Investigating the dielectric properties of PMMA/RGO nanocomposites using experimental techniques with artificial neural network ANN Model | ||
| Egyptian Journal of Solids | ||
| Volume 47, Issue 1, 2025, Pages 80-109 PDF (2.73 M) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/ejs.2025.350213.1054 | ||
| Authors | ||
| R. A. Mohamed* 1; mahmoud elbakry2; Doaa Habashy3; A. S. Mohamed4; A. M. Ismail5 | ||
| 1Faculty of Education - Physics department | ||
| 2faculty of education ain shams university | ||
| 3department of physics, faculty of education, ai shams university | ||
| 4physics, education, Ain shams | ||
| 5physics, education, Ain Shams | ||
| Abstract | ||
| The current research introduces a combined investigation using both experimental methods and theoretical model to understand and predict the dielectric behavior of PMMA polymer nanocomposites. Poly (methyl methacrylate) (PMMA)/reduced graphene oxide (RGO) nanocomposite films with varying RGO nano-platelets (NPs) contents are made using the casting process. The dielectric constant ε^', loss ε^'', ac-conductivity σ_ac of PMMA/RGO nanocomposites are investigated in the temperature range (300 K 390 K) and frequency range (100 Hz 1 MHz). σ_ac and the frequency exponent S are interpreted by the correlated barrier hopping CBH theory. The frequency exponent S and charge carrier binding energy W_m in the nanocomposite films exhibit a decrease with increasing temperature and RGO content. ε^', ε^'' and σ_ac of PMMA/RGO nanocomposites depend on both frequency f and temperature T. The study employed ANN as a soft-computing process to model the dielectric behavior of the investigated polymer nanocomposites. The measured experimental datasets served as inputs. The optimized ANN configuration was used to train the model for ε^', ε^'' and σ_ac. ANN simulation results exhibited excellent fitting with the measured experimental data. Notably, the ANN not only accurately predicted experimental measurements (serving as a test step) but also successfully predicted values for unmeasured data points. To evaluate the model's performance, Mean Squared Error MSE was calculated. The consistently low MSE values (below 0.08) indicated a high degree of accuracy. Additionally, the correlation coefficient R provided further confirmation, with its value signifying a strong correlation between the ANN results and their targets. | ||
| Keywords | ||
| Polymer nanocomposites; dielectric properties; ac conductivity, and ANN model | ||
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