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

Miniaturized gas sensors are expected to witness a high demand in the next decade in various industry fields due to the small foot print, low power consumption, and low manufacturing cost. To date, various miniaturized gas sensors have been proposed by combining key sensing principles/materials and micro/nano fabrication technologies. Among those platforms, graphene-based gas sensor is especially promising due the unique features: gas sensing capability at room temperature, unique electrical properties, and the truly two dimensional structure. On the other hand, several issues have prevented the graphene-based gas sensors from being applied to practical gas sensing applications in the ambient air: the electrical properties of graphene-based gas sensors are susceptible to the environmental factors when they are operated at room temperature; the gas sensitivity is relatively low when compared with heated metal oxide (MOX) type gas sensors; and the poor gas selectivity. As for the poor gas selectivity issue, electronic nose has been proposed to tackle the issue; however, the sensor performance is severely constrained by the inefficient functionalization process. Therefore, in order to realize practical miniaturized gas sensors, a new class of gas sensing scheme is required./nThe aim of this dissertation is to demonstrate a new class of miniaturized gas sensing platform by improving the stability, sensitivity, and selectivity of graphene-based gas sensors through fundamental properties of graphene, a novel measurement scheme, and microfabrication process./nThe influences of temperature, H2O (humidity), and O2 on 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 device to H2O decreases; while the sensitivity to O2 decreases first, and increases afterwards as the operation 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./nUnique 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) RuO2 is 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-RuO2 base 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./nThe 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 achieve selectivity, 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.

Advisor: Lin, Liwei