GAS Sensor Modelling and Simulation

Abstract

Both graphene and CNTs experience changes in their electrical conductance when exposed to different gases (such as CO2, NO2, and NH3), and they are, therefore, ideal candidates for sensing/measuring applications. In this research, a set of novel gas sensor models employing Field Effect Transistor structure using these materials have been proposed. In the suggested models, different physical properties such as conductance, capacitance, drift velocity, carrier concentration, and the current-voltage (I-V) characteristics of graphene/CNTs have been employed to model the sensing mechanism. An Artificial Neural Network model has also been developed for the special case of a CNT gas sensor exposed to NH3 to provide a platform to check the accuracy of the models. The performance of the models has been compared with published experimental data which shows a satisfactory agreement.