Support vector regression and neural networks analytical models for gas sensor based on molybdenum disulfide

In this study, MoS gas sensor based on field effect transistor has been proposed and the adsorption of NO molecules on the channel surface can lead to significant changes on its electronic and transport properties. The analytical models have been developed for the NO gas sensors by making an initial assumption that the gate voltage is directly proportional to the gas concentration. The performance of this sensor, is predicted and investigated by support vector regression (SVR) and artificial neural network (ANN) algorithms. The MoS gas sensor displays current changes upon exposure to very low concentrations of NO. The comparison between analytical model, ANN and SVR with the empirical data shows the successful model construction. However, ANN outperforms the SVR approach and gives more accurate results.