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- Title
An electronic nose using a single graphene FET and machine learning for water, methanol, and ethanol.
- Authors
Hayasaka, Takeshi; Lin, Albert; Copa, Vernalyn C.; Lopez, Lorenzo P.; Loberternos, Regine A.; Ballesteros, Laureen Ida M.; Kubota, Yoshihiro; Liu, Yumeng; Salvador, Arnel A.; Lin, Liwei
- Abstract
The poor gas selectivity problem has been a long-standing issue for miniaturized chemical-resistor gas sensors. The electronic nose (e-nose) was proposed in the 1980s to tackle the selectivity issue, but it required top-down chemical functionalization processes to deposit multiple functional materials. Here, we report a novel gas-sensing scheme using a single graphene field-effect transistor (GFET) and machine learning to realize gas selectivity under particular conditions by combining the unique properties of the GFET and e-nose concept. Instead of using multiple functional materials, the gas-sensing conductivity profiles of a GFET are recorded and decoupled into four distinctive physical properties and projected onto a feature space as 4D output vectors and classified to differentiated target gases by using machine-learning analyses. Our single-GFET approach coupled with trained pattern recognition algorithms was able to classify water, methanol, and ethanol vapors with high accuracy quantitatively when they were tested individually. Furthermore, the gas-sensing patterns of methanol were qualitatively distinguished from those of water vapor in a binary mixture condition, suggesting that the proposed scheme is capable of differentiating a gas from the realistic scenario of an ambient environment with background humidity. As such, this work offers a new class of gas-sensing schemes using a single GFET without multiple functional materials toward miniaturized e-noses. Sensors: Graphene and machine learning sniff out gases A sensor combined with machine learning algorithms makes an effective 'electronic nose' to distinguish different gases, according to research from the United States. The new approach, developed by a team led by Takeshi Hayasaka of the University of California, Berkeley, combines selectivity, low cost, and low power consumption without needing different materials to sense different gases. A graphene field effect transistor is used as a sensor, and four parameters of its conductivity profile are used as inputs to a machine learning classifier. With enough data, the classifier could identify water, methanol, and ethanol vapors. The researchers also showed that it could distinguish water and methanol in a mixture. These findings are an important step towards a miniaturized e-nose, which would be useful in areas such as environmental and safety monitoring, petrochemical processing, and other industries.
- Publication
Microsystems & Nanoengineering, 2020, Vol 6, Issue 1, p1
- ISSN
2096-1030
- Publication type
Article
- DOI
10.1038/s41378-020-0161-3