Volatile organic compounds sensing based on Bennet doubler-inspired triboelectric nanogenerator and machine learning-assisted ion mobility analysis.
Sci Bull (Beijing)
; 66(12): 1176-1185, 2021 Jun 30.
Article
em En
| MEDLINE
| ID: mdl-36654355
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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Base de dados:
MEDLINE
Tipo de estudo:
Prognostic_studies
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article