Browsing by Author "Hosseingholipourasl, Ali"

Now showing 1 - 2 of 2

Citation - WoS: 0
Citation - Scopus: 0
Analytical Modeling of Nh3 Gas Sensing Using Zigzag Graphene Nanoscrolls: Energy Band Structure and Electrical Properties
(Springer, 2025) Hosseingholipourasl, Ali
Graphene nanoscrolls (GNSs), a unique nanostructure of graphene, have garnered considerable attention due to their distinctive properties such as a rolled-up papyrus-like structure, adjustable core geometry, increased inner wall area, and enhanced surface-to-volume ratio. These properties make GNS a promising candidate for various nanoelectronic applications, including gas sensing devices. Despite its potential, GNS has been relatively underexplored in the context of gas sensing applications. In this study, we present a series of analytical models to characterize the behavior of zigzag graphene nanoscrolls (ZGNS)-based gas sensors in the presence of NH3 gas. The tight-binding technique, employing nearest neighbor approximation, is utilized to formulate the energy dispersion relation of GNS, incorporating the influence of gas molecule adsorption through parameters such as the hopping integral between GNS and gas and the on-site energy of adsorbed gas molecules. Furthermore, the derived energy equation is employed to establish the conductance relation and explore the impact of gas adsorption on the electrical conductance of GNS. Subsequently, the I-V characteristics of the GNS sensor are formulated, and the variations in current due to NH3 gas exposure are analyzed. The gate voltage is modeled as a function of NH3 concentration, and a sensing parameter is proposed based on current variations across different concentrations. Validation of the model is performed by comparing the obtained results with data extracted from previous studies. The findings demonstrate good agreement, underscoring the effectiveness of the proposed ZGNS-based sensor model for NH3 detection under varying environmental conditions.
Citation - Scopus: 0
Galapagos Giant Tortoise Mating Algorithm: Revolutionizing Wireless Charging Trajectories and Secure Data Transmission in Sustainable Power Plants
(Elsevier, 2025) Gharaei, Niayesh; Alabdali, Aliaa M.; Almagrabi, Alaa Omran; Hosseingholipourasl, Ali
This paper presents the Galapagos Giant Tortoise Mating Optimization Algorithm (GGTMOA), a novel nature-inspired metaheuristic developed to optimize the trajectory paths of Wireless Mobile Chargers (WMCs) and ensure secure data transmission in power plants. The algorithm addresses critical challenges such as energy-efficient charging, the spatial distribution of wireless sensor nodes, limited operational energy resources, dynamic trajectory planning, and data encryption for secure communication. Inspired by the unique mating behaviors of galapagos giant tortoises, GGTMOA achieves a robust balance between exploration and exploitation through innovative initialization techniques, movement strategies, mating mechanisms, and selection processes. In this study, the proposed algorithm is first employed to optimize the trajectory paths of WMCs, addressing key challenges in energy-efficient charging and dynamic path planning. Following this, the algorithm integrates advanced encryption methods to ensure the secure transmission of data between sensor nodes and base stations, safeguarding sensitive information and enhancing the overall security of the system. This two-fold approach not only optimizes charging efficiency and reduces energy consumption but also fortifies data communication, making the system more robust and reliable in industrial environments. Simulation results demonstrate that GGTMOA outperforms existing metaheuristics by generating optimal trajectories that enhance charging efficiency, reduce energy consumption, ensure secure data communication, and satisfy plant-specific energy constraints. These findings establish GGTMOA as a powerful tool for sustainable energy management, wireless charging optimization, and secure data handling in industrial environments.