二氧化锡传感器对挥发性有机物的动态测试方法研究

孟凡利; 季瀚洋; 苑振宇; 张华; 王稼鹏

doi:10.16383/j.aas.c190561

二氧化锡传感器对挥发性有机物的动态测试方法研究

doi: 10.16383/j.aas.c190561

1.
东北大学信息科学与工程学院沈阳 110819

基金项目: 国家自然科学基金(61833006, 61673367, 61973058), 教育部中央高校基本科研业务费项目(N180408018, N2004028), 辽宁省“兴辽英才计划”项目(XLYC1807198), 辽宁省自然科学基金(20180550483)资助

详细信息

作者简介:
孟凡利：东北大学信息科学与工程学院教授. 2009年获中国科学技术大学博士学位. 主要研究方向为传感材料和纳米传感器. 本文通信作者. E-mail: mengfanli@ise.neu.edu.cn

季瀚洋：东北大学信息科学与工程学院硕士研究生. 2018年获得山东建筑大学工学和艺术学双学士学位. 主要研究方向为半导体传感器的温度调制动态测试方法.E-mail: jihy1996@sina.cn

苑振宇：东北大学信息科学与工程学院副教授. 2014年获哈尔滨工业大学博士学位. 主要研究方向为纳米结构气体传感器和微机电系统. E-mail: yuanzhenyu@ise.neu.edu.cn

张华：东北大学信息科学与工程学院副教授. 2007年获东北大学博士学位. 主要研究方向为智能信息的处理、分析和识别.E-mail: zhanghua@ise.neu.edu.cn

王稼鹏：2019 年获得东北大学工学学士学位. 主要研究方向为半导体传感器的温度调制动态测试方法. E-mail: wjp19961028@163.com

中图分类号: TP212.2
计量
- 文章访问数: 501
- HTML全文浏览量: 272
- PDF下载量: 108
- 被引次数: 0
出版历程
- 收稿日期: 2019-07-31
- 录用日期: 2020-04-07
- 网络出版日期: 2022-02-11
- 刊出日期: 2022-03-25

Study on Dynamic Testing Method of Volatile Organic Compounds by Tin Dioxide Sensor

1.
School of Information Science and Engineering, Northeastern University, Shenyang 110819

Funds: Supported by National Natural Science Foundation of China (61833006, 61673367, 61973058), Fundamental Research Funds for the Central Universities in China (N180408018, N2004028), Liaoning Revitalization Talents Program (XLYC1807198), and Liaoning Province Natural Science Foundation (20180550483)

More Information

Author Bio:
MENG Fan-Li　Professor at the School of Information Science and Engineering, Northeastern University. He received his Ph.D. degree at University of Science and Technology of China in 2009. His research interest covers sensing materials and nanosensors. Corresponding author of this paper

JI Han-Yang　Master student at the School of Information Science and Engineering, Northeastern University. He received his bachelor degree in engineering and art from Shandong University of Architecture in 2018. His research interest covers dynamic measurement method of temperature modulation for semiconductor sensors

YUAN Zhen-Yu　Associate professor at the School of Information Science and Engineering, Northeastern University. He received his Ph.D. degree at Harbin Institute of Technology in 2014. His research interest covers nanostructured gas sensor and microelectromechanical systems (MEMS)

ZHANG Hua　Associate professor at the School of Information Science and Engineering, Northeastern University. She received his Ph.D. degree at Northeastern University, in 2007. Her research interste covers processing, analysis and identification of intelligent information

WANG Jia-Peng　Received his bachelor degree in engineering from Northeastern University in 2019. His research interest covers dynamic measurement method of temperaturemodulation for semiconductor sensors

摘要

摘要: 温度调制的动态测试是解决金属氧化物传感器选择性差的一种常用方法, 但至今尚未有明确的方法控制动态响应信号的波形以达到预期期望. 本文首先从静态测试的角度出发, 描述了静态性能指标与动态响应信号的对应关系, 提出了适合于动态测试的半导体传感器的选择方法. 然后以矩形波为例, 通过对其周期、占空比、工作温度范围的调整, 在不降低动态响应信号品质的前提下, 缩短在实际应用中的响应时间和功耗. 最后, 利用支持向量机算法验证了动态响应信号的品质, 在不同种类不同浓度的气体中, 识别率高达100%.
- 半导体气体传感器 /
- 静态性能指标 /
- 动态响应信号 /
- 响应时间 /
- 功耗
Abstract: Dynamic testing of temperature modulation is a common method to improve the selectivity of metal oxide sensors, but so far there is no clear way to control the waveform of dynamic response signal to reach the expected expectation. This paper first describes the corresponding relationship between static performance index and dynamic response signal from the perspective of static testing, and proposes the selection method of semiconductor sensor suitable for dynamic testing. Then, taking rectangular wave as an example, the response time and power consumption in practical applications are shortened by adjusting its period, duty cycle and operating temperature range without reducing the quality of dynamic response signal. Finally, support vector machine algorithm is used to verify the quality of dynamic response signal. In different kinds of gases with different concentrations, the recognition rate can reach 100%.
- Semiconductor gas sensor /
- static performance index /
- dynamic response signal /
- response time /
- power consumption

HTML全文

图 1 传感器结构示意图和气敏测试装置示意图

Fig. 1 Schematic diagram of sensor structure and Schematic diagram of gas sensing test device

下载: 全尺寸图片幻灯片

图 2 丙酮、甲醛、甲酸、乙酸丁酯和乙醇在不同温度下的气敏响应

Fig. 2 Gas sensing response of acetone, formaldehyde, formic acid, butyl acetate, and ethanol at different temperatures

下载: 全尺寸图片幻灯片

图 3 丙酮、甲醛、甲酸、乙酸丁酯和乙醇在最佳工作温度下的静态响应曲线

Fig. 3 Static response curve of acetone, formaldehyde, formic acid, butyl acetate, and ethanol at the optimum operating temperature

下载: 全尺寸图片幻灯片

图 4 丙酮、甲醛、甲酸、乙酸丁酯、乙醇在不同浓度下的气敏响应和SnO₂的长期稳定性测试

Fig. 4 Gas sensitive response of acetone, formaldehyde, formic acid, butyl acetate, and ethanol at different concentrations and long term stability test of SnO₂

下载: 全尺寸图片幻灯片

图 5 在施加3 ~ 7 V电压、周期为80 s的三角波时, 丙酮、甲醛、甲酸、乙酸丁酯和乙醇的动态响应信号

Fig. 5 Dynamic response signals of acetone, formaldehyde, formic acid, butyl acetate, and ethanol under the triangular wave with a period of 80 s and a voltage of 3 ~ 7 V

下载: 全尺寸图片幻灯片

图 6 100 ppm乙醇在400 ℃下的复现性测试结果

Fig. 6 Reproducibility test results of 100 ppm ethanol at 400 ℃

下载: 全尺寸图片幻灯片

图 7 不同周期和占空比的矩形波的动态响应信号

Fig. 7 Dynamic response signal of rectangular wave with different period and different duty cycle

下载: 全尺寸图片幻灯片

图 8 温度范围分别为90 ~ 350 ℃, 120 ~ 350 ℃, 150 ~ 350 ℃, 180 ~ 350 ℃的矩形波的动态响应信号

Fig. 8 Dynamic response signals of rectangular wave with temperature range of 90 ~ 350 ℃, 120 ~ 350 ℃, 150 ~ 350 ℃, and 180 ~ 350 ℃, respectively

下载: 全尺寸图片幻灯片

图 9 温度范围分别为120 ~ 320 ℃, 120 ~ 350 ℃和120 ~ 380 ℃的矩形波的动态响应周期重复性信号

Fig. 9 Dynamic response periodic repetitive signal of rectangular wave with temperature range of 120 ~ 320 ℃, 120 ~ 350 ℃, and 120 ~ 380, respectively

下载: 全尺寸图片幻灯片

图 10 丙酮、甲醛、甲酸、乙酸丁酯和乙醇的动态响应梯度信号

Fig. 10 Dynamic response gradient signals of acetone, formaldehyde, formic acid, butyl acetate, and ethanol

下载: 全尺寸图片幻灯片

图 11 支持向量机的预测结果

Fig. 11 Prediction results of SVM

下载: 全尺寸图片幻灯片

参考文献(25)

[1]	蔡朋程. 浅析中国的酸雨分布现状及其成因. 科技资讯, 2018, 16(15): 133-134. Cai Peng-cheng. A brief analysis of the distribution status and causes of acid rain in China. Science and Technology Information, 2018, 16(15): 133-134.
[2]	杨威. 大连市VOC现状调查与研究. 环境与可持续发展, 2014(5): 175-177. doi: 10.3969/j.issn.1673-288X.2014.05.051 Yang Wei. Investigation and research on the present situation of VOC in Dalian. Environment and Sustainable Development, 2014(5): 175-177. doi: 10.3969/j.issn.1673-288X.2014.05.051
[3]	Petrie B, Camachomuñoz D, Castrignanò E. Chiral liquid chromatography coupled with tandem mass spectrometry for environmental analysis of pharmacologically active compounds. Metabolomics, 2015, 11(6): 1514-1525. doi: 10.1007/s11306-015-0807-6
[4]	Agapiou A, Vamvakari J P, Andrianopoulos A. Volatile emissions during storing of green food waste under different aeration conditions. Environ Sci Pollut Res Int, 2016, 23(9): 8890-8901. doi: 10.1007/s11356-016-6131-5
[5]	王颖. 无线技术、人工智能和传感器成为智能家居驱动因素. 中国电子商情(基础电子), 2018, 1064(07): 35-36. Wang Ying. Wireless technology, artificial intelligence and sensors become smart home drivers. China Electronic Business (basic Electronics), 2018. 1064(07): 35-36.
[6]	韩卫济, 孙鹤, 徐光. 气体传感器综述. 计算机产品与流通, 2018(02): 279. Han Wei-ji, Sun he, Xu Guang. Review of gas sensors. Computer products and Circulation, 2018(02): 279.
[7]	徐甲强, 秦建华, 安春仙, 等. 半导体气体传感器的选择性研究. 传感器世界, 1997(8): 7-12. Xu Ji-qiang, Qin Jian-hua, An Chun-xian. Study on selectivity of semiconductor gas sensors. Sensor World, 1997(8): 7-12.
[8]	于洋, 赵文杰, 王欣, 等. 半导体气体传感器动态温度调制系统设计及检测方法研究. 传感技术学报, 2016, 29(9): 1365-1371. doi: 10.3969/j.issn.1004-1699.2016.09.012 Yu Yang, Zhao Wen-jie, Wang Xin. Design and detection method of dynamic temperature modulation system for semiconductor gas sensor. Journal of Sensing Technology, 2016, 29(9): 1365-1371. doi: 10.3969/j.issn.1004-1699.2016.09.012
[9]	魏广芬, 唐祯安, 王永强. 气体传感器的温度调制技术研究进展. 传感器与微系统, 2010, 29(5): 1-4. doi: 10.3969/j.issn.1000-9787.2010.05.001 Wei Guang-fen, Tang Zhen-an, Wang Yong-qiang. Research progress in temperature modulation of gas sensor. Sensor and Microsystem, 2010, 29(5): 1-4. doi: 10.3969/j.issn.1000-9787.2010.05.001
[10]	Huang J R, Gu C P, Meng F L. Detection of volatile organic compounds by using a single temperature-modulated SnO₂ gas sensor and artificial neural network. Smart Materials & Structures, 2007, 16(3): 701.
[11]	Liu H, He Y, Nagashima K. Discrimination of VOCs molecules via extracting concealed features from a temperature-modulated p-type NiO sensor. Sensors and Actuators, 2019, 293(8): 342-349.
[12]	Alinoori A H, Masoum S. Multicapillary gas chromatography-temperature modulated metal oxide semiconductor sensors array detector for monitoring of volatile organic compounds in closed atmosphere using gaussian apodization factor analysis. Analytical Chemistry, 2018, 90(11): 6635-6642. doi: 10.1021/acs.analchem.8b00426
[13]	Li F A, Jin H, Wang J X. Selective Sensing of Gas Mixture via a Temperature Modulation Approach: New Strategy for Potentiometric Gas Sensor Obtaining Satisfactory Discriminating Features. Sensors, 2017, 17(3): 573-582. doi: 10.3390/s17030573
[14]	Vergara A, Llobet E, Brezmes J. Optimized temperature modulation of micro-hotplate gas sensors through pseudorandom binary sequences. IEEE Sensors Journal, 2005, 5(6): 1369-1378. doi: 10.1109/JSEN.2005.855605
[15]	Vergara A, Llobet E, Brezmes J. Optimised temperature modulation of metal oxide micro-hotplate gas sensors through multilevel pseudo random sequences. Sensors and Actuators B(Chemical), 2005, 111-112(none): 271-280.
[16]	Martinelli E, Polese D, Catini A. Self-adapted temperature modulation in metal-oxide semiconductor gas sensors. Sensors and Actuators: B (Chemical), 2012, 161(1): 534-541. doi: 10.1016/j.snb.2011.10.072
[17]	Polese D, Martinelli E, Catini A. Self-adaptive thermal modulation of gas sensors. Procedia Engineering, 2010, 5(none): 156-159.
[18]	Durán C, Juan B, Jeniffer C. Response Optimization of a Chemical Gas Sensor Array using Temperature Modulation. Electronics, 2018, 7(4): 54-68. doi: 10.3390/electronics7040054
[19]	Rai S K, Kao K W, Agarwal A. Platinum Coating on an Ultrathin InN Epilayer as a Dual Gas Sensor for Selective Sensing of Ammonia and Acetone by Temperature Modulation for Liver Malfunction and Diabetes Applications. Ecs Journal of Solid State Science and Technology, 2018, 7(7): Q3221-Q3229. doi: 10.1149/2.0311807jss
[20]	安文, 魏广芬. 气体传感器温度调制信号的瞬时频谱分析. 传感技术学报, 2012, 25(6): 782-788. doi: 10.3969/j.issn.1004-1699.2012.06.013 An Wen, Wei Guang-fen. Instantaneous spectrum analysis of temperature modulation signal of gas sensor. Journal of Sensing Technology, 2012, 25(6): 782-788. doi: 10.3969/j.issn.1004-1699.2012.06.013
[21]	汪晓东. 基于支持向量机的气敏传感器阵列信号处理方法. 仪器仪表学报, 2005, 26(8): 871-875. doi: 10.3321/j.issn:0254-3087.2005.08.026 Wang Xiao-dong. Gas sensor array signal processing method based on support vector machine. Journal of Instruments, 2005, 26(8): 871-875. doi: 10.3321/j.issn:0254-3087.2005.08.026
[22]	张博, 杨云祥. SnO₂纳米结构气体传感器制备与气敏特性研究. 传感器与微系统, 2019, 38(03): 27-30. Zhang Bo, Yang Yun-xiang. Preparation and gas sensing properties of Sno₂ nanostructured gas sensors. Sensors and Microsystems, 2019, 38(03): 27-30.
[23]	Yuan A Y, Zhang J J, Meng F L. Highly sensitive ammonia sensors based on Ag-decorated WO₃ nanorods. IEEE Transactions on Nanotechnology, 2018, 17(6): 1252-1258. doi: 10.1109/TNANO.2018.2871675
[24]	马俊锋, 曾毅. Au纳米颗粒负载SnO₂双层空心立方体的CO气敏性能. 高等学校化学学报, 2018, 39(09): 19-26. Ma Jun-feng, Zeng Yi. CO gas sensing properties of SnO₂ double-layer hollow cubes loaded with Au nanoparticles. Journal of Chemistry of Colleges and Universities, 2018, 39(09): 19-26.
[25]	杨洁, 王莹, 王泽鹏. Ni掺杂SnO₂花状微结构的制备及其气敏特性研究. 传感技术学报, 2017, 30(12): 46-51. Yang Jie, Wang Ying, Wang Ze-peng. Preparation and gas sensing properties of flower-like microstructure of Ni-doped SnO₂. Journal of Sensing Technology, 2017, 30(12): 46-51.