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An electronic nose using a single graphene FET and machine learning for water, methanol, and ethanol

Engineering and Technology

An electronic nose using a single graphene FET and machine learning for water, methanol, and ethanol

T. Hayasaka, A. Lin, et al.

This groundbreaking research conducted by Takeshi Hayasaka, Albert Lin, Vernalyn C. Copa, Lorenzo P. Lopez Jr., Regine A. Loberternos, Laureen Ida M. Ballesteros, Yoshihiro Kubota, Yumeng Liu, Amel A. Salvador, and Liwei Lin showcases a unique gas-sensing technique utilizing a single graphene field-effect transistor for precise gas differentiation. With machine learning, their method identifies different vapors even in challenging conditions, paving the way for innovative applications.

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Playback language: English
Abstract
This paper presents a novel gas-sensing scheme using a single graphene field-effect transistor (GFET) and machine learning to achieve gas selectivity. Instead of using multiple functional materials, the GFET's gas-sensing conductivity profiles are recorded, decoupled into four physical properties (electron mobility, hole mobility, carrier concentration, and the ratio of residual carrier concentration to charged impurity concentration), and projected onto a 4D feature space. Machine learning algorithms classify these 4D vectors to differentiate target gases. The system successfully classified water, methanol, and ethanol vapors with high accuracy individually and qualitatively distinguished methanol from water in a binary mixture, suggesting its potential for real-world applications with background humidity.
Publisher
Microsystems & Nanoengineering
Published On
Jan 28, 2020
Authors
Takeshi Hayasaka, Albert Lin, Vernalyn C. Copa, Lorenzo P. Lopez Jr., Regine A. Loberternos, Laureen Ida M. Ballesteros, Yoshihiro Kubota, Yumeng Liu, Amel A. Salvador, Liwei Lin
Tags
gas sensing
graphene field-effect transistor
machine learning
gas selectivity
vapor classification
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