Ž .Sensors and Actuators B 60 1999 168–173www.elsevier.nlrlocatersensorb

Concentration determination of gas by organic thin film sensor and backpropagation network

J.C. Chen, Y.H. Ju ), C.J. LiuLaboratory 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 14 September 1998; received in revised form 14 June 1999; accepted 2 July 1999

Abstract

In this work, vacuum deposited thin films of PbPc, NiPc, VOPc, TiOPc and CoPc were employed as gas sensor to detect NO and2Ž .NO. Data collected from sensor responses were used to train a back-propagation network BPN for identifying the gas species and

quantifying its concentration. The results show that among the metallophthalocyanines tested, PbPc and NiPc have better sensingcharacteristics towards NO and NO. In BPN training, maximum error occurs for data collected by the TiOPc sensor, and minimum error2

occurs for array of PbPc and NiPc sensors. In the concentration prediction of NO or NO , the maximum predicted error is 6.94%. When2Ž .Two-Stage BPN or Single-Stage BPN was use to identify and quantify a single gas NO or NO , the accuracy of recognition approaches2

100% and the maximum error for concentration prediction is 7.45%. q 1999 Elsevier Science S.A. All rights reserved.

Keywords: Organic thin film sensor; Back-propagation network; NO; NO2

1. Introduction

The task for a gas sensing system used in ventilationcontrol or alarm system is to signal the occurrence of oneor more specified gases. Semiconductor sensors were usedin many applications due to their low prices, their robust-ness and simple measurement electronics. The basic gasdetection principles are well known, but the phenomenonis not understood in all facets. The principal detectionprocess is the change of gas concentration at the surface ofa metal oxide or organic such as metallophthalocyanineŽ .MPc , caused by the adsorption and heterogeneous cat-

w xalytic reaction of oxidizing and reducing gases 1 . Manyworks have been conducted on the study of metal oxide ororganic semi-conductor in response to oxidizing or reduc-

w xing gases 2 . The simultaneous identification of gas speciesand determination of its composition in a gas mixture is achallenging task. Based on data collected from sensorresponses to gases and pattern recognition techniques such

Ž .as artificial neural network ANN , partial successes havebeen achieved in the identification and quantification ofgas mixtures.

) Corresponding author. Tel.: q886-2-27376612; fax: q886-2-2737-6644; e-mail: ju@ch.ntust.edu.tw

w xWang et al. 3 used ANN and sensing elements that areconstructed using single layer and bilayer of reactivelysputtered thin films of SnO , ZnO, TiO , and WO with2 2 3

and without Pd catalyst to analyze mixtures of methanoland acetone. They found that the system has better recog-

w xnition result toward methanol. Sundgren et al. 4 foundthat when signals from sensors were analyzed with con-ventional multivariate analyses, partial least squares, andANN models, the results show that the two-componentmixture of hydrogen and acetone was best predicted from

w xan ANN model. Barker et al. 5 fabricated thin filmsensors based on the thermal evaporation and dip-coatingof polyaniline, and on the Langmuir–Blodgett depositionof a vanadium porphyrin. The DC components of theresponse current of the individual elements were found toexhibit different changes on exposure to simple vaporsŽ .water, propane, ethyl acetate and acetone . These datawere successfully used to train an ANN, based on a

Ž .back-propagation technique BPN , to recognize two of theŽ . w xvapors water and acetone . Debeda et al. 6 constructed a

Ž .small array of four microcalorimetric sensors pellistorsŽ .with various percentages of palladium Pd and platinum

Ž .Pt . The pellistors were exposed to various gases ofmethane, propane and ethanol vapors at different concen-trations in order to recognize the three combustible gases.

0925-4005r99r$ - see front matter q 1999 Elsevier Science S.A. All rights reserved.Ž .PII: S0925-4005 99 00267-1

( )J.C. Chen et al.rSensors and Actuators B 60 1999 168–173 169

As Pd and Pt have different catalytic activity at 4008C, thew xpellistors exhibit different responses. Hong et al. 7 fabri-

cated a gas recognition system using a gas sensor arrayand neural-network pattern recognition in order to identify

Ž .CH SH, CH H, C H OH, and CO gases in the concen-3 3 3 2 5

tration range of 0.1 to 100 ppm. Principal componentanalysis and neural-network pattern recognition techniquewere used for the discrimination of gas species and con-centrations. The recognition probability of the neural-net-work was 100% for each five trials of 12 gas samples.

w xKato et al. 8 proposed a new gas sensor, which obtainsinformation from a non-linear time-dependent response.They also described an appropriate method for processingdata from the time-dependent response collected within acertain temperature range.

Few works on the discrimination of gases’ species andconcentrations employed sensors fabricated from organicmaterial, especially MPc. In this work, we used a numberof MPc thin film gas sensors to detect NO and NO. Data2

collected were used to train a BPN for the discriminationof gas species and the determination of its concentrations.

2. Experimental

Ž .Lead phthalocyanine PbPc , nickel phthalocyanineŽ . Ž .NiPc , titanyl phthalocyanine TiOPc , and cobalt

Ž .phthalocyanine CoPc with a purity of 80%, 85%, 95%and 97%, respectively, were purchased from Aldrich

Ž .Chemical. Vanadyl phthalocyanine VOPc with a purityof 90% was provided by Acros Chemical. MPc was subli-mation purified twice in a vacuum at ca. 20 mTorr and

4508C. Purified powders were then vaporized and con-densed onto substrates in a diffusion-pumped vacuum sys-tem operating at a base pressure of 2=10y5 Torr. Sub-strates were 48.8 mm=48.8 mm=1 mm alumina plateswith screen-printed gold integrated electrodes, and werekept at ambient temperature during deposition. Typical

˚deposition rate was 1 Ars, and typical thickness was 1000A as measured with an oscillating quartz–crystal thin film

Ž .monitor ULVAC Japan, model CRTM-5000 .The resultant sensor was put in a Pyrex glass test

chamber that was immersed in an oil bath. The tempera-ture of the oil bath was maintained with a PID temperature

Ž .controller Eurotherm model 808 . Typical operating tem-peratures are, 1708C for PbPc, CoPc and NiPc, 1808C forTiOPc, and 1908C for VOPc, as optimized from previous

w xexperiments 9,10 . NO or NO gas diluted with high2

purity N passed through the test chamber at a flow rate of2

500 mlrmin, controlled by a Sierra mass flow controllerŽ .model 840 . Gas entering the chamber passed directlyover the sensor surface. The distance from the gas inlet tothe surface of the sensor was approximately 6 mm. Thedesorption cycle was performed by flushing the systemwith pure N gas. Fig. 1 gives the schematic description of2

the experimental setup.The DC conductivity of a sensor was measured by a

Keithley 236 multimeter with a source voltage of 10 V. AnIBM microcomputer was used to record the time profile ofDC current flowing through the sensor in response to theadsorption of NO or NO. In a typical run, nitrogen gas2

was firstly introduced into the test chamber at a flow rateof 500 mlrmin for at least 30 min. Then the sensor wasexposed to NO or NO for 10 min. Finally, the chamber2

Ž . Ž . Ž . Ž . Ž . Ž .Fig. 1. Schematic diagram of experimental setup. 1 mass flow meter, 2 N cylinder, 3 NO cylinder, 4 NO cylinder, 5 control valve, 6 test2 2Ž . Ž . Ž .chamber, 7 Keithley 236, 8 computer, 9 gas mixer.

( )J.C. Chen et al.rSensors and Actuators B 60 1999 168–173170

Fig. 2. Topology for the neural network with one hidden layer.

was flushed with pure nitrogen until complete desorptionwas assured.

The development of a neural network for the evaluationŽ .task consists of two parts: 1 The determination of net-

Žwork structure and network size the number of input,hidden and output neurons, and the interconnection struc-

. Ž .ture . 2 The training of the network, i.e., finding theoptimum interconnection weights between neurons.

Since it has been proved that BPN needs only onehidden layer to perform learning satisfactorily for most

w xfunctions 11,12 , a three-layer neural network was em-

ployed in this work. Fig. 2 shows the topology of athree-layer BPN. Details of the BPN and the related train-

w xing algorithms can be found elsewhere 13 . As shown inFig. 2, the input variables from the sensor, x , are multi-l

Žplied by a matrix of parameters W ls1,2, . . . , L; mslm.1,2, . . . , M , where L and M are the number of input units

and the number of the hidden neurons, respectively. Simi-larly the signals in the hidden layer are multiplied by

Ž .another matrix of parameters W ns1,2, . . . , N , wherem n

N is the number of output units. The output value can beexpressed as:

M L

y s W f W xÝ Ýn m n lm lms0 ls0

Ž w yx x.where f is a sigmoid function s1r 1qe in thiswork. The training of network aims at minimizing the errorfunction E, defined to be the sum of the squares of theerrors between the output y of the network and then

corresponding target value ta, summed over all the pat-terns:

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

˚Fig. 3. Typical response curves for a 1000 A thick PbPc film on exposure to 70 ppm NO gas.2

( )J.C. Chen et al.rSensors and Actuators B 60 1999 168–173 171

Fig. 4. Response curves of a NiPc sensor to various concentrations of NO gas. Deposition condition: substrate temperature at 258C, deposition rate at 12˚ ˚Ars, thicknesss1000 A. Detection parameter: gas flow rates500 mlrmin, operating temperature at 1708C.

the hidden layer is a variable that must be determined bytrial and error. The number of neurons in output layer isequal to the number of gas species, which is 1 in thiswork. The value of this output neuron equals to the gasconcentration in ppm. Back propagation technique is thebasis for the 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 as

input 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.

3. Results and discussion

To characterize the sensor behavior, its response to astep change in gas concentration was measured. Fig. 3

Fig. 5. Response curves of a NiPc sensor to various concentrations of NO gas. Deposition condition: substrate temperature at 258C, deposition rate at 1˚ ˚Ars, thicknesss1000 A. Detection parameter: gas flow rates500 mlrmin, operating temperature at 1708C.

( )J.C. Chen et al.rSensors and Actuators B 60 1999 168–173172

˚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. OtherMPc films exhibit similar responses towards NO in terms2

of steady state current, response time and reproducibility.However, in sensing NO, only PbPc and NiPc yield satis-factory responses.

3.1. Determination of the optimum network structure

The sensor was subjected to continuous exposure toNO or NO for 10 min. Response curves of a NiPc sensor2

to various concentrations of NO and NO gases are shown2

in Figs. 4 and 5, respectively. Response curves of PbPcsensors behave similarly. Usually the sensitivity and theresponse speed of MPc to NO gas is lower than that ofNO gas as shown in Figs. 4 and 5. Twenty equal spaced2

data points from a section of the response curve between300 and 600 s were selected and normalized with respectto the maximum response current. These data points wereused as input for training the BPN.

w xThe initial weights and thresholds were set in y1,1 .The optimum network structure and parameters of a three-layer BPN for the prediction of NO or NO concentration2

are shown in Table 1. Only the numbers of neurons in thehidden layer are shown since the numbers of input andoutput neurons were set at 20 and 1, respectively. Table 2shows the training errors and the predicted errors of MPcthin film gas sensors for the prediction of NO and NO2

concentrations. Error is defined as

< <EÝ iierror % s =100Ž .N

Žwhere N is the number of training data or prediction. Ž .results , and E is the ith trained prediction error. Fromi

Table 2, it is clear that array of PbPc and NiPc has the besttraining results, and the prediction error for the array isbetween those of PbPc and NiPc. It seems that betterresults are obtained as more information is fed into theBPN. Either single or array of MPc gives satisfactory toexcellent prediction in NO or NO concentration, if the2

Table 1Optimum network structure and parameters for the prediction of NO or2

NO concentration

Sensor Number of Momentum Learninga a aŽ . Ž .neurons factor a rate h

PbPc 3r3 0.5r0.5 9.0r9.0NiPc 17r3 0.5r0.5 7.0r5.0TiOPc 1ry 0.5ry 9.0ryVOPc 1ry 0.5ry 9.0ryCoPc 3ry 0.5ry 9.0ryArray 3r3 0.5r0.5 9.0r7.0

aArB, A: NO , B: NO.2

Table 2Training errors and prediction errors of the MPc thin film gas sensors forthe prediction of NO or NO concentration2

a aŽ . Ž .Sensor Training error % Predicted error %

PbPc 0.59r1.61 5.96r4.36NiPc 1.22r1.97 1.83r2.44TiOPc 10.15ry 4.41ryVOPc 0.05ry 3.88ryCoPc 1.92ry 6.94ryArray 0.00r0.00 3.48r2.54

aArB, A: NO , B: NO.2

sensor is able to yield satisfactory response in encounter-ing the gas. The maximum prediction error shown in Table2 is 6.94%.

3.2. Identifying gas species and quantifying its concentra-tion

In Section 3.1, MPc thin film sensor coupled with BPNwas employed for the determination of concentration of a

Ž .specified gas NO or NO . Ideally, the system should be2

able to discriminate both the gas species and concentrationin a mixture of gases. Currently, this can be partially

Žachieved by using sensor array mostly metal oxide semi-.conductors coupled with pattern recognition techniques.

In this section, the BPN developed in Section 3.1 will bemodified for simultaneously identifying an unknown gas

Ž .species NO or NO and determining its concentration.2

PbPc, NiPc or array of PbPc and NiPc sensors was usedŽfor the detection of an unknown gas which is NO or NO2

.in this work with unknown concentration. Data for thetraining of BPN were obtained as described before. BothTwo-Stage BPN and Single-Stage BPN were used for theidentifying of gas species and the quantifying of its con-centration.

In the 1st part of the Two-Stage BPN, the networkrecognizes the gas species, and in the 2nd part it quantifiesthe concentration of the gas. The output vector of the 1ststage has two components. One corresponds to the un-known gas species and its value is set at 1. The othercomponent has a value of 0, which corresponds to the gasthat is absent. The output of the 1st stage with known gasspecies and unknown gas concentration is used as input forthe 2nd stage. The determination of gas concentration of aspecified gas in the 2nd stage is the same as that described

Table 3Optimum network structure and parameters for the 1st stage of theTwo-Stage BPN

Sensor Number of Momentum LearningŽ . Ž .neurons factor a rate h

PbPc 5 0.5 7.0NiPc 11 0.5 9.0Array 11 0.5 9.0

( )J.C. Chen et al.rSensors and Actuators B 60 1999 168–173 173

Table 4Optimum network structure and parameters for the Single-Stage BPN aswell as errors for the prediction of concentrations

Sensor Number of Momentum Learning PredictionaŽ . Ž . Ž .neurons factor a rate h error %

PbPc 11 0.5 9.0 5.04r3.32NiPc 11 0.5 7.0 4.42r2.79Array 7 0.5 5.0 1.67r4.89

aArB, A: NO , B: NO.2

in Section 3.1. Table 3 shows the optimum network struc-ture and parameters for the 1st stage of the Two-StageBPN. The numbers of input and output neurons were set at20 and 2, respectively. The accuracy of gas species recog-nition approaches 100%. In concentration determination,the Two-Stage BPN performs better for NO than NO .2

The Single-Stage BPN identifies the category of anunknown single gas and predicts its concentration simulta-neously. The output vector has two components. Onecomponent has a value that is related to the concentrationof the existing gas; the other component has a value of 0that corresponds to the gas that is absent. Table 4 showsthe optimum network structure and parameters for theSingle-Stage BPN as well as errors for the prediction ofconcentrations. The numbers of input and output neuronswere set at 20 and 2, respectively. The accuracy of speciesidentification is 100% and is not shown in Table 4. Sensorarray of PbPc and NiPc gives the best predicted result forNO . The maximum prediction error for any MPc-Single-2

Stage BPN is 7.45%.The possibility of applying the Single-Stage BPN and

the Two-Stage BPN for the identification and quantifica-tion of a gas mixture is currently under investigation in ourlaboratory.

4. Conclusions

An investigation into the design of a gas sensing sys-tem, which consists of a vacuum deposited MPc thin filmsensor and BPN for the recognition of gas concentration ofNO and NO has been undertaken. Preliminary results2

show that the system can be successfully used to recognizethe species and predict its concentration for NO and NO.2

These results are encouraging, in the sense that onlysimple experimental arrangement was involved and despiteminor problems associated with the reproducibility of theindividual sensing elements. Future works will be focusedon the improvement of the measurement system and thedevelopment of more reliable sensing elements. The explo-ration on the use of other pattern recognition techniquesfor the purpose of discrimination of species and determina-tion of its concentrations in a gas mixture will also beincluded.

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.

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.

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.