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

Takeshi Hayasaka,Arnel A Salvador,Yumeng Liu,Albert Lin,Yoshihiro Kubota,Regine A Loberternos,Vernalyn C Copa,Laureen Ida M Ballesteros,Lorenzo P Lopez,Liwei Lin

doi:10.1038/s41378-020-0161-3

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.

Highlights

Miniaturized gas sensors are expected to witness a high demand in the decade in various sectors, including industrial, consumer electronics, automotive, medical, environmental, and petrochemical fields, due to the small footprint, low power consumption, and low cost[1,2,3]
Measurement setup and experimental conditions We prepared two different graphene field-effect transistor (GFET), namely, a pristine GFET and an atomic layer deposition (ALD) RuO2-functionalized GFET (ALD-RuO2-GFET), for three different experiments using three types of gases: water (H2O), methanol (MeOH), and ethanol (EtOH)
We focus on the results obtained from setup A using a pristine GFET, whereas the results from all the experimental setups can be found in Supplementary Information

Summary

Introduction

Miniaturized gas sensors are expected to witness a high demand in the decade in various sectors, including industrial, consumer electronics, automotive, medical, environmental, and petrochemical fields, due to the small footprint, low power consumption, and low cost[1,2,3]. A Gas-concentration profiles of the binary gas mixtures: H2O vapor (background humidity) and a target gas (either MeOH or EtOH). The gas-sensing patterns are grouped by light blue colored regions, and the corresponding background R.H. levels are labeled with three purge-exposure cycles of the carrier gas, MeOH, and EtOH as the target gases (blue color bars) for each R.H. level (complete dataset in Supplementary Fig. 4). The 3D gas-sensing patterns of the three binary gas mixtures of (1) H2O and the carrier gas (blank), (2) H2O and MeOH, and (3) H2O and EtOH were generated for each experiment and merged into a shared 3D feature space represented by green, red and blue markers, respectively (Fig. 4b for the 2D representation and Supplementary Movie 4 for the 3D movies). Similar trends can be found for those using the ALD-RuO2-GFET (Supplementary Fig. 7 and Supplementary Movie 8)

Methods

Results

Conclusion