Development of Electromagnetic Wave Absorption Properties of Graphene-Based Nanocomposites by Using Stochastic Optimization Methods

Today, with the developing telecommunications and electronic technologies for civilian and military purposes, electromagnetic (EM) applications have a wide usage area, including different frequency ranges, such as radar, wireless information transfer, and medical technologies, as well as radio frequencies. However, the broad use of these technologies causes EM wave pollution, so they have a devastating effect not only on the working functionalities of electronic devices but also on human health. Therefore, the need to develop these technologies to prevent EM wave pollution is increasing daily. In line with this need, numerous studies have been recently conducted to develop EM interference (EMI) shielding composite materials. Generally, it is aimed to achieve maximum absorption performance with minimum reflection to develop EMI shielding composites. On the other hand, the increase in the number of parameters affecting the performance of these materials also increases the time and cost required for experimental studies. In this case, mathematical and statistical approaches play a crucial role in contributing to experimental studies to reduce time and cost. iv In this thesis study, it is intended to develop a graphene foam/MnO2 nanowire composite structure as an EMI shielding material in order to apply a comprehensive design-modeling-optimization study to the field of materials science. In line with this aim, hydrothermal process parameters (temperature, time, molar ratio) were determined as the design variables of the optimization problem, and the effects of these parameters on the EMI shielding effectiveness (SE) of the nanocomposite structure were investigated. Moreover, the objective of the problem was defined as maximizing the absorption effectiveness of nanocomposite while conserving minimum reflection effectiveness. Data modeling processes were performed via a nonlinear neuro-regression approach to achieve a robust objective function for the defined optimization problem. In the optimization stage of this study, four different stochastic optimization algorithms (Simulated Annealing, Differential Evolution, Nelder-Mead, Random Search) were utilized simultaneously to obtain global optima. It is envisaged that this thesis study would make big contributions to the design-based optimization studies in the materials science subject.