Polymer coated sensor array based on quartz crystal microbalance for chemical agent analysis

Polymer coated sensor array based on quartz crystal microbalance for chemical agent analysis

Available online at www.sciencedirect.com European Polymer Journal 44 (2008) 1157–1164 EUROPEAN POLYMER JOURNAL www.elsevier.com/locate/europolj Po...

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Available online at www.sciencedirect.com

European Polymer Journal 44 (2008) 1157–1164

EUROPEAN POLYMER JOURNAL www.elsevier.com/locate/europolj

Polymer coated sensor array based on quartz crystal microbalance for chemical agent analysis Zhihua Ying, Yadong Jiang *, Xiaosong Du, Guangzhong Xie, Junsheng Yu *, Huiling Tai State Key Laboratory of Electronic Thin Films and Integrated Devices, School of Optoelectronic Information, University of Electronic Science and Technology of China (UESTC), Chengdu 610054, PR China Received 14 September 2007; received in revised form 27 December 2007; accepted 11 January 2008 Available online 18 January 2008

Abstract Quartz crystal microbalance (QCM) gas sensors based on polymeric material were fabricated and their gas response characteristics were examined for four simulant gases of chemical agents, which were dimethyl methyl phosphonate (DMMP), N,N-dimethylacetamide (DMA), 1,5-dichloropentane (DCP) and dichloroethane (DCE). For the selection of appropriate coating materials, both principal component analysis (PCA) and hierarchical cluster methods were applied to a data set collected from 15 QCM sensors for 12 analytes. Four appropriate coating materials were selected after optimizing the correlation between the 15 coating materials and the first four principal component (PC) factors. The four chosen polymers were used as sensitive component for a sensor array, and then PCA is adapted to classify four simulant gases. The results show that the QCM sensor array has high sensitivity and selectivity to four chemical agents. Ó 2008 Elsevier Ltd. All rights reserved. Keywords: Polymer; QCM sensor array; Chemical agent; Principal component analysis

1. Introduction Chemical agent defines as a chemical substance that has the potential to cause physical and/or physiological changes for human being, animal and their habitation. The intentional use of chemical agents for war and terrorism has created an imperative for the development of rapid detection and sensitive analytical method and apparatus. The horrific terrorism attack of 9-11-2001 in USA as well as in several other

countries in recent years have greatly spurred that urgency [1,2]. The current sensing systems can range from a sophisticated GC–MS to an array of simple chemical sensors. Among all kinds of sensors, quartz crystal microbalances (QCMs) sensor has attracted great attention for its low cost, compact volume, easy portability and high sensitivity [3–5]. As well known, the relation between mass changes Dm (g) due to absorbed/desorbed gas molecules and frequency shifts Df (Hz) follows Sauerbrey equation [5–7]

*

Corresponding authors. Fax: +86 28 83206123 (Y. Jiang). E-mail addresses: [email protected] (Y. Jiang), [email protected] (J. Yu).

Df ¼

0014-3057/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.eurpolymj.2008.01.015

2:3  106 F 2 Dm A

ð1Þ

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where F (Hz) is fundamental resonance frequency, and A (cm2) is sensing surface area. Selectivity, as well as the response time, stability and reproducibility of chemical sensors, has been a drawback to overcome. To compensate for the deficiency of selectivity, pattern recognition approaches have been applied using an array of various sensors [6– 12]. Also, many researches carried out an array of QCMs for those advantages [9,10]. In this study, by applying the principal component analysis (PCA) and hierarchical cluster analysis to a data set containing frequency shifts measured in response to analytes adsorbing on specific candidate coating materials, a reduced set of coatings for obtaining the maximum discriminating information of all analytes were attempted. And the sensing properties of the chemical agents were investigated using four simulant gases of dimethyl methyl phosphonate (DMMP), N,N-dimethyl acetamide (DMA), 1,5-dichloropentane (DCP) and 1,2-dichloroethane (DCE).

without further purification. Ultrapure water (18 MX cm) was used throughout all processes. The coating materials were as follows: poly(isobutylene) (PIB), poly(epichlorohydrin) (PECH), poly(ethylenimine) (PEI, 50 wt% aqueous solution) (from Acros organics), poly(vinylidene fluoride) (PVDF), polypyrrole (PPY, 5 wt% aqueous solution) and poly(vinyl alcohol) (PVA), all purchased from Aldrich. Polyethylene glycol (PEG), poly (acrylic acid) (PAA, 25 wt% aqueous solution), and poly(methylmethacrylate) (PMMA), all from Alfa Aesar. Poly(vinyl pyrrolidone) (PVP) obtained from BASF, Germany and collodion (AR) from Kelong, China. All chemicals were used as received. The following coatings were synthesized in our group. Polymethyl[3-(2-hydroxy)phenyl] siloxane (PMPS), BSP3 [11], poly(2-methoxy-5-octyloxy)1,4-phenylene vinylene (PMOCOPV), and poly(3,4ethylenedioxythiophene) (PEDT). All these polymer materials and their structures are given in Table 2. 2.2. Film preparation

2. Experimental 2.1. Reagents and materials The vapors used listed in Table 1. The volatile organic solvents were all analytical reagent grade reagents: DMMP (97%, Aldrich); DCP 98%, 2,20 thiodiethanol 99% (Alfa Aesar); DCE, DMA, cyclohexanone, hexane, ethanol, acetone; isopropanol, isooctane, ethyl acetate and acetic acid (all from Kelong, China). All solvents were used as received

Table 1 Chemical structures and CAS numbers of test vapors

Most of solutions for spin coating were prepared in chloroform (Kelong, Chengdu, China), except aqueous solution used as received and PVDF in N,N-dimethyl formamide (DMF). The polymer concentration was 0.05 M for all polymers. Spincoated films were prepared on the electrodes of QCMs at a spinning rate of 3000 rpm for 30 s. After film deposition, a frequency shift of 3–5 kHz was obtained, which revealed the coating thickness was between 20 and 35 nm, assuming that the film density was about 1 g/ml. 2.3. Apparatus for characterization

Test vapor

Chemical structure

CAS no.

DMMP DCP 2,20 -thiodiethanol DCE DMA

756-79-6 628-76-2 111-48-8 107-06-2 127-19-5

Cyclohexanone

(CH3O)2P(O)(CH3) Cl(CH2)5Cl S(CH2CH2OH)2 ClCH2CH2Cl CH3CON(CH3)2 o C6H10O

Hexane Ethanol Acetone Isopropanol Isooctane Ethyl acetate Acetic acid

CH3(CH2)4CH3 CH3CH2OH CH3COCH3 (CH3)2CHOH (CH3)2CHCH2C(CH3)3 CH3CO2CH2CH3 CH3COOH

110-54-3 64-17-5 67-64-1 67-63-0 540-84-1 141-78-6 64-19-7

108-94-1

The resonant frequency of commercialized QCMs (Benyue, China) used in this work was 8 MHz. The measurement was carried out in a sealed glass vessel having a volume of 250 ml. The QCM sensors were attached to the inner side of glass vessel lid. The resonant frequency shifts of QCM sensors due to vapor adsorption were monitored with a frequency counter (SUING, SS7200 intelligent counter, the Fourth Radio Factory, China) connected to a RF switch (SS2901A, the Fourth Radio Factory, China), and the recorded data were transferred to a computer versus GPIB interface. QCM-5 Oscillator (Shengyang Vacuum

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Table 2 Coating materials and their structures used by QCM sensors in the array Coating material

Chemical structure

PIB

Coating material

Chemical structure

PAA

[–CH2CH(COOH)–]n

CH 2 Cl

CH 3 CH 3

PECH

O

PMMA

CH 2

O

H3C

n OCH 3

n O

CH 2 Cl

NH2

-(CH2-CH)n-

PEI

x

PVDF

N

O

C y

H

F

C

C

H

PVP

NH

N

F

Collodion

[C6H7O2(ONO2)3]n

n CH3

H N PPY

Si

H N N H

PMPS

O

n

CH2 CH2

n

CH2 OH

PVA

[–CH2CH(OH)–]n

PEG

H[OCH2CH2]nOH

OCH3 PEDT

PMOCOPV

CH=CH n OC8H17

BSP3

Technology Institute, China) was used to oscillate the QCM devices. The measurement setup is depicted in a schematic diagram of Fig. 1. All the data were processed by SPSS 13.0.

2.4. Sampling method The sample of 10 ml liquid was placed in a closed 500 ml bottle at 25 ± 0.1 °C for at least 30 min.

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Fig. 1. Schematic diagram of measurement system in this study.

After that time saturated vapor was collected in a 60 mL syringe from the headspace above the liquid solvent, and analytes with different concentrations were obtained by multiple dilutions [12,13]. The vapor was diluted by injecting less volume vapor sample into a 100 mL dilution chamber containing clean gas. When repetitive dilution was carried out, the concentration needed in the experiment was obtained. A fresh syringe was used for each step and the dilution chamber was thoroughly flushed after each step. 3. Results and discussion Theoretically, using more sensors to obtain as many as parameters provides more information to verify the analysis for unknown chemical vapors. In practical application, it is more cost-effective and convenient if fewer sensors can be applied to represent a maximal variance of unknown chemicals. To select a minimum number of sensors that adequately describe different chemicals in the samples, we applied for PCA and hierarchical cluster analysis methods. 3.1. Selection of coating materials Various organic coating materials, PVP, PECH, PIB, PMMA, PVDF, PMPS, BSP3, PEI, PEG, PEDOT, PAA, PMOCOPV, PVA, PPY and collodion, were used as an adsorbent film for QCM sensors to detect 12 different organic vapors. The selection of coating materials was based on their stability, reversibility, selectivity, and hydrophobic property. The organic vapors are DMMP, 2,20 -thi-

odiethanol, DCE, cyclohexanone, hexane, ethanol, acetone, isopropanol, isooctane, water, ethyl acetate and acetic acid, which were chosen to represent a variety of structural and functional groups. In addition, we were specifically interested in coatings that would be sensitive to chemical agent compounds. The set of vapors contain three vapors selected as simulants. DMMP, 2,20 -thiodiethanol and DCE are structurally similar to chemical agents. In the measurement, the coated QCMs were placed in an airtight test chamber and the frequency shifts were monitored on-line. After injecting organic vapors into the system, coating materials adsorbed the organic vapors, the frequency shifts were monitored and response curves were obtained from a computer. The response curves show that the sensors have short response times and stable signals in this system. As an example, the response curves of PEG for various vapors (DMMP, cyclohexanone, hexane, ethanol and acetone) are shown in Fig. 2. The response curve shows that the sensor has short response time and stable signal. Reversible responses were observed for most vapor/coating pairs. The detailed data listed in Tables 3 and 4 are the frequency shifts observed for each analyte on each coating. To find out the most useful information from primary data, they were analyzed by PCA and hierarchical cluster methods. 3.1.1. Principle components analysis Principal component analysis (PCA) is useful to determine a proper potential coating material. The central idea of PCA is to reduce the dimensionality of a data set in which there are a large number of interrelated variables, while retaining as much as possible variation in the data set [9]. This is achieved by transforming to a smaller set of variables, the principal components (PCs), which are uncorrelated and ordered, so that the first few PCs retain most of the variation present in all of these original variables. The analysis of PCA first transforms each response into a normalized value. The purpose here of taking the normalized value is to standardize the responses of 15 sensors so that the situation of large variance but low distinguishability can be avoided [9]. The results of principal components analysis (PCA) for this data set, presented in Table 5, indicate that 92.1% variance in responses to the vapors can be accounted for with only four principal components. Since their eigenvalues are all more than 1, which indicates they have high distinguishability,

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Fig. 2. Responses of PEG coated QCM sensor for various compounds: (a) DMMP; (b) cyclohexanone; (c) hexane; (d) ethanol; (e) acetone.

Table 3 Responses of various material coated QCM sensors to different analytes in Hz Coating material

PVP

PECH

PIB

PMMA

PVDF

PMPS

BSP3

PEI

DCE DMMP Cyclohexanone Hexane Ethanol Acetone 2,20 -thiodiethanol Isopropanol Water Isooctane Ethyl acetate Acetic acid

218 82 80 110 234 135 52 95 173 120 94 1236

170 40 103 22 17 115 4 17.6 6 17 120 60

67 5.8 25 110 10 15 1 6 1 87 42 11

100 4 6 11 42 58 1 12 10 10 35 64

37 69 37 13 13 70 1 14 4 10 49 43

82 557 382 58 141 289 8 151 12 17 281 88

154 1178 630 72 281 611 13 446 12 33 807 193

164 80 78 108 160 110 56 105 316 200 110 6060

Table 4 Responses of various material coated QCM sensors to different analytes in Hz (continues from Table 1) Coating material

PEG

PEDT

PAA

PMOCOPV

PVA

PPY

Collodion

DCE DMMP Cyclohexanone Hexane Ethanol Acetone 2,20 -thiodiethanol Isopropanol Water Isooctane Ethyl acetate Acetic acid

140 18 21 14.3 22 31 3 20 11.5 13.2 33.3 146

55 27.4 21.5 25 16.8 18 12 16.8 28.6 28 26.2 445

130 73.6 63 95.5 40.6 38.5 40 47 122.8 96.9 58 2500

215 27 58 63 39 87 5 30 9 30 67 76

29 16 15 23 10 13 7 10 26 25 19 75

440 326.2 268.6 360.3 464 195 143.4 161 328.6 402 326 660

500 286 207 327 520 6500 55 95 334 254 700 900

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Table 5 Eigenvectors and eigenvalues determined by PCA method Eigenvector

1 2 3 4

Eigenvalue

% of variance preserved

6.719 3.615 2.422 1.059

Each

Total

44.794 24.100 16.143 7.062

44.794 68.894 85.038 92.099

these four factors are used to select four out of 15 coatings. Although the first principal component explains most of variance in the data, the most efficient approach to array construction entails selecting one sensor to provide the greatest contribution to each principal component [14]. This implies that the greatest information about vapor identities will be obtained from four well-chosen sensors and adding a fifth sensor will yield only marginal improvement. The correlation coefficient matrix of factors 1–4 with respect to 15 coatings are shown in Table 6. To select 4 from 15 coatings, the correlation coefficients of the first four factors with respect to the coatings are compared. For the first principal component, sensors coated with PVP, PEDT, and PVA contribute similarly, with a slight edge for the PVP. The second principal component is influenced most by the sensor coated with PVDF. The third and fourth principal components are influenced most by the sensors coated with PIB and PMOCOPV, respectively. This suggests that the four-sensor array providing the greatest degree of discrimination would consist of sensors coated with PVP, PVDF, PIB and PMOCOPV.

3.1.2. Hierarchical cluster analysis The hierarchical dendrogram plot of the coating materials, Fig. 3, can also be used to provide information on chemical behavior and to verify the results obtained by PCA. The interaction between analyte and coating material results in data points lying close each other for similar coatings and far apart for dissimilar coatings. Therefore, chemically similar coatings should be classified into one cluster. On comparison of the hierarchical cluster analysis results with those obtained from PCA, also PVP, PVDF, PIB and PMOCOPV were chosen out as the representative coatings. The suitability of four coatings to compose of an array can be explained from intermolecular attraction. As shown in Fig. 3, group A primarily consists of the first five materials including PVP, which mainly affords H-bond base interactions, while group B has the other four materials, including PMOCOPV, which mainly affords interactions due to polarizability. In group C including PVDF, which mainly affords H-bond acid interactions; and dispersion is the major interaction in PIB. The four coating materials possess different interaction, and the array can well play the role to recognize the analytes. 3.2. Vapor classification using the sensor array DMMP, DMA, DCE and DCP were used as the simulants of chemical agents. DMMP is structurally similar to many of organophosphorus nerve agents.

Table 6 Summary of the correlation coefficients of the fist four factors and all of the coatings Component

PVP PECH PIB PMMA PVDF PMPS BSP3 PEI PEG PEDT PAA PMOCOPV PVA PPY Collodion

1

2

3

4

0.948 0.310 0.016 0.669 0.251 0.196 0.193 0.916 0.901 0.940 0.901 0.468 0.943 0.822 0.063

0.184 0.814 0.061 0.454 0.885 0.739 0.742 0.209 0.221 0.166 0.187 0.553 0.179 0.150 0.561

0.242 0.372 0.690 0.467 0.325 0.544 0.552 0.321 0.261 0.275 0.355 0.661 0.082 0.081 0.039

0.064 0.030 0.442 0.225 0.050 0.316 0.294 0.043 0.034 0.006 0.054 0.053 0.110 0.340 0.695

Fig. 3. Hierarchical dendrogram of 15 layers coated materials.

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DMA has solubility properties that are similar to DMMP, as indicated by the solubility parameter values [15]. DCP is mainly chosen as the simulant of vesicant agents, and DCE is similar to DCP in molecular structure.

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Fig. 4 shows the four selected polymer coated QCMs frequency shifts to three different concentration (20, 30, and 40 ppm) analytes. The frequency shifts were compared by normalizing relative frequency shifts to vapor concentration in order to

Fig. 4. Frequency shifts of polymer coated QCMs to different analytes: (a) DMMP; (b) DMA; (c) DCP; (d) DCE.

Fig. 5. Scattering diagram of the analytes by PCA with frequency shifts.

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study all vapors at identical concentration (in ppm). The relative frequency shifts were obtained at saturated vapor concentration. Obviously, discrepancy can be found among four chemicals. PVDF coated sensor showed the highest selectivity to DMA. The PVP sensor was more selective to DCP. Additionally, both PVDF and PVP showed a high sensitivity to DMMP. In the case of DCE, although PMOCOPV and PVP had faster responses, the frequency shifts to all sensors were low. PCA method is applied to classify the chemical agent analytes. Fig. 5 shows the results of PCA of three frequency shifts to the analytes, respectively. It can be observed that DMMP, DMA and DCP can be clearly distinguished. There seems to be an overlap area for DCE. However, when the scale is magnified, as shown in insert plot, DCE of various concentrations can be separated distinctly. It is evident that each profile discrimination of DMMP, DMA, DCP and DCE has unique and fixed direction and shape. The separately plotted data dependent on vapor species and different concentrations suggests that this sensor system be applied for automatic qualification and quantification using artificial neural network. This result also indicates polymer coated QCM sensor array is a promising chemical agent detector. 4. Conclusion Pattern recognition techniques were applied to frequency shift data obtained from 15 QCM sensors, formed by coating with 15 different materials, and tested using 12 analytes. The objective to use a subset of coatings without a significant loss of analytes identification information was achieved. The first four principal components described almost 92.1% of the variance in original data set of 15 coatings. And the hierarchical cluster analysis verified the results of PCA, which proved that PCA could be adapted to classify the chemical warfare agents. The selected polymers composed of a sensor array

and have proven to be capable of discriminating different chemical agents even within identical chemical group. The most significant result shown in this work is that large arrays of polymer coated QCM sensors are not indispensable for accurate chemical agent’s analysis, while an array of only four sensors can realize the recognition and discrimination of chemical agents well. Acknowledgement This work was partially supported by National Science Foundation of China via Grant Nos. 60425101, 60736005 and Program for New Century Excellent Talents in University via Grant No. NCET-06-0812. References [1] . [2] Kanua AB, Haigh PE, Hill HH. Anal Chim Acta 2005; 553(1–2):148–59. [3] Koshets IA, Kazantseva ZI, Shirshov YM, Cherenok SA, Kalchenko VI. Sens Act B 2005;106(1):177–81. [4] Zeng H, Jiang Y, Xie G, Yu J. Sens Act B 2007;122(1):1–6. [5] Ying Z, Jiang Y, Du X, Xie G, Yu J, Wang H. Sens Act B 2007;125(1):167–72. [6] Sasaki I, Tsuchiya H, Nishioka M, Sadakata M, Okubo T. Sens Act B 2002;86(1):26–33. [7] Lau KT, Micklefield J, Slater J. Sens Act B 1998; 50(1):69–79. [8] Joo BS, Huh JS, Lee DD. Sens Act B 2007;121(1):47–53. [9] Chang P, Shih JS. Anal Chim Acta 2000;403(1):39–48. [10] Shafiqul Islam AKM, Ismail Z, Ahmad MN, Saad B, Othman AR, Shakaff AYMd, et al. Sens Act B 2005;109(2):238–43. [11] Grate JW, Patrash SJ, Kaganove SN, Wise BM. Anal Chem 1999;71:1033–40. [12] Santos JP, Fernandez MJ, Fontecha JL, Lozano J, Aleixandre M, Garcıa M, et al. Sens Act B 2005;107(1):291–5. [13] Levitsky I, Krivoshlykov SG, Grate JW. Anal Chem 2001; 73:3441–8. [14] Park J, Groves WA, Zellers ET. Anal Chem 1999;71: 3877–86. [15] McGill RA, Abraham MH, Grate JW. Chemtech 1994;9: 27–37.