In recent years, carbon nanotubes have received widespread attention as promising carbon-based nanoelectronic devices. Due to their exceptional physical, chemical, and electrical properties, namely a high surface-to-volume ratio, their enhanced electron transfer properties, and their high thermal conductivity, carbon nanotubes can be used effectively as electrochemical sensors. The integration of carbon nanotubes with a functional group provides a good and solid support for the immobilization of enzymes. The determination of glucose levels using biosensors, particularly in the medical diagnostics and food industries, is gaining mass appeal. Glucose biosensors detect the glucose molecule by catalyzing glucose to gluconic acid and hydrogen peroxide in the presence of oxygen. This action provides high accuracy and a quick detection rate. In this paper, a single-wall carbon nanotube field-effect transistor biosensor for glucose detection is analytically modeled. In the proposed model, the glucose concentration is presented as a function of gate voltage. Subsequently, the proposed model is compared with existing experimental data. A good consensus between the model and the experimental data is reported. The simulated data demonstrate that the analytical model can be employed with an electrochemical glucose sensor to predict the behavior of the sensing mechanism in biosensors.

Over the past years, carbon-based materials and especially graphene, have always been known as one of the most famous and popular materials for sensing applications. Graphene poses outstanding electrical and physical properties that make it favorable to be used as a transducer in the gas sensors structure. Graphene experiences remarkable changes in its physical and electrical properties when exposed to various gas molecules. Therefore, in this study, a set of new analytical models are developed to investigate energy band structure, the density of states (DOS), the velocity of charged carriers and I-V characteristics of the graphene after molecular (CO, NO₂, H₂O) adsorption. The results show that gas adsorption modulates the energy band structure of the graphene that leads to the variation of the energy bandgap, thus the DOS changes. Consequently, graphene converts to semiconducting material, which affects the graphene conductivity and together with the DOS variation, modulate velocity and I-V characteristics of the graphene. These parameters are important factors that can be implemented as sensing parameters and can be used to analyze and develop new sensors based on graphene material.

Because of its extraordinary characteristics, this material has attracted researchers in various arenas. Among the numerous fields where this material can be applied is the gas sensor technology. The graphene experiences remarkable changes in its electrical and physical characteristics when exposed to different gases; and they are, therefore, the ideal candidates for gas sensing application. However, a deep understanding of the effects of gas molecules on the graphene energy band structure and its electronic properties, need to be further studied. In this paper, a new quantum capacitance model for the gas sensor employing the graphene field effect transistor platform is proposed. Hence, a general approach using the Tight-binding approximation based on the nearest neighbor incorporating Schrödinger equation is developed. Therefore, the adsorption effects of the CO, NO, and NH ₃ gases on the energy band structure, quantum capacitance, and I-V characteristics of the graphene FET are analytically modeled and investigated. The results indicated that, the gas adsorption can cause significant changes on the graphene band structure and quantum capacitance. The I-V characteristics evaluation indicated current decrement after gas adsorption because of the conductance decrement induced by the band gap increment. The proposed models for the capacitance were also compared with the published experimental data and a satisfactory agreement was achieved.

Network lifetime and energy efficiency are crucial performance metrics used to evaluate wireless sensor networks (WSNs). Decreasing and balancing the energy consumption of nodes can be employed to increase network lifetime. In cluster-based WSNs, one objective of applying clustering is to decrease the energy consumption of the network. In fact, the clustering technique will be considered effective if the energy consumed by sensor nodes decreases after applying clustering, however, this aim will not be achieved if the cluster size is not properly chosen. Therefore, in this paper, the energy consumption of nodes, before clustering, is considered to determine the optimal cluster size. A two-stage Genetic Algorithm (GA) is employed to determine the optimal interval of cluster size and derive the exact value from the interval. Furthermore, the energy hole is an inherent problem which leads to a remarkable decrease in the network’s lifespan. This problem stems from the asynchronous energy depletion of nodes located in different layers of the network. For this reason, we propose Circular Motion of Mobile-Sink with Varied Velocity Algorithm (CM2SV2) to balance the energy consumption ratio of cluster heads (CH). According to the results, these strategies could largely increase the network’s lifetime by decreasing the energy consumption of sensors and balancing the energy consumption among CHs.

An explicit charge-based solution for the drain current, terminal charges and intrinsic capacitance of a long-channel junctionless nanowire transistor (JNT) incorporating the importance of an interface trap density that affect the threshold voltage and the subthreshold slope is presented in this study. Initially, a continuous implicit solution of the unified charge-based control model (UCCM) is derived from the 1D Poisson equation by invoking the parabolic potential approximation. The the continuous solution of the mobile charge density at the source/drain is obtained by adding the decoupled UCCM expression for the depletion and complementary parts, where each part is explicitly solved using the Lambert function without having an additional smoothing function to unify the two limits. The omission of an additional smoothing function could lead to a shorter computation time. Secondly, by solving Pao-Sah’s dual integral, a continuous charge-based expression for the drain current is derived. The expressions for the terminal charge are then derived based on the decoupled drain current model that also becomes an input for computing all four independent capacitances of the JNT. The explicit continuous models show a good agreement with numerical simulation over practical terminal voltages, doping levels, and geometry effects. For a given maximum surface potential error of 5%, the model is accurate for a dopant-geometry ratio of 0.001 < qN _D R ² /4 ϵ _Si < 0.3 and it is also independent of fitting parameters that may vary for different terminal biases or dopant geometries. The nonpiecewise models for drain current, terminal charges and intrinsic capacitance are significantly resolved by decoupling the mobile charge into depletion and complementary parts with no additional smoothing function to unify between operating regions, and omitting fitting parameters that have no physical meaning.