Carbon Nanotubes (CNTs) are generally nano-scale tubes comprising a network of carbon atoms in a cylindrical setting that compared with silicon counterparts present outstanding characteristics such as high mechanical strength, high sensing capability and large surface-to-volume ratio. These characteristics, in addition to the fact that CNTs experience changes in their electrical conductance when exposed to different gases, make them appropriate candidates for use in sensing/measuring applications such as gas detection devices. In this research, a model for a Field Effect Transistor (FET)-based structure has been developed as a platform for a gas detection sensor in which the CNT conductance change resulting from the chemical reaction between NH₃ and CNT has been employed to model the sensing mechanism with proposed sensing parameters. The research implements the same FET-based structure as in the work of Peng et al. on nanotube-based NH₃ gas detection. With respect to this conductance change, the I–V characteristic of the CNT is investigated. Finally, a comparative study shows satisfactory agreement between the proposed model and the experimental data from the mentioned research.

Graphene is an allotrope of carbon with two‐dimensional (2D) monolayer honeycombs. A larger detection area and higher sensitivity can be provided by graphene‐based nanosenor because of its 2D structure. In addition, owing to its special characteristics, including electrical, optical and physical properties, graphene is known as a more suitable candidate compared to other materials used in the sensor application. A novel model employing a field‐effect transistor structure using graphene is proposed and the current–voltage ( I – V ) characteristics of graphene are employed to model the sensing mechanism. This biosensor can detect Escherichia coli ( E. coli ) bacteria, providing high levels of sensitivity. It is observed that the graphene device experiences a drastic increase in conductance when exposed to E. coli bacteria at 0–10 ⁵ cfu/ml concentration. The simple, fast response and high sensitivity of this nanoelectronic biosensor make it a suitable device in screening and functional studies of antibacterial drugs and an ideal high‐throughput platform which can detect any pathogenic bacteria. Artificial neural network and support vector regression algorithms have also been used to provide other models for the I – V characteristic. A satisfactory agreement has been presented by comparison between the proposed models with the experimental data.

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.

Carbonaceous materials have recently received attention in electronic applications and measurement systems. In this work, we demonstrate the electrical behavior of carbon films fabricated by methane arc discharge decomposition technique. The current-voltage (I-V) characteristics of carbon films are investigated in the presence and absence of gas. The experiment reveals that the current passing through the carbon films increases when the concentration of CO2 gas is increased from 200 to 800 ppm. This phenomenon which is a result of conductance changes can be employed in sensing applications such as gas sensors.

Graphene is another allotrope of carbon with two-dimensional monolayer honeycomb. Owing to its special characteristics including electrical, physical and optical properties, graphene is known as a more suitable candidate compared to other materials to be used in the sensor application. It is possible, moreover, to use biosensor by using electrolyte-gated field effect transistor based on graphene (GFET) to identify the alterations in charged lipid membrane properties. The current article aims to show how thickness and charges of a membrane electric can result in a monolayer graphene-based GFET while the emphasis is on the conductance variation. It is proposed that the thickness and electric charge of the lipid bilayer (L_LP and Q_LP) are functions of carrier density, and to find the equation relating these suitable control parameters are introduced. Artificial neural network algorithm as well as support vector regression has also been incorporated to obtain other models for conductance characteristic. The results comparison between analytical models, artificial neural network and support vector regression with the experimental data extracted from previous work show an acceptable agreement.