Hediyeh Karimi

Development of solution-gated graphene transistor model for biosensors

AbstractThe distinctive properties of graphene, characterized by its high carrier mobility and biocompatibility, have stimulated extreme scientific interest as a promising nanomaterial for future nanoelectronic applications. In particular, graphene-based transistors have been developed rapidly and are considered as an option for DNA sensing applications. Recent findings in the field of DNA biosensors have led to a renewed interest in the identification of genetic risk factors associated with complex human diseases for diagnosis of cancers or hereditary diseases. In this paper, an analytical model of graphene-based solution gated field effect transistors (SGFET) is proposed to constitute an important step towards development of DNA biosensors with high sensitivity and selectivity. Inspired by this fact, a novel strategy for a DNA sensor model with capability of single-nucleotide polymorphism detection is proposed and extensively explained. First of all, graphene-based DNA sensor model is optimized using particle swarm optimization algorithm. Based on the sensing mechanism of DNA sensors, detective parameters (Ids and Vgmin) are suggested to facilitate the decision making process. Finally, the behaviour of graphene-based SGFET is predicted in the presence of single-nucleotide polymorphism with an accuracy of more than 98% which guarantees the reliability of the optimized model for any application of the graphene-based DNA sensor. It is expected to achieve the rapid, quick and economical detection of DNA hybridization which could speed up the realization of the next generation of the homecare sensor system.

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.

Graphene Nanoribbon Based Gas Sensor

Mono layer graphene (MLG) as a new kind of advanced material is in our focus. MLG indicates a twodimensional structure with quantum confinement effect in its thickness. The MLG based nanomaterial has remarkable potential on electrochemical catalysis and bio-sensing applications. Recently inter sheet sensing systems for graphene sensor have been reported which will be used in our model as well. We provide a new idea of electrochemical sensors based on the graphene application. In this paper carrier the concentration on the sensor as a function of gas concentration is reported. A field effect transistor (FET) base structure as a modeling platform is proposed. Gate voltage representing the gas concentration on the sensor, or in other words the gate voltage as a function of gas concentration can be employed. Finally the proposed model is used in simulation studies and evaluated by experimental result.

Analytical prediction of liquid-gated graphene nanoscroll biosensor performance

The latest discovery of the graphene nanoscroll has provided enormous new stimuli to carbon nanoresearch. Due to its one-dimensional structure and tunable core size, the graphene nanoscroll is suitable for nanoscale applications such as in nanotransistors, and biosensor devices. DNA sensing is critical in the identification of the genetic risk factors associated with complex human diseases, and continues to have an emerging role in therapeutics and personalized medicine. This paper presents the analytical model of liquid-gated field effect transistors (LGFETs) for zig-zag graphene nanoscrolls (ZGNSs) inspired by carbon nanotube behavior when exposed to DNA molecules. First of all, in order to gain physical insight into GNS-based devices, the conductance of GNSs is analytically modelled. Based on the sensing mechanism of the DNA sensor, GNS controlling elements (ηGNS and eGNS) are proposed and the behavior of LGFETs-based GNS nanomaterial in the presence of DNA molecules is predicted to get a greater insight into the rapid development of DNA sensors and their application. Because of the channel-doping effect due to the adsorption of the DNA molecules, the conductance of the channel is altered. On the other hand, the applied voltage effect in the form of tilted electron energy levels is utilized in the form of normalized Fermi energy variation which is used in the sensor modelling. This study emphasizes the promising nature of carbon nanoscrolls for a number of electronic device applications.

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.

Optimization of DNA sensor model based nanostructured graphene using particle swarm optimization technique

It has been predicted that the nanomaterials of graphene will be among the candidate materials for postsilicon electronics due to their astonishing properties such as high carrier mobility, thermal conductivity, and biocompatibility. Graphene is a semimetal zero gap nanomaterial with demonstrated ability to be employed as an excellent candidate for DNA sensing. Graphene-based DNA sensors have been used to detect the DNA adsorption to examine a DNA concentration in an analyte solution. In particular, there is an essential need for developing the cost-effective DNA sensors holding the fact that it is suitable for the diagnosis of genetic or pathogenic diseases. In this paper, particle swarm optimization technique is employed to optimize the analytical model of a graphene-based DNA sensor which is used for electrical detection of DNA molecules. The results are reported for 5 different concentrations, covering a range from 0.01 nM to 500 nM. The comparison of the optimized model with the experimental data shows an accuracy of more than 95% which verifies that the optimized model is reliable for being used in any application of the graphene-based DNA sensor.

Analytical development and optimization of a graphene-solution interface capacitance model

Summary Graphene, which as a new carbon material shows great potential for a range of applications because of its exceptional electronic and mechanical properties, becomes a matter of attention in these years. The use of graphene in nanoscale devices plays an important role in achieving more accurate and faster devices. Although there are lots of experimental studies in this area, there is a lack of analytical models. Quantum capacitance as one of the important properties of field effect transistors (FETs) is in our focus. The quantum capacitance of electrolyte-gated transistors (EGFETs) along with a relevant equivalent circuit is suggested in terms of Fermi velocity, carrier density, and fundamental physical quantities. The analytical model is compared with the experimental data and the mean absolute percentage error (MAPE) is calculated to be 11.82. In order to decrease the error, a new function of E composed of α and β parameters is suggested. In another attempt, the ant colony optimization (ACO) algorithm is implemented for optimization and development of an analytical model to obtain a more accurate capacitance model. To further confirm this viewpoint, based on the given results, the accuracy of the optimized model is more than 97% which is in an acceptable range of accuracy.

Analytical Modeling of Bilayer Graphene Based Biosensor

Recently, great attention has been devoted to the graphene because of its unique properties, such as high charge carrier mobility even at a high charge carrier concentration at room temperature, the existence of massless Dirac fermions, Quantum Hall effect at room temperature, gas sensing at the single molecule level and gate controlled transport (electron or hole) properties. Gas concentration effect on electrical conductivity of graphene by Green function method has been modeled however sensor analytical modeling needs to be done. In this paper injected carriers by Prostate Specific Antigen (PSA) concentration are simulated and carrier controlling parameters (F, pH) are suggested. Injected carriers from PSA to the graphene surface are monitored and their effect on the capacitance is modeled. Finally comparison with experimental data which illustrates good agreement between them is considered.

Structure and Thickness Optimization of Active Layer in Nanoscale Organic Solar Cells

This paper presents the development of a two-dimensional model of multilayer bulk heterojunction organic nanoscale solar cells, consisting of the thickness of active layer and morphology of the device. The proposed model is utilized to optimize the device parameters in order to achieve the best performance using particle swarm optimization algorithm. The organic solar cells under research are from poly (3-hexylthiophene) and [6,6]-phenyl C61-butyric acid methyl ester type which are modelled to be investigated for performance enhancement. A three-dimensional fitness function is proposed involving domain size and active layer thickness as variables. The best results out of 20 runs of optimization show that the optimized value for domain size is 17 nm, while the short-circuit current vs. voltage characteristic shows a very good agreement with the experimental results obtained by previous researchers.