Mohsen Khaledian

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 In the presence of gas, electrons are donated to or withdrawn from the FET channel and this phenomenon is employed as a sensing mechanism.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.

Sensitivity Modelling of Graphene Nanoscroll-Based NO2 Gas Sensors

Graphene nanoscrolls (GNSs) as a new category of quasi one-dimensional (1D) belong to the carbon-based nanomaterials, which have recently captivated the attention of researchers. The latest discoveries of exceptional structural and electronic properties of GNSs like high mobility, controllable band gap, and tunable core size have become a great stimuli for graphene researchers. Due to the importance and critical role of nanoscale sensors and biosensors in medical facilities and human life, using a promising material like graphene has been widely studied to achieve better accuracy and sensitivity in these devices. Up until now, the majority of surveys conducted previously have focused on experimental studies for sensors family. Therefore, there is lake of analytical models in comparison to experimental surveys. In order to start and understand about the modelling of gas sensors structure, the field effect transistor(FET)-based structure is employed as a basic. In this study, graphene nanoscroll conductivity has been evaluated under the impacts which is induced by the adsorption of different values of NO2 gas concentration on GNS surface. So that, when GNS-gas sensor is exposed to NO2 gas molecules, the carrier concentration of GNS is changed which leads to the changes in the conductance, and consequently, in the current, this phenomenon is considered as sensing mechanism. The I–V characteristic of graphene nanoscroll-based gas sensor has been considered as a criterion to detect the effect of gas adsorption. In order to verify the accuracy of the proposed model, the results have been compared with the existing experimental works.

Carrier statistics and quantum capacitance models of graphene nanoscroll

As a new category of quasi-one-dimensional materials, graphene nanoscroll (GNS) has captivated the researchers recently because of its exceptional electronic properties like having large carrier mobility. In addition, it is admitted that the scrolled configurations for graphene indicate larger stability concerning the energy, as opposed to their counterpart planar configurations like nanoribbon, nanotube, and bilayer graphene. By utilizing a novel analytical approach, the current paper introduces modeling of the density of state (DOS), carrier concentration, and quantum capacitance for graphene nanoscroll (suggested schematic perfect scroll-like Archimedes spiral). The DOS model was derived at first, while it was later applied to compute the carrier concentration and quantum capacitance model. Furthermore, the carrier concentration and quantum capacitance were modeled for both degenerate and nondegenerate regimes, along with examining the effect of structural parameters and chirality number on the density of state and carrier concentration. Latterly, the temperature effect on the quantum capacitance was studied too.

Analytical modeling of the sensing parameters for graphene nanoscroll-based gas sensors

Graphene nanoscrolls (GNSs) as a new category of quasi one dimensional (1D) materials belong to the carbon-based nanomaterials, which have recently captivated the attention of researchers. The latest discoveries of the outstanding characteristics of GNSs in terms of their structural and electronic properties such as, high mobility, controllable band gap, tunable core size, high mechanical strength, high sensing capability and large surface-to-volume ratio make them a great candidate for nanoelectronic devices in future work. Due to the importance and critical role of nanoscale sensors and biosensors in medical facilities and human life, using promising materials like graphene and graphene nanoscrolls has widely attracted the interest and attention of researchers to achieve better accuracy and sensitivity in these devices. Up until now, the majority of surveys conducted previously have focused on experimental studies for sensors. Therefore, there is a lack of analytical models in comparison to experimental surveys. In order to start and understand the modeling of gas sensor structure, the field effect transistor (FET)-based structure has been employed as a basic model for a gas detection sensor. The graphene nanoscroll conductance has been affected under exposure to the NH3 gas molecules. The adsorption of NH3 gas concentration on the GNSs surface which is caused by a chemical reaction between NH3 and the GNSs. Therefore it makes the changes in the GNS conductance and current–voltage characteristics of the proposed GNS based gas sensor. This phenomenon is considered as the sensing mechanism with proposed sensing parameters. The I–V characteristics of a GNS-based sensor have been proposed as a criterion to detect the effect of gas adsorption. Finally, in order to verify the accuracy of the proposed model, the results are compared with the existing experimental works.

Threshold voltage roll-off modelling of bilayer graphene field-effect transistors

An analytical model is presented for threshold voltage roll-off of double gate bilayer graphene field-effect transistors. To this end, threshold voltage models of short- and long-channel states have been developed. In the short-channel case, front and back gate potential distributions have been modelled and used. In addition, the tunnelling probability is modelled and its effect is taken into consideration in the potential distribution model. To evaluate the accuracy of the potential model, FlexPDE software is employed with proper boundary conditions and a good agreement is observed. Using the proposed models, the effect of several structural parameters on the threshold voltage and its roll-off are studied at room temperature.

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.

Analytical model for threshold voltage of double gate bilayer graphene field effect transistors

A new model for threshold voltage of double-gate Bilayer Graphene Field Effect Transistors (BLG-FETs) is presented in this paper. The modeling starts with deriving surface potential and the threshold voltage was modeled by calculating the minimum surface potential along the channel. The effect of quantum capacitance was taken into account in the potential distribution model. For the purpose of verification, FlexPDE 3D Poisson solver was employed. Comparison of theoretical and simulation results shows a good agreement. Using the proposed model, the effect of several structural parameters i.e. oxide thickness, quantum capacitance, drain voltage, channel length and doping concentration on the threshold voltage and surface potential was comprehensively studied.

Analytical Modeling and Artificial Neural Network (ANN) Simulation of Current-Voltage Characteristics in Graphene Nanoscroll Based Gas Sensors

Graphene nanoscrolls (GNSs) as a new category of quasi one-dimensional (1D) belong to the carbon-based nanomaterials, which have recently captivated the attention of researchers. The latest discoveries of outstanding characteristics of GNSs in terms of structural and electronic properties such as high mobility, controllable band gap, and tunable core size. Previous studies have shown the fact that graphene different structures such as carbon nanotube (CNT), bilayer graphene (BLG) and GNS experience changes in the electrical conductivity when expose to various gases. Therefore, these materials are proposed as a promising candidate for gas detection sensors. These are typically constructed on a field effect transistor (FET) based structure in which the GNS 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. The effect of gas adsorption on GNS surface makes the changes in GNS conductance which leads to the changes in the current of sensor consequently. This phenomenon is considered as sensing mechanism with proposed sensing parameters. Using the corresponding formula for GNS conductance, the proposed mathematical model is derived. Also, artificial neural network (ANN) algorithms have also been incorporated to obtain other models for the current-voltage (I-V) characteristic in which the analytical data extracted from current and previous related works has been used as the training data set. The comparative study of the results from ANN and the analytical models with the experimental data in hand shows a satisfactory agreement which validates the proposed models.

Analytical study of subthreshold behaviour of double gate bilayer graphene field effect transistors

In this paper, several analytical models have been developed for 2-D potential distribution, subthreshold current, drain induced barrier lowering (DIBL), and subthreshold-slope (SS) to study the subthreshold behaviour of bilayer graphene filed effect transistors (BLG-FETs). The models are grounded on the basis of the exact solution of the two-dimensional Poisson’s equation while the quantum capacitance effect has been considered throughout the models. The accuracy of the potential distribution model is verified by its analytical results that agree well with those of the FlexPDE Poissonʼs equation solver program. In addition, the effects of the channel length, the oxide thickness, quantum capacitance, and gate biases on subthreshold parameters of BLG-FETs have been explored and the results are compared with those of the silicon FETs.