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Gas sensing behavior of a single tin dioxide sensor under dynamic temperature modulation
Sensors and Actuators B 99 (2004) 444–450
Gas sensing behavior of a single tin dioxide sensor under dynamic temperature modulation Xingjiu Huang a,b , Fanli Meng a , Zongxin Pi a , Weihong Xu a , Jinhuai Liu a,∗ b
a Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, PR China Department of Chemistry, University of Science and Technology of China, Hefei 230026, PR China
Received 29 September 2003; received in revised form 15 December 2003; accepted 15 December 2003
Abstract The gas sensing behavior of a single SnO2 gas sensor was investigated based on a dynamic measurement method in which temperature was modulated by heating waveform and frequency, and the results were compared with those of static measurement. The heating waveforms studied were rectangular, sinusoidal, triangular, saw-tooth, pulse, trapezoidal, and etc. However, the most essential factor was the modulated temperature itself, because any change in heating waveform and frequency resulted in the changes in surface temperature of the sensor element. © 2003 Elsevier B.V. All rights reserved. Keywords: Dynamic measurement; Gas sensing behavior; Influencing factors
1. Introduction Low cost tin oxide chemical sensors currently applied to gas detection still have some well-known problems (lack of selectivity, drift, etc.), which have been stimulating further active researches from various approaches such as materials design [1,2], different measurement strategies and signal processing algorithms. Two typical measurement strategies are employment of an array of multi-sensors and dynamic measurement based on a single sensor. The latter includes modulation in operating temperature by pulsed or oscillated heating. The modulation is well known to provide more information than static measurement with a mode of a constant operating temperature and enables us to identify certain gases such as H2 S, CO, NO2 , CO2 , ethanol, methane, n-butane, ethane, propane, propylene, ammonia [3–23]. However, most of the previous works from such viewpoints were conducted at a constant heating waveform and frequency for a certain preset operating temperature, and there were few reports on the effects of the modulated temperature combined with different heating waveforms and frequencies on dynamic responses.
∗ Corresponding author. Tel.: +86-551-5591132; fax: +86-551-5592420. E-mail address: [email protected] (J. Liu).
0925-4005/$ – see front matter © 2003 Elsevier B.V. All rights reserved. doi:10.1016/j.snb.2003.12.013
In our previous work, we reported rapid detection of pesticide residues using temperature modulation method by employing only a single sensor rather than an array of sensors. We also reported that the amplitude of the fast Fourier transformation (FFT) signal exhibited dynamic response curves characteristic not only of the species but also of the concentration of pesticide gases [24,25]. In the present paper, we report the advantages of the dynamic measurement, and discuss the influencing factors, such as modulated temperature, waveform (rectangular, triangular, saw-tooth, pulse, sinusoidal, and trapezoidal) and frequency of heater voltage, on the dynamic responses.
2. Experimental The thick film SnO2 sensors used in this study have been described previously [24,25]. Sample gases measured were butanone, acetone, ethanol, methanol, formaldehyde and cyclohexanone (Analytical standard, provided by Sigma–Aldrich Laborchemikalien Gmbh). A headspace sample (HP-7694) of them was injected into a 2500 ml test chamber, in which a single SnO2 gas sensor was placed. A circuit board for signal processing was made by ECU Electronics Industrial Co., Ltd. (No. 38 Research Institute of China Electronics & Technology Group Corp. China). Heating frequency and operating temperature settings were adjusted to give high sensitivity and good selectivity to
X. Huang et al. / Sensors and Actuators B 99 (2004) 444–450 waveform generator
.
synthetic dry air
at a constant rate of 10 cm3 /s. Data acquisition started at 80 s before injection of sample gases into the air-flow. The sampling rate was twice per second and it took several minutes to complete measurement. The surface temperature of the sensing element was measured with an infrared thermometer (Keyence, IT2-01, Japan).
data acquisition monitoring and storage
signal conditioning
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testing gas
. . 3. Results and discussion
test chamber
heater
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3.1. Static responses
Fig. 1. Experimental set-up.
The static responses of a SnO2 sensor to 0.5 ppm butanone, acetone, ethanol, methanol and formaldehyde at 300 ◦ C are shown in Fig. 2. It is clearly seen that the resistance of the sensing element changed obviously upon exposure to the organic gas, while the response time was
the pesticide gases measured. The sensor resistance was monitored, acquired and stored in a PC for further analysis. Fig. 1 shows the experimental set-up. The measurement process was as follows. Dry air as carrier gas was flowed 700
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dependent on the kind of sample gases. It is also noted, however, that except the changes in resistance and response time there was no other information about the response reactions. Thus, it is difficult to analyze the sensing mechanism of the butanone, acetone, ethanol and methanol. Except the response to formaldehyde, other responses curves were similar to each other. The former two molecules have a carbonyl group, while the latter two a hydroxyl group. Thus, we have no way to distinguish carbonyl and hydroxyl groups because of the lack of sufficient information. In other words, during the static measurements, only the resistance changes between initial and final stages can be monitored, and no further information is obtained during the response processes. 3.2. Dynamic responses Fig. 3 shows the dynamic responses of a SnO2 sensor to 0.5 ppm ethanol, methanol, formaldehyde and cyclohexanone under the conditions of applied potential 7 V, modulation frequency 20 mHz and a rectangular mode. Under these conditions, the temperature oscillated in the range of 210–300 ◦ C. In Fig. 3, it is worthy noting that more information was contained in the dynamic responses, so that the sample gases could be easily identified by the response characteristics. The dynamic response curves to ethanol and methanol with a hydroxyl group were similar to each other, but were differ-
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ent from those of formaldehyde with an aldehyde group and cyclohexanone with a ring carbonyl. The latter two were also different from each other. Thus, one could easily discriminate the gaseous species by the dynamic method, compared with the static method. It is widely accepted that oxygen molecules in air environment was chemisorbed in the forms of O2 − , O− and O2− . At a constant temperature, there exists an equilibrium state among the chemisorbed species on the surface of SnO2 : − 2− O2 ↔ O− 2 (ad) ↔ O (ad) ↔ O (ad). Semiconductor gas sensors monitor changes in conductance during the interaction of the sensing material with gas molecules to be detected. The reaction at low temperatures is mainly surface reactions, while bulk reactions between point defects in the SnO2 lattice and gaseous oxygen molecules become important at high temperatures. In both cases, the first step is the adsorption at active sites (intrinsic point defects like oxygen vacancies, and/or extrinsic point defects like segregated metal atoms), followed by some surface catalytic reactions. Similar reactions also occur at grain boundaries or at three-phase boundaries (e.g., at metallic contacts on surface metallic clusters). All of these reactions involve negatively charged oxygen adsorbates, either molecular (O2− ) or atomic (O− ), as well as hydroxyl groups (OH) at different surface sites. During the static measurement, the oxygen adsorbates are partly consumed by oxidation of target gases on the SnO2 surface. The decrease in the amount of chemisorbed
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Fig. 3. Dynamic responses to ethanol, methanol, formaldehyde and cyclohexanone with a rectangular temperature mode. Sampling dosage, 0.5 ppm; applied potential, 7 V; modulation frequency, 20 mHz.
X. Huang et al. / Sensors and Actuators B 99 (2004) 444–450
example, the operation temperature reached 450 ◦ C in the static mode using an applied potential of 7 V, whereas the dynamic temperature was in the range of 210–300 ◦ C under the rectangular heating with a frequency of 20 mHz.
oxygen induces an increase in conductance, as shown in Fig. 2. In the case of a dynamic mode of Fig. 3, where the sensor temperature was modulated with a rectangular heating mode, the complicated response transients are considered to be intimately related to different reaction kinetics of individual target molecules on the SnO2 surface. During the temperature modulation, any equilibriums no longer exist among the surface oxygen species. In this way reactions between reducing and oxidizing gases are dramatically influenced by the temperature modulation, and complicated response transients characteristic of individual target molecules are resulted. Thus, the method of temperature modulation was quite beneficial for identification of sample gases and analyzing the sensing mechanisms. The method was also beneficial from the viewpoint of low power consumption, compared with the static mode. For
3.3. Effect of the modulation temperature To optimize the selectivity of a temperature modulated sensor, it is necessary to obtain a relationship between the modulated sensor temperature and the conductance response to a specific gas. Fig. 4 shows the response transients to 0.5 ppm acetone at a constant frequency of 20 mHz in a rectangular mode when the temperature was modulated in various ranges. As seen in figure, different response transients were observed at different sensor temperatures. At lower temperatures (25–100 and 100–150 ◦ C), the response
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Fig. 5. Effect of modulation frequencies on the response transient to 0.5 ppm butanone at an applied potential of 7 V in a rectangular mode.
transients exhibited a simple increase and decrease in output signal periodically during the modulation, but they became very complicated and characteristic with increasing the overall sensor temperature, obviously due to various surface reaction mechanisms between acetone and chemisorbed oxygen. Anyway, it was possible to identify acetone from the characteristic response transients when the sensor temperature was modulated around 250– 300 ◦ C.
and 20 mHz, respectively; the temperature difference between highest and lowest during the modulation was largest (i.e., 90 ◦ C) at 20 mHz. The reaction mechanism of target gases would vary when the temperature was different. Thus, the effect of the modulation frequency may be understood in terms of the difference in the modulated surface temperature.
3.4. Effect of the modulation frequency at an applied potential of 7 V
The dynamic measurements mentioned above indicated a possibility to discriminate the organic species in air environment from the characteristic response curves, but the discrimination was sometimes obscure. To improve the discrimination, further studies were carried out in which different modulation waveforms were employed. The dynamic responses of methanol are shown in Fig. 6. It is obvious that the responses were different from each other. The results are in agreement with the report of Ortega et al. [21]. They reported that CO and CH4 could be detected by using pulse and triangular heating waveform. By analogy with the analysis of Sections 3.3 and 3.4, the changes in heating waveform may be ascribed to changes in surface temperature of the sensing element. Anyway, the heating waveform is an alternative parameter to make the discrimination of target gases more precise.
Fig. 5 shows the response transients to butanone at different modulation frequencies in a rectangular mode with an applied potential of 7 V. Obviously, modulation frequencies had a significant effect on the sensing behavior of the sensor. With decreasing the modulating frequency the output signal increased obviously. Response transients also markedly varied. At frequencies <20 mHz, however, no further changes were observed in the response characteristic. Here, it should be noted that the different modulation frequencies caused variations in surface temperature of the sensing element. Actually, the temperature varied in the ranges of 85–115, 125–180, 180–250 and 210–300 ◦ C for the modulation frequency of 50, 30, 25
3.5. Effect of modulation waveform
X. Huang et al. / Sensors and Actuators B 99 (2004) 444–450 a pulse technique
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Fig. 6. Effect of modulation waveform on the response transient to 0.5 ppm methanol at an applied potential of 7 V and a modulation frequency of 20 mHz.
4. Conclusion The study on the dynamic and static measurement has demonstrated the advantage of the former in maximizing the information extracted from a single gas sensor. It was shown that the dynamic measurement was beneficial for discrimination of gaseous molecules. A variety of influencing factors such as modulation temperature, frequency and heating waveform (rectangular, triangular, saw-tooth, pulse, sinusoidal, trapezoidal wave) were investigated in the dynamic measurement. As from the results, it was found that the optimum modulation temperature was 250–300 ◦ C at a heating frequency of 20 mHz for acetone detection. In addition, the modulation with a heating potential of 7 V at a frequency of 20 mHz resulted in the largest difference (90 ◦ C) in the modulated temperature, which was beneficial for detection of butanone. It was also shown that different waveforms could
modify the gas identification. However, the effects of heating waveform and frequency may be understood in terms of variations in the surface temperature of the sensor element.
Acknowledgements This work was financially supported by the National Natural Science Foundation of China (Project 60274061) and Anhui Province Natural Science Foundation (Project 01041404), which are gratefully acknowledged.
References [1] Z. Jiao, M. Wu, Z. Qin, M. Lu, J. Gu, The NO2 sensing ITO thin films prepared by ultrasonic spray pyrolysis, Sensors 3 (2003) 285–289.
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[2] Z. Jiao, M. Wu, J. Gu, X. Sun, The gas sensing characteristics of ITO thin film prepared by sol–gel method, Sens. Actuators B 94 (2003) 216–221. [3] Y. Hiranaka, T. Abe, H. Murata, Gas-dependent response in the temperature transient of SnO2 gas sensors, Sens. Actuators B 9 (1992) 177–182. [4] T. Amamoto, T. Yamaguchi, Y. Matsuura, Y. Kajiyama, Development of pulse-drive semiconductor gas sensor, Sens. Actuators B 13–14 (1993) 587–588. [5] J. Kelleter, C.-D. Kohi, H. Petig, Apparatus for measuring a state variable in a gas with at least one semiconductive gas sensor, US Patent 5668304 (16 September 1997). [6] R. Aigner, F. Auerbach, P. Huber, R. Muller, G. Scheller, Sinusoidal temperature modulation of the Si-planar-pellistor, Sens. Actuators B 18–19 (1994) 143–147. [7] S. Nakata, H. Nakamura, New strategy for the development of a gas sensor based on the dynamic characteristics: principle and preliminary experiment, Sens. Actuators B 8 (1992) 187–189. [8] S. Nakata, Y. Kaneda, H. Nakamura, K. Yoshikawa, Detection and quantification of CO gas based on the dynamic response of a ceramic sensor, Chem. Lett. (1991) 1505–1508. [9] P. Romppainen, V. Lantto, S. Leppavuori, Effect of water vapour on the CO response behaviour of tin dioxide sensors in constant temperature and temperature–pulsed modes of operation, Sens. Actuators B 1 (1990) 73–78. [10] V. Lantto, P. Romppainen, Response of SnO2 gas sensors to H2 S after quick cooling, J. Electrochem. Soc. 135 (1988) 2550–2556. [11] Y. Kato, K. Yoshikawa, M. Kitora, Temperature-dependent dynamic response enables the qualification and quantification of gases by a single sensor, Sens. Actuators B 40 (1997) 33–37. [12] N.-J. Choi, C.-H. Shim, K.-D. Song, K.-S. Lee, J.-S. Huh, K.-D. Lee, Classification of workplace gases using temperature modulation of two SnO2 sensing films on substrate, Sens. Actuators B 86 (2002) 251–258. [13] M. Roth, R. Hartinger, R. Faul, H.-E. Endres, Drift reduction of organic coated gas-sensors by temperature modulation, Sens. Actuators B 35–36 (1996) 358–362. [14] A.P. Lee, B.J. Reedy, Temperature modulation in semiconductor gas sensing, Sens. Actuators B 60 (1999) 35–42. [15] S. Nakata, E. Ozaki, N. Ojima, Gas sensing based on the dynamic non-linear responses of a semiconductor gas sensor: dependence on the range and frequency of a cyclic temperature change, Anal. Chim. Acta 361 (1998) 93–100. [16] S. Nakata, M. Nakasuji, N. Ojima, M. Kitora, Characteristic non-linear responses for gas species on the surface of different semiconductor gas sensors, Appl. Surf. Sci. 135 (1998) 285–292. [17] A. Fort, M. Gregorkiewitz, N. Machetti, S. Rocchi, B. Serrano, L. Tondi, N. Ulivieri, V. Vignoli, G. Faglia, E. Comini, Selectivity enhancement of SnO2 sensors by means of operating temperature modulation, Thin Solid Films 418 (2002) 2–8. [18] R. Aigner, M. Dietl, R. Katterloher, V. Klee, Si-planar-pellistor: designs for temperature modulated operation, Sens. Actuators B 33 (1996) 151–155.
[19] A. Heilig, N. Barsan, U. Weimar, M. Schweizer-Berverich, J.W. Gardner, W. Gopel, Gas identification by modulating temperatures of SnO2 -based thick film sensors, Sens. Actuators B 43 (1997) 45– 51. [20] R.E. Cavicchi, J.S. Suehle, K.G. Kreider, M. Gaitan, P. Chaparala, Optimized temperature–pulse sequences for the enhancement of chemically specific response patterns from micro-hotplate gas sensors, Sens. Actuators B 33 (1996) 142–146. [21] A. Ortega, S. Marco, A. Perera, T. Sundic, A. Pardo, J. Samitier, An intelligent detector based on temperature modulation of a gas sensor with a digital signal processor, Sens. Actuators B 78 (2001) 32–39. [22] R. Ionescu, E. Llobet, Wavelet transform-based fast feature extraction from temperature modulated semiconductor gas sensors, Sens. Actuators B 81 (2002) 289–295. [23] S. Nakata, N. Ojima, Detection of a sample gas in the presence of an interferant gas based on a non-linear dynamic response, Sens. Actuators B 56 (1999) 79–84. [24] X. Huang, J. Liu, D. Shao, Z. Pi, Z. Yu, Rectangular mode of operation for detecting pesticide residue by using a single SnO2 -based gas sensor, Sens. Actuators B 96 (2003) 630–635. [25] X. Huang, J. Liu, Z. Pi, Z. Yu, Detecting pesticide residue by using modulating temperature over a single SnO2 -based gas sensor, Sensors 3 (2003) 361–370.
Biographies Xingjiu Huang received his MS degree in electrochemistry from Wuhan University, China, in 2001. Since 2002, he has been a PhD student in Department of Chemistry at University of Science and Technology of China, China. His work focuses on the sensing materials and chemical sensors. Fanli Meng received his BS degree in School of Chemical Engineering from Nanjing University of Science and Technology, China, in 2002. Now, he is a MS student in Hefei Institute of Intelligent Machines, CAS, China. Zongxin Pi received his PhD degree in Department of Chemical Physics from University of Science and Technology of China, China, in 2001. Presently, he is a vice-professor in Hefei Institute of Intelligent Machines at Chinese Academy of Sciences, China. His current fields of interest includes non-linear dynamics in semiconductor gas sensors, intelligent and sensing materials and chemical sensors. Weihong Xu received his BS degree in Wuhan Institute of Chemical Technology, China, in 1997. He is now a MS student in Hefei Institute of Intelligent Machines, CAS, China. Jinhuai Liu received his BS Degree in Department of Chemistry from Yunnan Agricultural University, China, in 1982. He is currently a professor at the Hefei Institute of Intelligent Machines, CAS, China. He has performed research on semiconductor gas sensor since 1982, and also performed a research in the field of intelligent materials.
Gas sensing behavior of a single tin dioxide sensor under dynamic temperature modulation Xingjiu Huang a,b , Fanli Meng a , Zongxin Pi a , Weihong Xu a , Jinhuai Liu a,∗ b
a Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, PR China Department of Chemistry, University of Science and Technology of China, Hefei 230026, PR China
Received 29 September 2003; received in revised form 15 December 2003; accepted 15 December 2003
Abstract The gas sensing behavior of a single SnO2 gas sensor was investigated based on a dynamic measurement method in which temperature was modulated by heating waveform and frequency, and the results were compared with those of static measurement. The heating waveforms studied were rectangular, sinusoidal, triangular, saw-tooth, pulse, trapezoidal, and etc. However, the most essential factor was the modulated temperature itself, because any change in heating waveform and frequency resulted in the changes in surface temperature of the sensor element. © 2003 Elsevier B.V. All rights reserved. Keywords: Dynamic measurement; Gas sensing behavior; Influencing factors
1. Introduction Low cost tin oxide chemical sensors currently applied to gas detection still have some well-known problems (lack of selectivity, drift, etc.), which have been stimulating further active researches from various approaches such as materials design [1,2], different measurement strategies and signal processing algorithms. Two typical measurement strategies are employment of an array of multi-sensors and dynamic measurement based on a single sensor. The latter includes modulation in operating temperature by pulsed or oscillated heating. The modulation is well known to provide more information than static measurement with a mode of a constant operating temperature and enables us to identify certain gases such as H2 S, CO, NO2 , CO2 , ethanol, methane, n-butane, ethane, propane, propylene, ammonia [3–23]. However, most of the previous works from such viewpoints were conducted at a constant heating waveform and frequency for a certain preset operating temperature, and there were few reports on the effects of the modulated temperature combined with different heating waveforms and frequencies on dynamic responses.
∗ Corresponding author. Tel.: +86-551-5591132; fax: +86-551-5592420. E-mail address: [email protected] (J. Liu).
0925-4005/$ – see front matter © 2003 Elsevier B.V. All rights reserved. doi:10.1016/j.snb.2003.12.013
In our previous work, we reported rapid detection of pesticide residues using temperature modulation method by employing only a single sensor rather than an array of sensors. We also reported that the amplitude of the fast Fourier transformation (FFT) signal exhibited dynamic response curves characteristic not only of the species but also of the concentration of pesticide gases [24,25]. In the present paper, we report the advantages of the dynamic measurement, and discuss the influencing factors, such as modulated temperature, waveform (rectangular, triangular, saw-tooth, pulse, sinusoidal, and trapezoidal) and frequency of heater voltage, on the dynamic responses.
2. Experimental The thick film SnO2 sensors used in this study have been described previously [24,25]. Sample gases measured were butanone, acetone, ethanol, methanol, formaldehyde and cyclohexanone (Analytical standard, provided by Sigma–Aldrich Laborchemikalien Gmbh). A headspace sample (HP-7694) of them was injected into a 2500 ml test chamber, in which a single SnO2 gas sensor was placed. A circuit board for signal processing was made by ECU Electronics Industrial Co., Ltd. (No. 38 Research Institute of China Electronics & Technology Group Corp. China). Heating frequency and operating temperature settings were adjusted to give high sensitivity and good selectivity to
X. Huang et al. / Sensors and Actuators B 99 (2004) 444–450 waveform generator
.
synthetic dry air
at a constant rate of 10 cm3 /s. Data acquisition started at 80 s before injection of sample gases into the air-flow. The sampling rate was twice per second and it took several minutes to complete measurement. The surface temperature of the sensing element was measured with an infrared thermometer (Keyence, IT2-01, Japan).
data acquisition monitoring and storage
signal conditioning
445
computer
testing gas
. . 3. Results and discussion
test chamber
heater
sensing film
waste gas
3.1. Static responses
Fig. 1. Experimental set-up.
The static responses of a SnO2 sensor to 0.5 ppm butanone, acetone, ethanol, methanol and formaldehyde at 300 ◦ C are shown in Fig. 2. It is clearly seen that the resistance of the sensing element changed obviously upon exposure to the organic gas, while the response time was
the pesticide gases measured. The sensor resistance was monitored, acquired and stored in a PC for further analysis. Fig. 1 shows the experimental set-up. The measurement process was as follows. Dry air as carrier gas was flowed 700
1000 900
off
600
output voltage/mV
output voltage/mV
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dependent on the kind of sample gases. It is also noted, however, that except the changes in resistance and response time there was no other information about the response reactions. Thus, it is difficult to analyze the sensing mechanism of the butanone, acetone, ethanol and methanol. Except the response to formaldehyde, other responses curves were similar to each other. The former two molecules have a carbonyl group, while the latter two a hydroxyl group. Thus, we have no way to distinguish carbonyl and hydroxyl groups because of the lack of sufficient information. In other words, during the static measurements, only the resistance changes between initial and final stages can be monitored, and no further information is obtained during the response processes. 3.2. Dynamic responses Fig. 3 shows the dynamic responses of a SnO2 sensor to 0.5 ppm ethanol, methanol, formaldehyde and cyclohexanone under the conditions of applied potential 7 V, modulation frequency 20 mHz and a rectangular mode. Under these conditions, the temperature oscillated in the range of 210–300 ◦ C. In Fig. 3, it is worthy noting that more information was contained in the dynamic responses, so that the sample gases could be easily identified by the response characteristics. The dynamic response curves to ethanol and methanol with a hydroxyl group were similar to each other, but were differ-
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ethanol
methanol 2000
2000
output voltage/mV
output votage/mV
2500
ent from those of formaldehyde with an aldehyde group and cyclohexanone with a ring carbonyl. The latter two were also different from each other. Thus, one could easily discriminate the gaseous species by the dynamic method, compared with the static method. It is widely accepted that oxygen molecules in air environment was chemisorbed in the forms of O2 − , O− and O2− . At a constant temperature, there exists an equilibrium state among the chemisorbed species on the surface of SnO2 : − 2− O2 ↔ O− 2 (ad) ↔ O (ad) ↔ O (ad). Semiconductor gas sensors monitor changes in conductance during the interaction of the sensing material with gas molecules to be detected. The reaction at low temperatures is mainly surface reactions, while bulk reactions between point defects in the SnO2 lattice and gaseous oxygen molecules become important at high temperatures. In both cases, the first step is the adsorption at active sites (intrinsic point defects like oxygen vacancies, and/or extrinsic point defects like segregated metal atoms), followed by some surface catalytic reactions. Similar reactions also occur at grain boundaries or at three-phase boundaries (e.g., at metallic contacts on surface metallic clusters). All of these reactions involve negatively charged oxygen adsorbates, either molecular (O2− ) or atomic (O− ), as well as hydroxyl groups (OH) at different surface sites. During the static measurement, the oxygen adsorbates are partly consumed by oxidation of target gases on the SnO2 surface. The decrease in the amount of chemisorbed
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example, the operation temperature reached 450 ◦ C in the static mode using an applied potential of 7 V, whereas the dynamic temperature was in the range of 210–300 ◦ C under the rectangular heating with a frequency of 20 mHz.
oxygen induces an increase in conductance, as shown in Fig. 2. In the case of a dynamic mode of Fig. 3, where the sensor temperature was modulated with a rectangular heating mode, the complicated response transients are considered to be intimately related to different reaction kinetics of individual target molecules on the SnO2 surface. During the temperature modulation, any equilibriums no longer exist among the surface oxygen species. In this way reactions between reducing and oxidizing gases are dramatically influenced by the temperature modulation, and complicated response transients characteristic of individual target molecules are resulted. Thus, the method of temperature modulation was quite beneficial for identification of sample gases and analyzing the sensing mechanisms. The method was also beneficial from the viewpoint of low power consumption, compared with the static mode. For
3.3. Effect of the modulation temperature To optimize the selectivity of a temperature modulated sensor, it is necessary to obtain a relationship between the modulated sensor temperature and the conductance response to a specific gas. Fig. 4 shows the response transients to 0.5 ppm acetone at a constant frequency of 20 mHz in a rectangular mode when the temperature was modulated in various ranges. As seen in figure, different response transients were observed at different sensor temperatures. At lower temperatures (25–100 and 100–150 ◦ C), the response
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transients exhibited a simple increase and decrease in output signal periodically during the modulation, but they became very complicated and characteristic with increasing the overall sensor temperature, obviously due to various surface reaction mechanisms between acetone and chemisorbed oxygen. Anyway, it was possible to identify acetone from the characteristic response transients when the sensor temperature was modulated around 250– 300 ◦ C.
and 20 mHz, respectively; the temperature difference between highest and lowest during the modulation was largest (i.e., 90 ◦ C) at 20 mHz. The reaction mechanism of target gases would vary when the temperature was different. Thus, the effect of the modulation frequency may be understood in terms of the difference in the modulated surface temperature.
3.4. Effect of the modulation frequency at an applied potential of 7 V
The dynamic measurements mentioned above indicated a possibility to discriminate the organic species in air environment from the characteristic response curves, but the discrimination was sometimes obscure. To improve the discrimination, further studies were carried out in which different modulation waveforms were employed. The dynamic responses of methanol are shown in Fig. 6. It is obvious that the responses were different from each other. The results are in agreement with the report of Ortega et al. [21]. They reported that CO and CH4 could be detected by using pulse and triangular heating waveform. By analogy with the analysis of Sections 3.3 and 3.4, the changes in heating waveform may be ascribed to changes in surface temperature of the sensing element. Anyway, the heating waveform is an alternative parameter to make the discrimination of target gases more precise.
Fig. 5 shows the response transients to butanone at different modulation frequencies in a rectangular mode with an applied potential of 7 V. Obviously, modulation frequencies had a significant effect on the sensing behavior of the sensor. With decreasing the modulating frequency the output signal increased obviously. Response transients also markedly varied. At frequencies <20 mHz, however, no further changes were observed in the response characteristic. Here, it should be noted that the different modulation frequencies caused variations in surface temperature of the sensing element. Actually, the temperature varied in the ranges of 85–115, 125–180, 180–250 and 210–300 ◦ C for the modulation frequency of 50, 30, 25
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X. Huang et al. / Sensors and Actuators B 99 (2004) 444–450 a pulse technique
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4. Conclusion The study on the dynamic and static measurement has demonstrated the advantage of the former in maximizing the information extracted from a single gas sensor. It was shown that the dynamic measurement was beneficial for discrimination of gaseous molecules. A variety of influencing factors such as modulation temperature, frequency and heating waveform (rectangular, triangular, saw-tooth, pulse, sinusoidal, trapezoidal wave) were investigated in the dynamic measurement. As from the results, it was found that the optimum modulation temperature was 250–300 ◦ C at a heating frequency of 20 mHz for acetone detection. In addition, the modulation with a heating potential of 7 V at a frequency of 20 mHz resulted in the largest difference (90 ◦ C) in the modulated temperature, which was beneficial for detection of butanone. It was also shown that different waveforms could
modify the gas identification. However, the effects of heating waveform and frequency may be understood in terms of variations in the surface temperature of the sensor element.
Acknowledgements This work was financially supported by the National Natural Science Foundation of China (Project 60274061) and Anhui Province Natural Science Foundation (Project 01041404), which are gratefully acknowledged.
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Biographies Xingjiu Huang received his MS degree in electrochemistry from Wuhan University, China, in 2001. Since 2002, he has been a PhD student in Department of Chemistry at University of Science and Technology of China, China. His work focuses on the sensing materials and chemical sensors. Fanli Meng received his BS degree in School of Chemical Engineering from Nanjing University of Science and Technology, China, in 2002. Now, he is a MS student in Hefei Institute of Intelligent Machines, CAS, China. Zongxin Pi received his PhD degree in Department of Chemical Physics from University of Science and Technology of China, China, in 2001. Presently, he is a vice-professor in Hefei Institute of Intelligent Machines at Chinese Academy of Sciences, China. His current fields of interest includes non-linear dynamics in semiconductor gas sensors, intelligent and sensing materials and chemical sensors. Weihong Xu received his BS degree in Wuhan Institute of Chemical Technology, China, in 1997. He is now a MS student in Hefei Institute of Intelligent Machines, CAS, China. Jinhuai Liu received his BS Degree in Department of Chemistry from Yunnan Agricultural University, China, in 1982. He is currently a professor at the Hefei Institute of Intelligent Machines, CAS, China. He has performed research on semiconductor gas sensor since 1982, and also performed a research in the field of intelligent materials.