02309nas a2200253 4500008004100000245012600041210006900167260001200236300001200248490000800260520147700268653003101745653002801776653003401804653002301838653003601861100001801897700002101915700001901936700002201955700001901977700001901996856004002015 2014 eng d00aAnalytical Modelling and Simulation of I-V Characteristics in Carbon Nanotube Based Gas Sensors Using ANN and SVR Methods0 aAnalytical Modelling and Simulation of IV Characteristics in Car c10/2014 a173-1800 v1373 aAs 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. 10aArtificial neural networks10aCarbon nanotubes (CNTs)10aField effect transistor (FET)10aI-V characteristic10aSupport vector regression (SVR)1 aAkbari, Elnaz1 aBuntat, Zolkafle1 aEnzevaee, Aria1 aEbrahimi, Monireh1 aYazdavar, Amir1 aYusof, Rubiyah uhttp://knoesis.wright.edu/node/2733