Ali Hosseingholi Pourasl

Analytical modeling of glucose biosensors based on carbon nanotubes

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.

An Energy-Efficient Mobile Sink-Based Unequal Clustering Mechanism for WSNs

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.

Gas adsorption effect on the graphene nanoribbon band structure and quantum capacitance

Graphene nanoribbons (GNRs) as a quasi-one dimensional (1D) narrow strip of graphene hold great potential for applications in variety of sensors because of π-bonds that can react with chemical elements. Despite outstanding properties, graphene nanoribbons have not fully exploited for variety of application in nanoelectronic and nanosensors due to poor understanding of their physical, electrical properties and basic limitations on the synthesis. Therefore, in order to achieve analytical understanding on the interaction of the gas molecules with GNR surface and gas sensing mechanism, a theoretical method using tight binding model based on nearest neighbour approximation is developed in this study. Additionally, the adsorption effects of NO2 and CO2 gas molecules on the band structure and electrical properties of the GNRFET based gas sensor are investigated. Based on the proposed model numerical simulation is carried out which emphasizes the significant effect of the gas adsorption on the band structure and electrical properties of GNRs. On the other hand, quantum capacitance created between metal gate and channel as a sensing parameter is considered and its variations when GNR exposed to the NO2 and CO2 molecules are analytically modelled. Moreover, the adsorption energy and charge transfer occurred during gas molecules interaction with GNR surface are calculated. Also band structure and I–V characteristics are analysed using first principle calculation based on density functional theory. The current–voltage analysis clearly indicates the changes of the quantum capacitance when exposed to the gas molecules. The results of the proposed model are compared with the available experimental data or data obtained by density functional theory (DFT) calculations and good agreements are observed.

Inter- and intra-cluster movement of mobile sink algorithms for cluster-based networks to enhance the network lifetime

Abstract Due to limited resources of the sensor nodes energy efficiency is an important performance metric to evaluate the wireless sensor networks (WSN). In cluster-based WSNs, the cluster heads (CH) located around base station (BS) consume more energy and exhaust their energy supplies faster than other ones which leads to energy hole problem and premature network death. Furthermore, single-hop transmission manner is usually applied for intra-cluster communication, therefore the member nodes (MN) located farther away from CHs die sooner than other MNs, which leads to coverage holes and reduce the network performance. In this paper, we propose mobile sink (MS) based inter- and intra-cluster routing algorithms. The first algorithm aims to solve the unbalanced energy consumption of CHs, which results in enhanced network lifetime. Likewise, the goal of the second algorithm is to balance the energy consumption of MNs, which results in improved coverage time of the network. In the proposed mechanism, MSs move along clusters and stay in each cluster for a limited sojourn time calculated via first algorithm. Then, the optimal sojourn positions of MSs in the clusters are determined via second algorithm. Simulation results reveal that the proposed algorithms enhance the network performance in terms of different performance evaluation metrics.

Analytical modelling and simulation of gas adsorption effects on graphene nanoribbon electrical properties

Abstract Inspired by the realisation of the ability of graphene nanoribbon (GNR) based sensors to detect individual gas molecules, analytical approach based on the nearest neighbour tight-binding approximation is proposed to study the effect of gas adsorption on GNR electrical properties. Numerical calculations indicate that the electrical properties of the GNR are completely dependent on the adsorbed gas. Conductance as one of the most important electrical parameters as a sensing parameter is considered and analytically modelled. Additionally, gas adsorption effect on the conductance variation in the form of current-voltage characteristics is investigated which points out that gas adsorption dramatically influences electrical conductance of the GNR. Furthermore, to support the proposed analytical models, simulation study is carried out to investigate adsorption of O2 and NH3 gas molecules on the GNR surface. While, the charge transfer phenomenon that occurred as a result of molecular doping of the GNR is explored and the roll of band structure changes by adsorbents and their effects on the conductance and I-V characteristics of the GNRFET sensor is analysed. The comparison study with adopted experimental results is presented; also the I-V characteristics obtained from analytical modelling compared with the first principle calculations and close agreement is observed.