Analytical Modeling and Artificial Neural Network (ANN) Simulation of Current-Voltage Characteristics in Graphene Nanoscroll Based Gas Sensors

Abstract

Graphene nanoscrolls (GNSs) as a new category of quasi one-dimensional (1D) belong to the carbon-based nanomaterials, which have recently captivated the attention of researchers. The latest discoveries of outstanding characteristics of GNSs in terms of structural and electronic properties such as high mobility, controllable band gap, and tunable core size. Previous studies have shown the fact that graphene different structures such as carbon nanotube (CNT), bilayer graphene (BLG) and GNS experience changes in the electrical conductivity when expose to various gases. Therefore, these materials are proposed as a promising candidate for gas detection sensors. These are typically constructed on a field effect transistor (FET) based structure in which the GNS is employed as the channel between the source and the drain. In this study, an analytical model has been proposed and developed with the initial assumption that the gate voltage is directly proportional to the gas concentration as well as its temperature. The effect of gas adsorption on GNS surface makes the changes in GNS conductance which leads to the changes in the current of sensor consequently. This phenomenon is considered as sensing mechanism with proposed sensing parameters. Using the corresponding formula for GNS conductance, the proposed mathematical model is derived. Also, artificial neural network (ANN) algorithms have also been incorporated to obtain other models for the current-voltage (I-V) characteristic in which the analytical data extracted from current and previous related works has been used as the training data set. The comparative study of the results from ANN and the analytical models with the experimental data in hand shows a satisfactory agreement which validates the proposed models.