Soft computing techniques in prediction gas sensor based 2D material

Abstract

Two-dimensional materials may be the key to unlock the next technological advancement. There have been many other two-dimensional materials discovered after the discovery of graphene, all with their own unique and interesting properties. One type of two-dimensional materials that were discovered has attracted quite a bit of attention, Phosphorene. Phosphorene is a natural semiconductor with a variable band gap and similar structure to carbon-based graphene. The Field Effect Transistor (FET) structure is employed in this project, where phosphorene has been proposed as the channel material between drain and source. An analytical model is developed assuming that gate voltage variations are proportional to nitrogen dioxide (NO2) gas concentration changes. Furthermore, to obtain other models for the current-voltage(I–V) characteristic, Artificial Neural Network (ANN) and Classification and Regression Trees (CART) algorithms have been employed. Finally, the developed models were compared with the data extracted from experimental work by A. Abbas, which show satisfactory agreement and validity of the proposed models.