globalchange  > 气候变化与战略
DOI: 10.1016/j.scib.2021.03.021
论文题名:
Volatile organic compounds sensing based on Bennet doubler-inspired triboelectric nanogenerator and machine learning-assisted ion mobility analysis
作者: Zhu J.; Sun Z.; Xu J.; Walczak R.D.; Dziuban J.A.; Lee C.
刊名: Science Bulletin
ISSN: 20959273
出版年: 2021
卷: 66, 期:12
起始页码: 1176
结束页码: 1185
语种: 英语
中文关键词: Ion mobility ; Machine learning ; Plasma discharge ; Triboelectric nanogenerator ; Volatile organic compounds
英文关键词: Electric discharges ; Gas chromatography ; Ion mobility spectrometers ; Machine learning ; Nanogenerators ; Triboelectricity ; Volatile organic compounds ; Doublers ; Fast response ; Gas-phases ; Ion Mobility ; Machine-learning ; Mobility analysis ; Nanogenerators ; Plasma discharge ; Triboelectric nanogenerator ; Volatile organics ; Ions
英文摘要: Ion mobility analysis is a well-known analytical technique for identifying gas-phase compounds in fast-response gas-monitoring systems. However, the conventional plasma discharge system is bulky, operates at a high temperature, and inappropriate for volatile organic compounds (VOCs) concentration detection. Therefore, we report a machine learning (ML)-enhanced ion mobility analyzer with a triboelectric-based ionizer, which offers good ion mobility selectivity and VOC recognition ability with a small-sized device and non-strict operating environment. Based on the charge accumulation mechanism, a multi-switched manipulation triboelectric nanogenerator (SM-TENG) can provide a direct current (DC) bias at the order of a few hundred, which can be further leveraged as the power source to obtain a unique and repeatable discharge characteristic of different VOCs, and their mixtures, with a special tip-plate electrode configuration. Aiming to tackle the grand challenge in the detection of multiple VOCs, the ML-enhanced ion mobility analysis method was successfully demonstrated by extracting specific features automatically from ion mobility spectrometry data with ML algorithms, which significantly enhance the detection ability of the SM-TENG based VOC analyzer, showing a portable real-time VOC monitoring solution with rapid response and low power consumption for future internet of things based environmental monitoring applications. © 2021 Science China Press
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/170531
Appears in Collections:气候变化与战略

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作者单位: School of Mechanical Engineering, Southeast University, Nanjing, 211189, China; Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576, Singapore; Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, Singapore, 117576, Singapore; NUS Suzhou Research Institute (NUSRI), Suzhou, 215123, China; Department of Mircroengineering and Photovoltaics, Wroclaw University of Science and Technology, Wroclaw 50-370, Poland; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, 119077, Singapore

Recommended Citation:
Zhu J.,Sun Z.,Xu J.,et al. Volatile organic compounds sensing based on Bennet doubler-inspired triboelectric nanogenerator and machine learning-assisted ion mobility analysis[J]. Science Bulletin,2021-01-01,66(12)
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