Discriminating BTX Molecules by the Nonselective Metal Oxide Sensor-Based Smart Sensing System
文献类型：期刊论文
| 作者 | Liu, Hongyu1,2; Meng, Gang3,4 ; Deng, Zanhong3,4; Nagashima, Kazuki5; Wang, Shimao3,4; Dai, Tiantian3,4; Li, Liang6; Yanagida, Takeshi5; Fang, Xiaodong1
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| 刊名 | ACS SENSORS
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| 出版日期 | 2021-11-26 |
| 卷号 | 6 |
| ISSN号 | 2379-3694 |
| 关键词 | BTX molecules xylene isomer classification temperature modulation deep learning algorithm smart sensing system |
| DOI | 10.1021/acssensors.1c01704 |
| 通讯作者 | Meng, Gang(mengggang@aiofm.ac.cn) ; Li, Liang(lli@suda.edu.cn) ; Fang, Xiaodong(fangxiaodong@sztu.edu.cn) |
| 英文摘要 | Discriminating structurally similar volatile organic compounds (VOCs) molecules, such as benzene, toluene, and three xylene isomers (BTX), remains a significant challenge, especially, for metal oxide semiconductor (MOS) sensors, in which selectivity is a long-standing challenge. Recent progress indicates that temperature modulation of a single MOS sensor offers a powerful route in extracting the features of adsorbed gas analytes than conventional isothermal operation. Herein, a rectangular heating waveform is applied on NiO-, WO3-, and SnO(2)(-)based sensors to gradually activate the specific gas/oxide interfacial redox reaction and generate rich (electrical) features of adsorbed BTX molecules. Upon several signal preprocessing steps, the intrinsic feature of BTX molecules can be extracted by the linear discrimination analysis (LDA) or convolutional neural network (CNN) analysis. The combination of three distinct MOS sensors noticeably benefits the recognition accuracy (with a reduced number of training iterations). Finally, a prototype of a smart BTX recognition system (including sensing electronics, sensors, Wi-Fi module, UI, PC, etc.) based on temperature modulation has been explored, which enables a prompt, accurate, and stable identification of xylene isomers in the ambient air background and raises the hope of innovating the future advanced machine olfactory system. |
| WOS关键词 | XYLENE ; TOLUENE ; BENZENE ; TEMPERATURE ; PERFORMANCE ; ARRAY |
| 资助项目 | National Natural Science Foundation of China[52025028] ; National Natural Science Foundation of China[11674324] ; National Natural Science Foundation of China[62075223] ; Natural Science Foundation of Top Talent of SZTU[2020101] ; CAS-JSPS Joint Research Projects[GJHZ1891] ; CAS Pioneer Hundred Talents Program from Chinese Academy of Sciences |
| WOS研究方向 | Chemistry ; Science & Technology - Other Topics |
| 语种 | 英语 |
| 出版者 | AMER CHEMICAL SOC |
| WOS记录号 | WOS:000755689100032 |
| 资助机构 | National Natural Science Foundation of China ; Natural Science Foundation of Top Talent of SZTU ; CAS-JSPS Joint Research Projects ; CAS Pioneer Hundred Talents Program from Chinese Academy of Sciences |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/127618]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Meng, Gang; Li, Liang; Fang, Xiaodong |
| 作者单位 | 1.Shenzhen Technol Univ, Coll New Mat & New Energies, Shenzhen 518118, Peoples R China 2.Shenzhen Univ, Sch Biomed Engn, Guangdong Key Lab Biomed Measurements & Ultrasoun, Hlth Sci Ctr, Shenzhen 518060, Peoples R China 3.Chinese Acad Sci, Anhui Inst Opt & Fine Mech, Hefei 230031, Peoples R China 4.Chinese Acad Sci, Key Lab Photovolta & Energy Conservat Mat, Hefei 230031, Peoples R China 5.Univ Tokyo, Grad Sch Engn, Dept Appl Chem, Tokyo 1138656, Japan 6.Soochow Univ, Sch Phys Sci & Technol, Jiangsu Key Lab Thin Films, Ctr Energy Convers Mat & Phys CECMP, Suzhou 215006, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Hongyu,Meng, Gang,Deng, Zanhong,et al. Discriminating BTX Molecules by the Nonselective Metal Oxide Sensor-Based Smart Sensing System[J]. ACS SENSORS,2021,6. |
| APA | Liu, Hongyu.,Meng, Gang.,Deng, Zanhong.,Nagashima, Kazuki.,Wang, Shimao.,...&Fang, Xiaodong.(2021).Discriminating BTX Molecules by the Nonselective Metal Oxide Sensor-Based Smart Sensing System.ACS SENSORS,6. |
| MLA | Liu, Hongyu,et al."Discriminating BTX Molecules by the Nonselective Metal Oxide Sensor-Based Smart Sensing System".ACS SENSORS 6(2021). |
入库方式： OAI收割
来源：合肥物质科学研究院
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