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Zhu J, Sun Z, Xu J, Walczak RD, Dziuban JA, Lee C. Volatile organic compounds sensing based on Bennet doubler-inspired triboelectric nanogenerator and machine learning-assisted ion mobility analysis. Sci Bull (Beijing) 2021; 66:1176-1185. [PMID: 36654355 DOI: 10.1016/j.scib.2021.03.021] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 02/04/2021] [Accepted: 03/15/2021] [Indexed: 02/07/2023]
Abstract
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.
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Affiliation(s)
- Jianxiong Zhu
- 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
| | - Zhongda Sun
- 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
| | - Jikai Xu
- 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
| | - Rafal D Walczak
- Department of Mircroengineering and Photovoltaics, Wroclaw University of Science and Technology, Wroclaw 50-370, Poland
| | - Jan A Dziuban
- Department of Mircroengineering and Photovoltaics, Wroclaw University of Science and Technology, Wroclaw 50-370, Poland
| | - Chengkuo Lee
- 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; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore 119077, Singapore.
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