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Discrimination of Methanol from Ethanol Using Graphene-based Smart Gas Sensors

文献类型： 会议论文

第一作者： Shirong Huang

作者： Shirong Huang 1 ; Bergoi Ibarlucea 2 ; Gianaurelio Cuniberti 1 ;

作者机构： 1.Institute for Materials Science and Max Bergmann Center for Biomaterials, TUD Dresden University of Technology, Dresden, Germany

2.TECNALIA, Basque Research and Technology Alliance (BRTA), Donostia-San Sebastian, Spain

关键词： Volatile organic compounds;Industries;Spectroscopy;Machine learning algorithms;Ethanol;Fingerprint recognition;Electronic noses

会议名称： IEEE International Symposium on Olfaction and Electronic Nose

主办单位：

页码： 1-3

摘要： Methanol and ethanol are physical-chemically similar volatile organic compounds and are widely used in the industry. Compared with ethanol, methanol is extremely toxic to human health by ingestion or inhalation. Therefore, it is of great importance to develop effective techniques to discriminate methanol from ethanol. The gold standard approaches for methanol and ethanol detection are gas chromatography-mass spectroscopy (GC-MS) and nuclear magnetic resonance (NMR), which are rather expensive and sophisticated. Alternatively, chemiresitive gas sensors show promising applications in volatile organic compounds detection. Here, we present the development of graphene-based smart gas sensors for methanol discrimination from ethanol. By using multiple transient-state features as the fingerprint information of gas, the selectivity of developed gas sensors is enhanced. This proposed strategy enables the graphene-based gas sensors with an excellent discrimination performance (accuracy-98.9%) leveraging supervised machine learning algorithms. This work paves the path to design a low-cost, low-power consumption, facile, highly sensitive, and highly selective smart gas sensor to discriminate methanol from ethanol, which could also be extended to other similar VOCs discrimination.

分类号： tp212-53

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