Investigation of carbon dioxide adsorption effects on graphene nanoribbon conductance

A novel method is employed for the derivation of an analytical model for a carbon-dioxide (CO2) gas sensor based on graphene nanoribbon (GNR) conductance variation. The capacitance gradient created between the channel and the gate of a field effect transistor device is employed as an important property in the interpretation. Gas concentration and its effect on capacitance are incorporated as a modelling platform. In another attempt to model the electrical conductance in GNRs, an intelligent artificial neural network scheme is used in the modelling stage. A satisfactory agreement is presented by comparison between the empirical data extracted from a study conducted by Yoon et al. and the proposed models. © The Institution of Engineering and Technology 201

Analytical model of graphene-based biosensors for bacteria detection

In this research, a set of novel models based on field effect transistor (FET) structure using graphene have been proposed with the current–voltage (I–V) characteristics of graphene employed to model the sensing mechanism. It has been observed that the graphene device experiences a drastic increase in conductance when exposed to Escherichia coli bacteria at 0– (Formula presented.) cfu/mL concentrations. Hence, simplicity of the structure, fast response rate and high sensitivity of this nanoelectronic biosensor make it a more suitable device in screening and functional studies of antibacterial drugs and an ideal high-throughput platform that can detect any pathogenic bacteria. Accordingly, the proposed model exhibits a satisfactory agreement with the experimental data

An analytical approach to evaluate the performance of graphene and carbon nanotubes for NH3 gas sensor applications

Carbon, in its variety of allotropes, especially graphene and carbon nanotubes (CNTs), holds great potential for applications in variety of sensors because of dangling π-bonds that can react with chemical elements. In spite of their excellent features, carbon nanotubes (CNTs) and graphene have not been fully exploited in the development of the nanoelectronic industry mainly because of poor understanding of the band structure of these allotropes. A mathematical model is proposed with a clear purpose to acquire an analytical understanding of the field-effect-transistor (FET) based gas detection mechanism. The conductance change in the CNT/graphene channel resulting from the chemical reaction between the gas and channel surface molecules is emphasized. NH3 has been used as the prototype gas to be detected by the nanosensor and the corresponding current-voltage (I-V) characteristics of the FET-based sensor are studied. A graphene-based gas sensor model is also developed. The results from graphene and CNT models are compared with the experimental data. A satisfactory agreement, within the uncertainties of the experiments, is obtained. Graphene-based gas sensor exhibits higher conductivity compared to that of CNT-based counterpart for similar ambient condition

Sensing and identification of carbon monoxide using carbon films fabricated by methane arc discharge decomposition technique

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

An analytical model and ANN simulation for carbon nanotube based ammonium gas sensors

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 mode

Analytical modeling and simulation of I-V characteristics in carbon nanotube based gas sensors using ANN and SVR methods

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 result

Analytical calculation of sensing parameters on carbon nanotube based gas sensors

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 researc