Volatile organic compounds sensing based on Bennet doubler-inspired triboelectric nanogenerator and machine learning-assisted ion mobility analysis

Jianxiong Zhu; Zhongda Sun; Jikai Xu; Rafal D Walczak; Jan A Dziuban; Chengkuo Lee

doi:10.1016/j.scib.2021.03.021

Volatile organic compounds sensing based on Bennet doubler-inspired triboelectric nanogenerator and machine learning-assisted ion mobility analysis

Sci Bull (Beijing). 2021 Jun 30;66(12):1176-1185. doi: 10.1016/j.scib.2021.03.021. Epub 2021 Mar 23.

Authors

Jianxiong Zhu¹, Zhongda Sun², Jikai Xu², Rafal D Walczak³, Jan A Dziuban³, Chengkuo Lee⁴

Affiliations

¹ 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.
² 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.
³ Department of Mircroengineering and Photovoltaics, Wroclaw University of Science and Technology, Wroclaw 50-370, Poland.
⁴ 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. Electronic address: elelc@nus.edu.sg.

PMID: 36654355
DOI: 10.1016/j.scib.2021.03.021

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.

Keywords: Ion mobility; Machine learning; Plasma discharge; Triboelectric nanogenerator; Volatile organic compounds.