摘要
Ion mobility analysis is a well-known analytical technique for identifying gas-phase compounds in fastresponse 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.
离子迁移分析谱是识别气相化合物的一种常用分析技术,可实现气体的快速响应与多种成分辨识与分析.然而,传统的离子迁移谱仪因高压的电离导致系统的体积过大、离子腔体的工作温度高、测量精度差,不适用于可挥发性有机物(VOCs)的浓度的检测.为解决这类问题,本文报道了一种纳米发电机的高压实现冷场与常压放电的离子迁移谱分析仪,主要是基于电荷积累机制的多开关调控纳米发电机,可实现几百甚至几千伏的直流偏压.试验测试显示该系统具有良好的离子迁移重复性与特征,可用于VOCs种类与其浓度的辨识.为了进一步提高测试效果,作者采用了深度学习算法提取特征,极大提高了VOCs的检测能力.该方法为物联网的环境检测提供了一种响应速度快、功耗低的便携式VOCs检测与成分分析的解决方案.
作者
Jianxiong Zhu
Zhongda Sun
Jikai Xu
Rafal D.Walczak
Jan ADziuban
Chengkuo Lee
朱建雄;孙仲达;徐继开;Rafal DWalczak;Jan ADziuban;李正国(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)
基金
supported by the research grant of‘‘Chip-Scale MEMS Micro-Spectrometer for Monitoring Harsh Industrial Gases”(R-263-000-C91-305)at the National University of Singapore(NUS),Singapore
the research grant of RIE Advanced Manufacturing and Engineering(AME)programmatic grant A18A4b0055‘‘Nanosystems at the Edge”at NUS,Singapore。
