table 2, it is clear that largeerrors result from mixtures with high concentration ofNO . One possible explanation is that the measured cur-2

rents from PbPc sensor in responses to the same concentra-tions of NO and NO differs by one order of magnitude as2

can be seen from Fig. 3. In categories 2, 3 and 4, the largecontribution in signal caused by the presence of NO2

overshadows the small changes in signal caused by vary-ing NO concentrations. The acquiring of additional datafrom a sensor that yields higher current in respond to NOas compared to NO are recommended in order to obtain2

better training results. The search of a metallo-phthalo-Ž .cyanine MPc or substituted MPc for this purpose is

currently under way in our laboratory. The simultaneousidentification of several species and the determination oftheir concentrations in a gas mixture remain a challengingtask by both academics and industries. We have demon-strated with partial success that it is possible to use asingle organic sensor, which is more selective as comparedto metal oxide sensor, together with BPN in identifyinggas species and their concentration in a gas mixture. Ofcourse, its application is limited by the fact that themixture must belongs to the specified categories. Theoreti-cally, the number of categories can be expanded and anarbitrary mixture can be classified into a certain category

by interpolation. This may require a much larger andsophisticated network structure with its inherent obstacles.

The possibility of using a one-stage BPN to determinethe composition of a mixture of NO and NO has been2

explored. The output vector has two components, corre-sponding to NO and NO with the magnitude of the2

components equal to the individual gas concentration. Nocategorization of mixtures, as required by the two-stageBPN described previously, is necessary. Such a one-stageBPN, if proved successful, would be more practical andsuperior to a two-stage BPN in application. Althoughone-stage BPN has been successfully applied in identifyinggas species and determining its concentration for a single

w xgas 14 , unfortunately, it failed to converge properly in thetraining stage when applied to mixtures of NO and NO .2

The identification of gas species and its concentration in agas mixture is inherently a complicate problem. We be-lieve that more information is helpful for the successfultraining of the network. It was discovered that the responsecharacteristics of a MPc also depend on its operation

w xtemperature 10 . We tried to use data collected from asingle PbPc sensor at various operating temperatures inresponse to gas mixtures to train a single stage BPN.However, improvement in convergence during the trainingof a one-stage BPN was only marginal at this point.

4. Conclusion

We have demonstrated with partial success that thesimultaneous detection of NO and NO using single PbPc2

thin film gas sensor and a two-stage BPN is possible in alaboratory environment. The success rate in identifyingcategory of a gas mixture approaches 100%. The maxi-mum error for the prediction of NO concentration in a gasmixture with fixed, high NO concentration is 14.7%.2

Although the system developed in this work is applicableonly when a gas mixture belongs to the certain specifiedcategories, however, this represents a small step towardsthe successful identification of gas species and determina-tion of its concentration in a gas mixture by using aorganic sensor.

Acknowledgements

The financial support by the grant from the CPC ofŽ .Taiwan NSC87-CPC-E-011-001 is gratefully acknowl-

edged.

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Jan-Chen Chen received his MS degree in Chemical Engineering, Na-tional Taiwan University of Science and Technology in 1998. His re-search interest is gas sensing by organic thin films.

Chin-Hsin J. Liu received his PhD degree in Physical Chemistry fromWashington University at St. Louis, USA. He is currently an AssociateProfessor with the Department of Chemical Engineering, National TaiwanUniversity of Science and Technology. His research interests include gassensor, solar cell, biomaterials and surface analysis.

Yi-Hsu Ju received his PhD degree in Chemical Engineering, Universityof Washington in 1977. He is currently a Professor with the Departmentof Chemical Engineering, National Taiwan University of Science andTechnology. His research interests include transport phenomena, biotech-nology and organic gas sensor.