An analytical model and ANN simulation for carbon nanotube based ammonium gas sensors
Abstract
As one of the most interesting advancements in the field of nano technology, 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. An Artificial Neural Network (ANN) algorithm has also been incorporated to obtain another model for the I–V characteristics 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 as well as the analytical models with the experimental data in hand show a satisfactory agreement which validates the proposed models. It is observed that the results obtained from the ANN model are closer to the experimental data than those from the analytical model.
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