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A research on detection and identification of volatile organic compounds utilizing cataluminescence-based sensor array
Sensors and Actuators B 177 (2013) 1167–1172
Contents lists available at SciVerse ScienceDirect
Sensors and Actuators B: Chemical journal homepage: www.elsevier.com/locate/snb
A research on detection and identification of volatile organic compounds utilizing cataluminescence-based sensor array Bo Li a,b,∗ , Juefu Liu a , Guolong Shi b , Jinhuai Liu b a b
School of Information Engineering, East China Jiaotong University, Nanchang 330013, PR China Research Center for Biomimetic Functional Materials and Sensing Devices, Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, PR China
a r t i c l e
i n f o
Article history: Received 27 September 2012 Received in revised form 10 December 2012 Accepted 12 December 2012 Available online 21 December 2012 Keywords: Cataluminescence Sensor array Gas sensor Pattern recognition
a b s t r a c t A novel cataluminescence (CTL)-based sensor array consisting of 16 types of catalytic nanomaterials was developed for the determination and identification of volatile organic compounds (VOCs). The sensing nanomaterials, including nano-sized metal oxides, decorated nanoparticles, carbonates, and nano-sized AgSe have been selected carefully. A 4 × 4 array was integrated by depositing these nanomaterials onto the ceramic chip. Dynamic and static analysis methods were utilized to characterize the performance of the sensor array to 12 kinds of VOCs. Each compound gives its unique CTL pattern after interaction with the sensor array, which can be employed to recognize VOCs. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) were used to analyze the CTL patterns. PCA was conducted to classify the drug precursor gas and the plots showed that the groups were well classified. In addition, the patterns obtained at different working temperature and the analytical characteristics of array were investigated. The CTL-based sensor array shows promising perspective for the recognition and discrimination of VOCs. Crown Copyright © 2012 Published by Elsevier B.V. All rights reserved.
1. Introduction Volatile organic compounds (VOCs) are ubiquitous in the air we breathe which can cause short- or long-term adverse health effects [1]. After Breysse et al. [2] reported that the catalytic oxidation of carbon monoxide on the surface of thoria could produce a weak chemiluminescence (CL) emission, and established a concept of “cataluminescence (CTL)” for the first time, persistent efforts have been made for many years to develop the CTL analytical method for the detection of VOCs such as acetaldehyde [3], benzene [4], benzaldehyde [5], formaldehyde [6], ethanol [7], ether [8] and acetone [9]. Furthermore, the development of CTL-based sensors offers new opportunities for VOCs analysis, mainly because of the high sensitivity, long-term stability, and simplicity of the CTL sensors [10]. However, for the recognition of complex and similar mixtures, a single sensing element is limited, a sensor array based on CTL sensing mode is desired [11]. In recent years, although sensor array technology has been applied successfully in gas detection, multiple sensing elements are not beneficial to the stability of the instrument [12–16]. In comparison, the sensing nanomaterials of CTL-based sensor array
∗ Corresponding author at: School of Information Engineering, East China Jiaotong University, Nanchang 330013, PR China. Tel.: +86 0791 87046245; fax: +86 0791 87046245. E-mail address: [email protected] (B. Li).
are solid catalysts and essentially without consumption during the sensing process [17,18], which means this new sensor array possesses long-term stability. Therefore, it provides a novel sensing strategy for the detection and identification of the analytes. Moreover, the development of nanoscience and nanotechnology also brings great opportunities for the advancement of CTL sensor array [19]. It is worth mentioning that Zhang et al. developed a CTL-based sensor array with nine sensing elements to recognize alcohols, amines and thiols [20]. Moreover, the sensor array constructed by CTL transducers shows the ability to collect many kinds of complex information simultaneously, including signal intensity, temperature, luminescence lifetime, wave-length, and spectral shape [21]. In our previous work [22,23], single-sensor systems were applied for detecting some VOCs. For example, nanosized La2 O3 and cocoon-like Au/La2 O3 were used for detecting tetrahydrofuran, acetone, ethanol, benzene, chloroform and chlorobenzene. Enhanced CTL performance of ether on nanosized SiO2 /Fe3 O4 was observed compared with pure Fe3 O4 . However, this study was limited to study only four catalysts, a wider adaptation for their application as sensor array elements have not been demonstrated. In this paper, a total of 12 kinds of VOCs have been discriminated based on their distinct CTL patterns obtained by a 4 × 4 nanomaterial-based array. The collected testing data are further processed using hierarchical cluster analysis (HCA) and principle component analysis (PCA) methods in order to illustrate the selectivity of this sensor array. In addition, the analytical characteristics and temperature effect of the CTL-based array had been
0925-4005/$ – see front matter. Crown Copyright © 2012 Published by Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.snb.2012.12.049
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Fig. 1. Schematic diagram of the cataluminescence (CTL) sensor array system.
investigated. The excellent linearity and stability indicates the feasibility of this array for VOCs determination. Illegal drug detection is of great importance for public security, our study demonstrated the possibility for determination and identification of drug precursor gas by increasing sensor units.
Fig. 2. Arrangement of nanomaterials spots on the sensor array.
2. Experimental The schematic diagram of the experimental device is presented in Fig. 1. The sensing nanomaterials, including nanosized oxides (La2 O3 , SiO2 , Y2 O3 , MgO, Al2 O3 , ZrO2 , CeO2 , ZnO, Fe3 O4 and CuO), decorated nanoparticles (SiO2 /Fe3 O4 , CeO2 /TiO2 , Au/La2 O3 and ZrO2 /MgO), carbonates (BaCO3 ) and nanosized AgSe were synthesized. As shown in Fig. 2, nanomaterials were spotted orderly onto the surface of a ceramic chip to form a 4 × 4 array (about 0.2 mm in thickness and 4 mm in diameter for each sensing element). The air
flow was supplied by a pneumatic pump and a precision flow meter was employed for the measurement of the gas flow rate. A digital temperature controller was used to control the temperature of the ceramic chip. The final CTL patterns were recorded by a camera closely placed to the ceramic chip. All chemicals (ethyl acetate, ether, ethanol, benzene, acetone, formaldehyde, methanol, acetaldehyde, chloroform, chlorobenzene, toluene, and tetrahydrofuran) used in the experiment had the high purity (≥99.0%) of analytical grade or even higher and were
Fig. 3. Histogram of the brightness for VOCs on each sensing element of the array obtained from the patterns. The concentrations of all the VOCs are 2000 ppm, working temperature: 210 ◦ C, flow rate of carrier gas: 240 mL/min.
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Fig. 4. Hierarchical cluster analysis figure of 12 VOCs.
purchased from Shanghai Chemical Reagents Company. Thus they could be used in our experiment without further purification. 3. Results and discussion 3.1. Discrimination and cross-reactivity by sensor array The cross sensitivity responses are crucial for discrimination using nonspecific response patterns [24,25]. In order to analyze the cross sensitivity of the sensor array, a total of 12 types of common VOCs, including ethyl acetate, ether, ethanol, benzene, acetone, formaldehyde, methanol, acetaldehyde, chloroform, chlorobenzene, toluene and tetrahydrofuran, were examined in our study. The mean responses of 12 VOCs on different CTL-based sensor array were shown in Fig. 3. As expected, the results showed that luminescent efficiencies of the CTL are distinct for a given compound on different catalysts, and the same catalyst exhibits different CTL signals to different VOCs. Therefore, the fabricated cross sensitivity sensor array was confirmed feasible to be used for discriminating VOCs. The collection and analysis of the sensor array data was carried out, a metric analysis was then applied in order to estimate the multivariate distances among the responses of each compound. The procedure for the identification of 12 types of common VOCs was defined by HCA. HCA is a multivariate statistical analysis method which is comprised of agglomerative and divisive methods that find clusters of observations within a data set [26]. The HCA dendrogram was shown in Fig. 4, all 12 VOCs gas samples are accurately identified against each other. Remarkably, an excellent differentiation of closely related VOCs was achieved with the data shown in Fig. 3. It suggested that the CTL-based sensor array had commendable selectivity and immunity on recognition of VOCs. 3.2. Discrimination of drug precursor gas Many VOCs, such as ether, acetone, chloroform, and toluene, are usually important auxiliary materials utilized by drug trafficker even though they are not drugs of their own. Obviously, the detection and recognition of drug precursor gas is necessary. Selectivity is the ability of the array to distinguish one analyte from another, which is an important criterion in selecting a sensor array [27]. PCA can be used to extract the selective feature of original data according to variance criteria and
Fig. 5. (a) PCA score plot of four types of drug precursor gas. The concentrations of all the VOCs are 2000 ppm, working temperature: 210 ◦ C, flow rate of carrier gas: 240 mL/min. (b) PCA score plot of four types of drug precursor gas and two kinds of VOCs. The concentrations of all the VOCs are 4000 ppm, working temperature: 210 ◦ C, flow rate of carrier gas: 240 mL/min.
visualize the extracted feature. The PCA result of the four drug samples was shown in Fig. 5(a). Each analyte including ether (purity ≥ 99.7%), acetone (purity ≥ 99.5%), chloroform (purity ≥ 99.0%), toluene (purity ≥ 99.5%) formed a differentiable and tight cluster in the PCA score plot. Each chemical was tested for 5 times and the first three PCs accounted for 98.23% of the total variance. The sensor array was further applied to distinguish ether, acetone, chloroform, toluene, ethanol (purity ≥ 99.7%) and acetaldehyde (purity ≥ 99.5%) under the concentration of 4000 ppm. The PCA result was shown in Fig. 5(b). Each chemical was also tested for 5 times and the first three PCs were 67.94%, 25.18% and 5.16%. The three-dimensional PCA score plot showed clear clustering of 30 trials carried out for six different gases. The PCA results show that most of different groups were separated well, and hence proved the analytes discrimination capability of the CTLbased sensor array. The results of pattern recognition illustrated
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Fig. 6. The patterns of the sensor array at different working temperature. The concentrations of the compounds, 2000 ppm; flow rate of carrier gas: 240 mL/min.
Fig. 7. The calibration curves for ether and acetone at 210 ◦ C, and flow rate of carrier gas: 240 mL/min.
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Fig. 8. The CTL patterns of acetone during 0–90 h. The concentration of acetone is 2000 ppm, working temperature: 210 ◦ C, flow rate of carrier gas: 240 mL/min.
the excellent selectivity and repeatability of this sensor array. In this case, this sensor array could be applied for the discrimination of real drug samples containing enough amounts of these analytes. 3.3. Effect of working temperature Temperature is an important factor for optimization of CTLbased sensor array on account of the fact that catalytic reaction is temperature dependent. Different temperature may lead to different luminescence efficiencies during the course to discriminate the compounds with similar chemical properties [28,29]. The patterns of the sensor array at different working temperature were investigated by the CTL-based array imaging approach in our work. As shown in Fig. 6, the discrimination of ether, acetone, chloroform and toluene at different temperature was carried out. For chloroform and toluene the patterns with a relative weak brightness of spots were obtained at 190 ◦ C. When the temperature was increased to 210 ◦ C and 230 ◦ C, the brightness of the spots on the patterns was feasible to differentiate these four compounds. In the present study, a lower temperature of 210 ◦ C was selected as the working temperature for the discrimination of VOCs by the sensor array. The present work indicates different patterns can be obtained at different working temperature which may contribute to the distinguishing of VOCs. 3.4. Analytical characteristics and lifetime of the sensor array Further, characteristics of the CTL-based array performance of the four kinds of VOCs sensing were investigated under the optimal conditions. The array exhibits sensitive and stable CTL responses to ether, acetone, chloroform, and toluene, and the relative CTL intensity increases with the concentration of the four compounds. For example, responses of ether on the sensor utilized SiO2 /Fe3 O4 microspheres are shown in Fig. 7(a). The detection limit is 7 ppm and the linear range is 10–4000 ppm (R = 0.9985). In addition, responses of acetone on the sensor utilized Au/La2 O3 nanomaterial are shown in Fig. 7(b). The detection limit is 5 ppm and the linear range is 10–3000 ppm (R = 0.9975). The quantitative analysis indicates the sensor array can be used to quantify the concentration of given analyte by its CTL intensity. Most nanomaterial-based sensors have a long-term stability since the sensing materials are solid catalysts and essentially nonconsumable during the sensing process [21]. During the study, the stability of single sensor unit and the whole array were investigated. We found that the as-prepared CTL-based sensor array showed excellent stability and durability toward VOCs by testing the 12 kinds of common possible compounds under the optimal conditions. For example, the CTL intensities of SiO2 /Fe3 O4 sensor unit were collected every 2 h. The sensor exhibited good stability
and durability for continuously introducing 500 ppm ether for 100 h and the signal variation varied within ±8%. Moreover, the stability of the whole array was investigated. The CTL patterns of acetone during 0–90 h was shown in Fig. 8, most sensor units show no significant difference on CTL signal from 0 to 90 h. However, the signal of (1, 3) and (2, 4) shows a little decrease after 90 h. 4. Conclusions In conclusion, a catalytic nanomaterial-based CTL sensor array was developed for the discrimination and identification of VOCs. Different CTL patterns have been obtained for 12 types of VOCs and four drug precursor gases were successfully discriminated with the array. A good correlation between gas concentration and CTL intensity was obtained, which indicated the CTL intensity can be used in the evaluation of analyte concentration. The high sensitivity, long-term stability, and simplicity indicate the promising practical application of this sensor array. Acknowledgements This work was supported by the National Basic Research Program of China (2011CB933700), the National Natural Science Foundation of China (61163055) and the Natural Science Foundation of Jiangxi Province of China (20114BAB211017). References [1] L. Tang, Y.M. Li, K.L. Xu, X.D. Hou, Y. Lv, Sensitive and selective acetone sensor based on its cataluminescence from nano-La2 O3 surface, Sensors and Actuators B: Chemistry 132 (2008) 243–249. [2] M. Breysse, B. Claudel, L. Faure, M. Guenin, R.J.J. Williams, Chemiluminescense during the catalysis of carbon monoxide oxidation on a thoria surface, Journal of Catalysis 45 (1976) 137–144. [3] X.A. Cao, Z.Y. Zhang, X.R. Zhang, A novel gaseous acetaldehyde sensor utilizing cataluminescence on nanosized BaCO3 , Sensors and Actuators B: Chemistry 99 (2004) 30–35. [4] Z.M. Rao, L.J. Liu, J.Y. Xie, Y.Y. Zeng, Development of a benzene vapour sensor utilizing chemiluminescence on Y2 O3 , Luminescence 23 (2008) 163–168. [5] Y.Y. Wu, S.C. Zhang, X. Wang, N. Na, Z.X. Zhang, Development of a benzaldehyde sensor utilizing chemiluminescence on nanosized Y2 O3 , Luminescence 23 (2008) 376–380. [6] K.W. Zhou, X.L. Ji, N. Zhang, X.R. Zhang, On-line monitoring of formaldehyde in air by cataluminescence-based gas sensor, Sensors and Actuators B: Chemistry 119 (2006) 392–397. [7] Z.Y. Zhang, C. Zhang, X.R. Zhang, Development of a chemiluminescence ethanol sensor based on nanosized ZrO2 , Analyst 127 (2002) 792–796. [8] J. Hu, K.L. Xu, Y.Z. Jia, Y. Lv, Y.B. Li, X.D. Hou, Oxidation of ethyl ether on borate glass: chemiluminescence, mechanism, and development of a sensitive gas sensor, Analytical Chemistry 80 (2008) 7964–7969. [9] L.C. Zhang, Q. Zhou, Z.H. Liu, X.D. Hou, Y.B. Li, Y. Lv, Novel Mn3 O4 micro-octahedra: promising cataluminescence sensing material for acetone, Chemistry of Materials 21 (2009) 5066–5071. [10] R.K. Zhang, X.A. Cao, Y.H. Liu, X.Y. Chang, A new method for identifying compounds by luminescent response profiles on a cataluminescence based sensor, Analytical Chemistry 83 (2011) 8975–8983.
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[11] J. Hu, X.M. Jiang, L. Wu, K.L. Xu, X.D. Hou, Y. Lv, UV-induced surface photovoltage and photoluminescence on n-Si/TiO2 /TiO2 : Eu for dual-channel sensing of volatile organic compounds, Analytical Chemistry 83 (2011) 6552–6558. [12] C.D. Natale, M.Z. Sasse, A. Macagnano, R. Paolesse, B. Herold, A. D’Amico, Outer product analysis of electronic nose and visible spectra: application to the measurement of peach fruit characteristics, Analytica Chimica Acta 459 (2002) 107–117. [13] F.D. Francesco, B. Lazzerini, F. Marcelloni, G. Pioggia, An electronic nose for odour annoyance assessment, Atmospheric Environment 35 (2001) 1225–1234. [14] A. Pavlou, A.P.F. Turner, N. Magan, Recognition of anaerobic bacterial isolates in vitro using electronic nose technology, Letters in Applied Microbiology 35 (2002) 366–369. [15] S.E. Stitzel, L.J. Cowen, K.J. Albert, D.R. Walt, Array to array transfer of an artificial nose classifier, Analytical Chemistry 73 (2001) 5266–5271. [16] K.J. Albert, D.S. Gill, T.C. Pearce, D.R. Walt, Automatic decoding of sensor types within randomly ordered, high-density optical sensor arrays, Analytical and Bioanalytical Chemistry 373 (2002) 792–802. [17] Y.F. Zhu, J.J. Shi, Z.Y. Zhang, X.R. Zhang, Development of a gas sensor utilizing chemiluminescence on nanosized titanium dioxide, Analytical Chemistry 74 (2002) 120–124. [18] Z.Y. Zhang, K. Xu, W.R.G. Baeyens, X.R. Zhang, An energy-transfer cataluminescence reaction on nanosized catalysts and its application to chemical sensors, Analytica Chimica Acta 535 (2005) 145–152. [19] L.C. Zhang, J. Hu, Y. Lv, X.D. Hou, Recent progress in chemiluminescence for gas analysis, Applied Spectroscopy Review 45 (2010) 474–489. [20] N. Na, S.C. Zhang, S. Wang, X.R. Zhang, A catalytic nanomaterial-based optical chemo-sensor array, Journal of the American Chemical Society 128 (2006) 14420–14421. [21] Y.Y. Wu, N. Na, S. Zhang, X. Wang, D. Liu, X.R. Zhang, Discrimination and identification of flavors with catalytic nanomaterial-based optical chemosensor array, Analytical Chemistry 81 (2009) 961–966. [22] Z. Guo, Z.W. Jiang, X. Chen, B. Sun, M.Q. Li, J.H. Liu, X.J. Huang, Novel cocoon-like Au/La2 O3 nanomaterials: synthesis and their ultra-enhanced cataluminescence performance to volatile organic compounds, Journal of Materials Chemistry 21 (2011) 1874–1879. [23] G.L. Shi, B. Sun, Z. Jin, J.H. Liu, M.Q. Li, Synthesis of SiO2 /Fe3 O4 nanomaterial and its application as cataluminescence gas sensor material for ether, Sensors and Actuators B: Chemistry 171 (2012) 699–704. [24] K.J. Albert, N.S. Lewis, C.L. Schauer, G.A. Sotzing, S.E. Stitzel, T.P. Vaid, D.R. Walt, Cross-reactive chemical sensor arrays, Chemical Reviews 100 (2000) 2595–2626.
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Biographies Bo Li received his PhD in 2010 from Huazhong University of Science and Technology, China. Currently, he works as a lecturer at School of Information Engineering, East China Jiaotong University, China, and at same time doing postdoctoral research at Institute of Intelligent Machines, Chinese Academy of Sciences, China. His current research interests include pattern recognition, automatic detection and gas sensor. Juefu Liu received the BS degree from East China Institute of Technology, in 1982 and the MS degree in computer application from China University of Geosciences, in 1997. Currently, he works as a professor at School of Information Engineering, East China Jiaotong University. His current research interests include pattern recognition and automatic detection. Guolong Shi received his BS degree in 2010 from Hohai University, Nanjing, China. Now he is perusing his MS degree from University of Science and Technology of China, Hefei, Anhui. His research interests mainly are chemiluminescence and gas sensor. Jinhuai Liu received the BS degree in inorganic chemistry from Yunnan Agricultural University, China, in 1982 and the PhD degree in inorganic chemistry from Graduate University of Chinese Academy of Sciences, China, in 2003. Currently, he works as a researcher at Institute of Intelligent Machines, Chinese Academy of Sciences. His current research interests include biomimetric material, gas-sensing nanomaterials and nanodevice, sensing technology and their applications in detecting hazardous gases and drug/explosive.
Contents lists available at SciVerse ScienceDirect
Sensors and Actuators B: Chemical journal homepage: www.elsevier.com/locate/snb
A research on detection and identification of volatile organic compounds utilizing cataluminescence-based sensor array Bo Li a,b,∗ , Juefu Liu a , Guolong Shi b , Jinhuai Liu b a b
School of Information Engineering, East China Jiaotong University, Nanchang 330013, PR China Research Center for Biomimetic Functional Materials and Sensing Devices, Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, PR China
a r t i c l e
i n f o
Article history: Received 27 September 2012 Received in revised form 10 December 2012 Accepted 12 December 2012 Available online 21 December 2012 Keywords: Cataluminescence Sensor array Gas sensor Pattern recognition
a b s t r a c t A novel cataluminescence (CTL)-based sensor array consisting of 16 types of catalytic nanomaterials was developed for the determination and identification of volatile organic compounds (VOCs). The sensing nanomaterials, including nano-sized metal oxides, decorated nanoparticles, carbonates, and nano-sized AgSe have been selected carefully. A 4 × 4 array was integrated by depositing these nanomaterials onto the ceramic chip. Dynamic and static analysis methods were utilized to characterize the performance of the sensor array to 12 kinds of VOCs. Each compound gives its unique CTL pattern after interaction with the sensor array, which can be employed to recognize VOCs. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) were used to analyze the CTL patterns. PCA was conducted to classify the drug precursor gas and the plots showed that the groups were well classified. In addition, the patterns obtained at different working temperature and the analytical characteristics of array were investigated. The CTL-based sensor array shows promising perspective for the recognition and discrimination of VOCs. Crown Copyright © 2012 Published by Elsevier B.V. All rights reserved.
1. Introduction Volatile organic compounds (VOCs) are ubiquitous in the air we breathe which can cause short- or long-term adverse health effects [1]. After Breysse et al. [2] reported that the catalytic oxidation of carbon monoxide on the surface of thoria could produce a weak chemiluminescence (CL) emission, and established a concept of “cataluminescence (CTL)” for the first time, persistent efforts have been made for many years to develop the CTL analytical method for the detection of VOCs such as acetaldehyde [3], benzene [4], benzaldehyde [5], formaldehyde [6], ethanol [7], ether [8] and acetone [9]. Furthermore, the development of CTL-based sensors offers new opportunities for VOCs analysis, mainly because of the high sensitivity, long-term stability, and simplicity of the CTL sensors [10]. However, for the recognition of complex and similar mixtures, a single sensing element is limited, a sensor array based on CTL sensing mode is desired [11]. In recent years, although sensor array technology has been applied successfully in gas detection, multiple sensing elements are not beneficial to the stability of the instrument [12–16]. In comparison, the sensing nanomaterials of CTL-based sensor array
∗ Corresponding author at: School of Information Engineering, East China Jiaotong University, Nanchang 330013, PR China. Tel.: +86 0791 87046245; fax: +86 0791 87046245. E-mail address: [email protected] (B. Li).
are solid catalysts and essentially without consumption during the sensing process [17,18], which means this new sensor array possesses long-term stability. Therefore, it provides a novel sensing strategy for the detection and identification of the analytes. Moreover, the development of nanoscience and nanotechnology also brings great opportunities for the advancement of CTL sensor array [19]. It is worth mentioning that Zhang et al. developed a CTL-based sensor array with nine sensing elements to recognize alcohols, amines and thiols [20]. Moreover, the sensor array constructed by CTL transducers shows the ability to collect many kinds of complex information simultaneously, including signal intensity, temperature, luminescence lifetime, wave-length, and spectral shape [21]. In our previous work [22,23], single-sensor systems were applied for detecting some VOCs. For example, nanosized La2 O3 and cocoon-like Au/La2 O3 were used for detecting tetrahydrofuran, acetone, ethanol, benzene, chloroform and chlorobenzene. Enhanced CTL performance of ether on nanosized SiO2 /Fe3 O4 was observed compared with pure Fe3 O4 . However, this study was limited to study only four catalysts, a wider adaptation for their application as sensor array elements have not been demonstrated. In this paper, a total of 12 kinds of VOCs have been discriminated based on their distinct CTL patterns obtained by a 4 × 4 nanomaterial-based array. The collected testing data are further processed using hierarchical cluster analysis (HCA) and principle component analysis (PCA) methods in order to illustrate the selectivity of this sensor array. In addition, the analytical characteristics and temperature effect of the CTL-based array had been
0925-4005/$ – see front matter. Crown Copyright © 2012 Published by Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.snb.2012.12.049
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Fig. 1. Schematic diagram of the cataluminescence (CTL) sensor array system.
investigated. The excellent linearity and stability indicates the feasibility of this array for VOCs determination. Illegal drug detection is of great importance for public security, our study demonstrated the possibility for determination and identification of drug precursor gas by increasing sensor units.
Fig. 2. Arrangement of nanomaterials spots on the sensor array.
2. Experimental The schematic diagram of the experimental device is presented in Fig. 1. The sensing nanomaterials, including nanosized oxides (La2 O3 , SiO2 , Y2 O3 , MgO, Al2 O3 , ZrO2 , CeO2 , ZnO, Fe3 O4 and CuO), decorated nanoparticles (SiO2 /Fe3 O4 , CeO2 /TiO2 , Au/La2 O3 and ZrO2 /MgO), carbonates (BaCO3 ) and nanosized AgSe were synthesized. As shown in Fig. 2, nanomaterials were spotted orderly onto the surface of a ceramic chip to form a 4 × 4 array (about 0.2 mm in thickness and 4 mm in diameter for each sensing element). The air
flow was supplied by a pneumatic pump and a precision flow meter was employed for the measurement of the gas flow rate. A digital temperature controller was used to control the temperature of the ceramic chip. The final CTL patterns were recorded by a camera closely placed to the ceramic chip. All chemicals (ethyl acetate, ether, ethanol, benzene, acetone, formaldehyde, methanol, acetaldehyde, chloroform, chlorobenzene, toluene, and tetrahydrofuran) used in the experiment had the high purity (≥99.0%) of analytical grade or even higher and were
Fig. 3. Histogram of the brightness for VOCs on each sensing element of the array obtained from the patterns. The concentrations of all the VOCs are 2000 ppm, working temperature: 210 ◦ C, flow rate of carrier gas: 240 mL/min.
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Fig. 4. Hierarchical cluster analysis figure of 12 VOCs.
purchased from Shanghai Chemical Reagents Company. Thus they could be used in our experiment without further purification. 3. Results and discussion 3.1. Discrimination and cross-reactivity by sensor array The cross sensitivity responses are crucial for discrimination using nonspecific response patterns [24,25]. In order to analyze the cross sensitivity of the sensor array, a total of 12 types of common VOCs, including ethyl acetate, ether, ethanol, benzene, acetone, formaldehyde, methanol, acetaldehyde, chloroform, chlorobenzene, toluene and tetrahydrofuran, were examined in our study. The mean responses of 12 VOCs on different CTL-based sensor array were shown in Fig. 3. As expected, the results showed that luminescent efficiencies of the CTL are distinct for a given compound on different catalysts, and the same catalyst exhibits different CTL signals to different VOCs. Therefore, the fabricated cross sensitivity sensor array was confirmed feasible to be used for discriminating VOCs. The collection and analysis of the sensor array data was carried out, a metric analysis was then applied in order to estimate the multivariate distances among the responses of each compound. The procedure for the identification of 12 types of common VOCs was defined by HCA. HCA is a multivariate statistical analysis method which is comprised of agglomerative and divisive methods that find clusters of observations within a data set [26]. The HCA dendrogram was shown in Fig. 4, all 12 VOCs gas samples are accurately identified against each other. Remarkably, an excellent differentiation of closely related VOCs was achieved with the data shown in Fig. 3. It suggested that the CTL-based sensor array had commendable selectivity and immunity on recognition of VOCs. 3.2. Discrimination of drug precursor gas Many VOCs, such as ether, acetone, chloroform, and toluene, are usually important auxiliary materials utilized by drug trafficker even though they are not drugs of their own. Obviously, the detection and recognition of drug precursor gas is necessary. Selectivity is the ability of the array to distinguish one analyte from another, which is an important criterion in selecting a sensor array [27]. PCA can be used to extract the selective feature of original data according to variance criteria and
Fig. 5. (a) PCA score plot of four types of drug precursor gas. The concentrations of all the VOCs are 2000 ppm, working temperature: 210 ◦ C, flow rate of carrier gas: 240 mL/min. (b) PCA score plot of four types of drug precursor gas and two kinds of VOCs. The concentrations of all the VOCs are 4000 ppm, working temperature: 210 ◦ C, flow rate of carrier gas: 240 mL/min.
visualize the extracted feature. The PCA result of the four drug samples was shown in Fig. 5(a). Each analyte including ether (purity ≥ 99.7%), acetone (purity ≥ 99.5%), chloroform (purity ≥ 99.0%), toluene (purity ≥ 99.5%) formed a differentiable and tight cluster in the PCA score plot. Each chemical was tested for 5 times and the first three PCs accounted for 98.23% of the total variance. The sensor array was further applied to distinguish ether, acetone, chloroform, toluene, ethanol (purity ≥ 99.7%) and acetaldehyde (purity ≥ 99.5%) under the concentration of 4000 ppm. The PCA result was shown in Fig. 5(b). Each chemical was also tested for 5 times and the first three PCs were 67.94%, 25.18% and 5.16%. The three-dimensional PCA score plot showed clear clustering of 30 trials carried out for six different gases. The PCA results show that most of different groups were separated well, and hence proved the analytes discrimination capability of the CTLbased sensor array. The results of pattern recognition illustrated
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Fig. 6. The patterns of the sensor array at different working temperature. The concentrations of the compounds, 2000 ppm; flow rate of carrier gas: 240 mL/min.
Fig. 7. The calibration curves for ether and acetone at 210 ◦ C, and flow rate of carrier gas: 240 mL/min.
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Fig. 8. The CTL patterns of acetone during 0–90 h. The concentration of acetone is 2000 ppm, working temperature: 210 ◦ C, flow rate of carrier gas: 240 mL/min.
the excellent selectivity and repeatability of this sensor array. In this case, this sensor array could be applied for the discrimination of real drug samples containing enough amounts of these analytes. 3.3. Effect of working temperature Temperature is an important factor for optimization of CTLbased sensor array on account of the fact that catalytic reaction is temperature dependent. Different temperature may lead to different luminescence efficiencies during the course to discriminate the compounds with similar chemical properties [28,29]. The patterns of the sensor array at different working temperature were investigated by the CTL-based array imaging approach in our work. As shown in Fig. 6, the discrimination of ether, acetone, chloroform and toluene at different temperature was carried out. For chloroform and toluene the patterns with a relative weak brightness of spots were obtained at 190 ◦ C. When the temperature was increased to 210 ◦ C and 230 ◦ C, the brightness of the spots on the patterns was feasible to differentiate these four compounds. In the present study, a lower temperature of 210 ◦ C was selected as the working temperature for the discrimination of VOCs by the sensor array. The present work indicates different patterns can be obtained at different working temperature which may contribute to the distinguishing of VOCs. 3.4. Analytical characteristics and lifetime of the sensor array Further, characteristics of the CTL-based array performance of the four kinds of VOCs sensing were investigated under the optimal conditions. The array exhibits sensitive and stable CTL responses to ether, acetone, chloroform, and toluene, and the relative CTL intensity increases with the concentration of the four compounds. For example, responses of ether on the sensor utilized SiO2 /Fe3 O4 microspheres are shown in Fig. 7(a). The detection limit is 7 ppm and the linear range is 10–4000 ppm (R = 0.9985). In addition, responses of acetone on the sensor utilized Au/La2 O3 nanomaterial are shown in Fig. 7(b). The detection limit is 5 ppm and the linear range is 10–3000 ppm (R = 0.9975). The quantitative analysis indicates the sensor array can be used to quantify the concentration of given analyte by its CTL intensity. Most nanomaterial-based sensors have a long-term stability since the sensing materials are solid catalysts and essentially nonconsumable during the sensing process [21]. During the study, the stability of single sensor unit and the whole array were investigated. We found that the as-prepared CTL-based sensor array showed excellent stability and durability toward VOCs by testing the 12 kinds of common possible compounds under the optimal conditions. For example, the CTL intensities of SiO2 /Fe3 O4 sensor unit were collected every 2 h. The sensor exhibited good stability
and durability for continuously introducing 500 ppm ether for 100 h and the signal variation varied within ±8%. Moreover, the stability of the whole array was investigated. The CTL patterns of acetone during 0–90 h was shown in Fig. 8, most sensor units show no significant difference on CTL signal from 0 to 90 h. However, the signal of (1, 3) and (2, 4) shows a little decrease after 90 h. 4. Conclusions In conclusion, a catalytic nanomaterial-based CTL sensor array was developed for the discrimination and identification of VOCs. Different CTL patterns have been obtained for 12 types of VOCs and four drug precursor gases were successfully discriminated with the array. A good correlation between gas concentration and CTL intensity was obtained, which indicated the CTL intensity can be used in the evaluation of analyte concentration. The high sensitivity, long-term stability, and simplicity indicate the promising practical application of this sensor array. Acknowledgements This work was supported by the National Basic Research Program of China (2011CB933700), the National Natural Science Foundation of China (61163055) and the Natural Science Foundation of Jiangxi Province of China (20114BAB211017). References [1] L. Tang, Y.M. Li, K.L. Xu, X.D. Hou, Y. Lv, Sensitive and selective acetone sensor based on its cataluminescence from nano-La2 O3 surface, Sensors and Actuators B: Chemistry 132 (2008) 243–249. [2] M. Breysse, B. Claudel, L. Faure, M. Guenin, R.J.J. Williams, Chemiluminescense during the catalysis of carbon monoxide oxidation on a thoria surface, Journal of Catalysis 45 (1976) 137–144. [3] X.A. Cao, Z.Y. Zhang, X.R. Zhang, A novel gaseous acetaldehyde sensor utilizing cataluminescence on nanosized BaCO3 , Sensors and Actuators B: Chemistry 99 (2004) 30–35. [4] Z.M. Rao, L.J. Liu, J.Y. Xie, Y.Y. Zeng, Development of a benzene vapour sensor utilizing chemiluminescence on Y2 O3 , Luminescence 23 (2008) 163–168. [5] Y.Y. Wu, S.C. Zhang, X. Wang, N. Na, Z.X. Zhang, Development of a benzaldehyde sensor utilizing chemiluminescence on nanosized Y2 O3 , Luminescence 23 (2008) 376–380. [6] K.W. Zhou, X.L. Ji, N. Zhang, X.R. Zhang, On-line monitoring of formaldehyde in air by cataluminescence-based gas sensor, Sensors and Actuators B: Chemistry 119 (2006) 392–397. [7] Z.Y. Zhang, C. Zhang, X.R. Zhang, Development of a chemiluminescence ethanol sensor based on nanosized ZrO2 , Analyst 127 (2002) 792–796. [8] J. Hu, K.L. Xu, Y.Z. Jia, Y. Lv, Y.B. Li, X.D. Hou, Oxidation of ethyl ether on borate glass: chemiluminescence, mechanism, and development of a sensitive gas sensor, Analytical Chemistry 80 (2008) 7964–7969. [9] L.C. Zhang, Q. Zhou, Z.H. Liu, X.D. Hou, Y.B. Li, Y. Lv, Novel Mn3 O4 micro-octahedra: promising cataluminescence sensing material for acetone, Chemistry of Materials 21 (2009) 5066–5071. [10] R.K. Zhang, X.A. Cao, Y.H. Liu, X.Y. Chang, A new method for identifying compounds by luminescent response profiles on a cataluminescence based sensor, Analytical Chemistry 83 (2011) 8975–8983.
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Biographies Bo Li received his PhD in 2010 from Huazhong University of Science and Technology, China. Currently, he works as a lecturer at School of Information Engineering, East China Jiaotong University, China, and at same time doing postdoctoral research at Institute of Intelligent Machines, Chinese Academy of Sciences, China. His current research interests include pattern recognition, automatic detection and gas sensor. Juefu Liu received the BS degree from East China Institute of Technology, in 1982 and the MS degree in computer application from China University of Geosciences, in 1997. Currently, he works as a professor at School of Information Engineering, East China Jiaotong University. His current research interests include pattern recognition and automatic detection. Guolong Shi received his BS degree in 2010 from Hohai University, Nanjing, China. Now he is perusing his MS degree from University of Science and Technology of China, Hefei, Anhui. His research interests mainly are chemiluminescence and gas sensor. Jinhuai Liu received the BS degree in inorganic chemistry from Yunnan Agricultural University, China, in 1982 and the PhD degree in inorganic chemistry from Graduate University of Chinese Academy of Sciences, China, in 2003. Currently, he works as a researcher at Institute of Intelligent Machines, Chinese Academy of Sciences. His current research interests include biomimetric material, gas-sensing nanomaterials and nanodevice, sensing technology and their applications in detecting hazardous gases and drug/explosive.