Graphene and CNT Field Effect Transistors Based Biosensor Models

In this chapter, novel ideas of graphene and CNT based electrical biosensors are provided. A liquid gated graphene field effect transistor (LG-GFET) based biosensor model is analytically developed for electrical detection of Escherichia coli (E. coli) bacteria. E. coli absorption effects on the graphene surface in the form of conductance variation is considered. Moreover, the current-voltage characteristic in terms of conductance model is applied to evaluate the performance of the biosensor model. Furthermore, the CNT-FET platform is employed for modeling biosensor in order to detect Glucose. For diagnosing and monitoring the blood glucose level, glucose oxidase (GOx) based enzyme sensors have been immensely used. According to the proposed CNT-FET structure, charge based analytical modeling approach is used. The charge-based carrier velocity model is implemented to study electrical characteristics of CNT-FET. In the presented model, the gate voltage is considered as a function of glucose concentration. Finally, the both of presented models are compared with published experimental data.

Silicene Nanoribbons and Nanopores for Nanoelectronic Devices and Applications

Given the compatibility of silicene with existing semiconductor techniques, and a need for new materials to continue Moore's low, it is natural to ask if this material can form a platform as field effect transistor. Here we provide analytical models to study the electrical properties of two dimensional silicene such as electrical conductance, carrier concentration, mobility and magneto-conductance. Furthermore, we show that silicene nanoribbons and nanopores can be used as a discriminating sensor for DNA sequencing and for efficient thermoelectric power generation.

CNT as a Sensor Platform

One of the most important drawbacks which caused the Silicon based technologies to their technical limitations is the instability of their products at nano-level. On the other side, carbon based materials such as carbon nanotube (CNT) as alternative materials have been involved in scientific efforts. Some of the important advantages of CNTs over silicon components are high mechanical strength, high sensing capability and large surface-to-volume ratio. Many researches have been presented using CNT as a sensing material in various applications to improve the sensor characteristics. In this chapter, the platform of CNT sensors such as transistor-based sensors, chemiresistors, chemicapacitance and resonator sensors are discussed in detail. Using CNT as a sensor platform although has great advantages; it does not have sufficient sensor reliability. Some of these technical challenges of CNT-based sensors including Schottky contact formation and non-selective synthesization have also been pointed out in the chapter.

Sensors and Amplifiers

Sensors are electrical-mechanical elements which are the interface between environment and electrical systems. The input of sensors is characteristics of the environment for example temperature, pressure and etc. and their output is a small electric voltage or current. Their job is to convert environment characteristics to an electric voltage or current at their outputs. Since the output current or voltage is very small, it must be amplified in order to be suitable for use in electronic systems. In this chapter we completely explain the design procedure and characteristics of sensor amplifiers. The important parameters of sensor amplifiers are input and output resistance, gain, unity gain bandwidth and etc. One of the most important characteristics of amplifiers is the linearity of amplification in a way that it must have uniformity for all amplitude voltages or currents in all frequencies of the bandwidth. For this purpose, first the operational amplifier is completely discussed, then the linearity of feedback operation will be explained.

Surface Plasmon Resonance-Based Sensor Modeling

Exceptional optical and electrical characteristics of graphene based materials attract significant interest of the researchers to develop sensing center of surface Plasmon resonance (SPR) based sensors by graphene application. On the other hand refractive index calculation of graphene based structures is necessary for SPR sensor analysis. In this chapter first of all a new method for refractive index investigation of some graphene based structures are introduced and then the effect of carrier density variant in the form of conductance gradient on graphene based SPR sensor response is modeled. The molecular properties such as electro-negativity, molecular mass, effective group number and effective outer shell factor of the molecule are engaged. In addition each factor effect in the cumulative carrier variation is explored analytically. The refractive index shift equation based on these factors is defined and related coefficients are proposed. Finally a semi-empirical model for interpretation of changes in SPR curve is suggested and tested for some organic molecules.

Carbon Materials Based Ion Sensitive Field Effect Transistor (ISFET)

Graphene and SWCNT-based Ion Sensitive FET (ISFET) as a novel material with organic nature and ionic liquid gate is intrinsically sensitive to pH changes. pH is an important factor in enzymes stabilities which can affect the enzymatic reaction and broaden the number of enzyme applications. More accurate and consistent results of enzymes must be optimized to realize their full potential as catalysts accordingly. In this chapter, an appropriate structure to ISFET device is designed for the purpose of electrical measurement of different pH buffer solutions. Electrical detection model of each pH value is suggested using conductance modelling of monolayer graphene. In addition, ISFET based on nanostructured SWCNT is studied for the purpose of electrical detection of hydrogen ion concentrations. Electrical detection of hydrogen ion concentrations by modelling the conductance of SWCNT sheets is proposed. pH buffer as a function of gate voltage is assumed and sensing factor is defined. Finally, the proposed new approach improving the analytical model is compared with experimental data and shows good overall agreement.

Graphene Based-Biosensor

Because of unique electrical properties of graphene, it has been employed in many applications, such as batteries, energy storage devices and biosensors. In this chapter modelling of bilayer graphene nanoribbon (BGNR) sensor is in our focus. Based on the presented model BGNR quantum capacitance variation effect by the prostate specific antigen (PSA) injected electrons into the FET channel as a sensing mechanism is considered. Also carrier movement in BGNR as another modelling parameter is suggested. PSA adsorption and local pH value of injecting carriers on the surface of player BGNR is modelled. Carrier concentration as a function of control parameters (f, p) is predicted. Furthermore, changes in charged lipid membrane properties can be electrically detected by graphene based electrolyte gated Graphene Field Effect Transistor (GFET). In this chapter, monolayer graphene-based GFET with a focus on conductance variation occurred by membrane electric charges and thickness is studied. Monolayer graphene conductance as an electrical detection platform which is tuned by neutral, negative and positive electric charged membrane together with membrane thickness is suggested. Electric charge and thickness of the lipid bilayer (QLP and LLP) as a function of carrier density are proposed and the control parameters are defined. Finally, the proposed analytical model is compared with experimental data which indicates good overall agreement.

Optimization of Current-Voltage Characteristics of Graphene-Based Biosensors

The aim of this project is to study and develop graphene-based DNA sensor model for detection of DNA hybridization application. This includes modeling and simulation of carrieconcentration, conductance, and current-voltage characteristics of graphene-based sensors on the field effect transistor (FET) platform. The main challenge is to validate the developed modelwith the experimental data,sincegraphene is considered as a new emerging material and research is still rapidly taking place with fabrication effort reported so far. In this research, first, numerical model is developed which shows the dependency of current-voltage characteristics on the DNA concentration factor. The iteration method is used for developing the numerical model. The proposed model is simulated utilizing MATLAB software to validate with experimental data of DNA hybridization. The Id-Vg characteristic of the proposed numerical model is depicted for different concentrations of DNA molecules and compared with experimental data for the verification purpose. After determining the accuracy of the models, particle swarm optimization (PSO) technique is used to minimize the error of the numerical model.Then, optimization results are shown. Overally, the accuracy of more than 98% represents an overall error of less than 2\% which is quite acceptable for the optimized numerical model.

Development of Gas Sensor Model for Detection of NO2 Molecules Adsorbed on Defect-Free and Defective Graphene

A wide popularity has been generated by graphene as a result of fundamental scientific interest in nano-materials. Graphene-based nanostructure then possess a wide range of special physical uniqueness which can be used in many types of applications including some categories of sensors like optical, magnetic, electronic field, strain and mass sensors as well as field-effect, electrochemical and piezoelectric gas sensors. Graphene is believed to be a fantastic sensor material because of its single atomic layer of graphite with surface.

Graphene-Based Gas Sensor Theoretical Framework

Both graphene and CNTs experience changes in their electrical conductance when exposed to different gases (such as CO2, NO2, and NH3), and they are, therefore, ideal candidates for sensing/measuring applications. In this research, a set of novel gas sensor models employing Field Effect Transistor structure using these materials have been proposed. In the suggested models, different physical properties such as conductance, capacitance, drift velocity, carrier concentration, and the current-voltage (I-V) characteristics of graphene/CNTs have been employed to model the sensing mechanism. An Artificial Neural Network model has also been developed for the special case of a CNT gas sensor exposed to NH3 to provide a platform to check the accuracy of the models. The performance of the models has been compared with published experimental data which shows a satisfactory agreement.