Investigating the dielectric properties of PMMA/RGO nanocomposites using experimental techniques with artificial neural network ANN Model
Mohamed, R., elbakry, M., Habashy, D., Mohamed, A., Ismail, A. (2025). Investigating the dielectric properties of PMMA/RGO nanocomposites using experimental techniques with artificial neural network ANN Model. EKB Journal Management System, 47(1), 80-109. doi: 10.21608/ejs.2025.350213.1054
R. A. Mohamed; mahmoud elbakry; Doaa Habashy; A. S. Mohamed; A. M. Ismail. "Investigating the dielectric properties of PMMA/RGO nanocomposites using experimental techniques with artificial neural network ANN Model". EKB Journal Management System, 47, 1, 2025, 80-109. doi: 10.21608/ejs.2025.350213.1054
Mohamed, R., elbakry, M., Habashy, D., Mohamed, A., Ismail, A. (2025). 'Investigating the dielectric properties of PMMA/RGO nanocomposites using experimental techniques with artificial neural network ANN Model', EKB Journal Management System, 47(1), pp. 80-109. doi: 10.21608/ejs.2025.350213.1054
Mohamed, R., elbakry, M., Habashy, D., Mohamed, A., Ismail, A. Investigating the dielectric properties of PMMA/RGO nanocomposites using experimental techniques with artificial neural network ANN Model. EKB Journal Management System, 2025; 47(1): 80-109. doi: 10.21608/ejs.2025.350213.1054

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, Page 80-109  XML PDF (2.73 MB)
Document Type: Original Article
DOI: 10.21608/ejs.2025.350213.1054
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Authors
R. A. Mohamed email 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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