Aria Enzevaee

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 research.

Gas Concentration Effects on the Sensing Properties of Bilayer Graphene

Graphene is a single-atom thin layer with sp2 hybridized and two-dimensional (2D) honeycomb structure of carbon. Because of its exclusive properties including high conductivity, high surface area and high mechanical strength, graphene has attracted a great deal of attention of many researchers in chemistry, physics, biology, nanoelectronics and nanotechnology in the recent years. Due to the fact that different kinds of nanoscale sensors including gas sensors and biosensors are playing important roles in human life, the idea of using promising materials such as graphene to achieve accuracy and higher speed in these devices is becoming a matter of attention. Although there are plenty of experimental studies in this field, the lack of analytical models is felt deeply. To start with modelling, the field effect transistor (FET)-based structure is employed as a platform and graphene conductivity has been studied under the impacts induces by the adsorption of different values of gas concentration on its surface. The reaction between graphene and gas makes new carriers in graphene which cause changes in the carrier concentration and consequently alters the conductance. In the presence of gas, electrons are donated to or withdrawn from the FET channel and this phenomenon is employed as a sensing mechanism. The I–V characteristic of bilayer graphene (BLG) has been incorporated as a measure to study the effects of gas adsorption. In order to assess the accuracy of the proposed models, the obtained results are compared with the existing experimental data and acceptable agreement is reported.

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

Summary 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 conditions.

Bilayer graphene application on NO 2 sensor modelling

Graphene is one of the carbon allotropes which is a single atom thin layer with sp2 hybridized and two-dimensional (2D) honeycomb structure of carbon. As an outstanding material exhibiting unique mechanical, electrical, and chemical characteristics including high strength, high conductivity, and high surface area, graphene has earned a remarkable position in todays experimental and theoretical studies as well as industrial applications. One such application incorporates the idea of using graphene to achieve accuracy and higher speed in detection devices utilized in cases where gas sensing is required. Although there are plenty of experimental studies in this field, the lack of analytical models is felt deeply. To start with modelling, the field effect transistor- (FET-) based structure has been chosen to serve as the platform and bilayer graphene density of state variation effect by NO2 injection has been discussed. The chemical reaction between graphene and gas creates new carriers in graphene which cause density changes and eventually cause changes in the carrier velocity. In the presence of NO2 gas, electrons are donated to the FET channel which is employed as a sensing mechanism. In order to evaluate the accuracy of the proposed models, the results obtained are compared with the existing experimental data and acceptable agreement is reported.

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 model.

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– 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.

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

Correction for ‘An analytical model and ANN simulation for carbon nanotube based ammonium gas sensors’ by Elnaz Akbari et al., RSC Adv., 2014, 4, 36896–36904.