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 temper...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.展开更多
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),Singaporethe research grant of RIE Advanced Manufacturing and Engineering(AME)programmatic grant A18A4b0055‘‘Nanosystems at the Edge”at NUS,Singapore。
文摘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.
