Stability, Sensitivity, and Selectivity of Graphene FET-based Gas Sensors

The influences of temperature, H2O (humidity), and O2on the stability of the electrical properties and the gas sensing characteristics of graphene field effect transistor (GFET)-based gas sensors are studied as these environmental factors are often encountered in practical gas sensing applications. Both empirical results and theoretical analyses are characterized for heated GFET-based gas sensors from room temperature to 100°C under a wide range of applied gate voltages. It is found that at a constant applied gate voltage of−20 V with respect to the gate voltage at the charge neutrality point VNP, the sensitivity of the deviceto H2O decreases; while the sensitivity to O2 decreases first, and increases afterwards as theoperation temperature increases. These phenomena are explained by using the physisorption and chemisorption models between the tested gases and the graphene surface. Furthermore,devices operate in the hole regime result in lower sensitivity to H2O and O2 as compared to those results for the electron regime. As such, these studies provide foundations to improve the stability of GFET-based gas sensors in practical application environments under the influences of ambient air, temperature, and humidity.

Unique graphene-catalyst hybrid structure is proposed to realize both high gas sensitivity and reproducibility. The proposed device structure is readily realized by standard MEMS fabrication process. For the catalytic layer, atomic layer deposition (ALD) RuO2is used. The gas sensing properties of a pristine-GFET and a ALD-RuO2 functionalized GFET are compared. Three distinctive advancements have been achieved: (1) enhanced sensitivity using the scheme of electron mobility characterizations by a hybrid structure of graphene and ALD-RuO2base layer; (2) first demonstration of gas sensing by means of the 4-dimentional (4D) physical properties vectors of graphene FETs; and (3) using the 16-dimensional (16D) characteristic gas sensing pattern to distinguish water vapor and methanol. As such, the proposed unique device structure and the measurement scheme could offer enhanced sensitivity as well as selectivity.

The poor gas selectivity problem has been a long-standing issue for miniaturized chemiresistor type gas sensors. An e-nose system based on a single GFET is developed to achieveselectivity, miniaturization, low cost, and low power consumption. 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. Furthermore, the gas sensing patterns of methanol were qualitatively distinguished form that of water vapor in a binary mixture condition, suggesting that the proposed scheme is capable of differentiating a gas in the realistic scenario of ambient environment with background humidity. As such, this work offers a new class of e-nose sensing scheme using a single GFET without multiple functional materials towards practical gas sensing applications.