Machine Learning-assisted Optimization of TiO₂/CuO Nanocomposites for Enhanced Energy Storage Performance
J. Environ. Nanotechnol., Volume 15, No 2 (June 2026) pp. 401-417
Abstract
Optimizing nanocomposites for energy storage requires precise control of structural, optical, and electrochemical properties. In this research TiO2 and CuO nanoparticles were synthesized by hydrolysis and precipitation. They were integrated into a TiO₂/CuO nanocomposite. Structural and morphological features were examined using X-ray diffraction and scanning electron microscopy, while photoluminescence analysis assessed the optical behavior. Machine learning (ML) models, including Support Vector Regression (SVR), artificial neural networks, multilayer perceptron and hybrid fuzzy logic were employed to predict band gap, crystallite size, charge transfer resistance, and energy storage performance, respectively. CuO incorporation decreased the band gap from 3.0 eV at 0% to 2.1 eV at 30%, and minimized charge transfer resistance from ~16.8 Ω to ~5.8 Ω at 15% CuO. The crystallite size stabilized at 20.74 nm, with SVR demonstrating highest predictive accuracy (R2 = 0.927–0.956). These results indicate that the TiO₂/CuO nanocomposite offers enhanced charge transfer and energy storage capability. The study validates the integration of ML and fuzzy logic in nanocomposite optimization, highlighting its potential for high-performance supercapacitors, batteries, and flexible electronic devices.
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Reference
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