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.
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.
