Analytical Modelling and Simulation of I-V Characteristics in Carbon Nanotube Based Gas Sensors Using ANN and SVR Methods

TitleAnalytical Modelling and Simulation of I-V Characteristics in Carbon Nanotube Based Gas Sensors Using ANN and SVR Methods
Publication TypeJournal Article
Year of Publication2014
AuthorsElnaz Akbari, Zolkafle Buntat, Aria Enzevaee, Monireh Ebrahimi, Amir Yazdavar, Rubiyah Yusof
JournalChemometrics and Intelligent Laboratory Systems
Volume137
Pagination173-180
Date Published10/2014
KeywordsArtificial neural networks, Carbon nanotubes (CNTs), Field effect transistor (FET), I-V characteristic, Support vector regression (SVR)
Abstract

As one of the most interesting advancements in the field of nanotechnology, carbon nanotubes (CNTs) have been given special attention because of their remarkable mechanical and electrical properties and are being used in many scientific and engineering research projects. One such application facilitated by the fact that CNTs experience changes in electrical conductivity when exposed to different gases is the use of these materials as part of gas detection sensors. These are typically constructed on a field effect transistor (FET) based structure in which the CNT 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. Using the corresponding formulae for CNT conductance, the proposed mathematical model is derived. artificial neural network (ANN) and support vector regression (SVR) algorithms have also been incorporated to obtain other models for the current-voltage (I-V) characteristic in which the experimental data extracted from a recent work by N. Peng et al. has been used as the training data set. The comparative study of the results from ANN, SVR, and the analytical models with the experimental data in hand shows a satisfactory agreement which validates the proposed models. However, SVR outperforms the ANN approach and gives more accurate results.

DOI10.1016/j.chemolab.2014.07.001