Machine Learning Based Simulation, Synthesis, and Characterization of Zinc Oxide/Graphene Oxide Nanocomposite for Energy Storage Applications

Artificial intelligence (AI) based models have been used to predict the structural, optical, mechanical, and electrochemical properties of zinc oxide/graphene oxide nanocomposites. Machine learning (ML) models such as Artificial Neural Networks (ANN), Support Vector Regression (SVR), Multilayer Perceptron (MLP), and hybrid, along with fuzzy logic tools, were applied to predict the different properties like wavelength at maximum intensity (444 nm), crystallite size (17.50 nm), and optical bandgap (2.85 eV). While some other properties, such as energy density, power density, and charge transfer resistance, were also predicted with the help of datasets of 1000 (80:20). In general, the energy parameters were predicted more accurately by hybrid models. The hydrothermal method was used to synthesize graphene oxide (GO) and zinc oxide (ZnO) nanocomposites. The increased surface area, conductivity, and stability of graphene oxide in zinc oxide nanoparticles make the composite an ideal option for energy storage. X-ray diffraction (XRD) confirmed the crystallite size of 17.41 nm for the nanocomposite and the presence of GO (12.8°) peaks. The scanning electron microscope (SEM) showed anchored wrinkled GO sheets on zinc oxide with an average particle size of 2.93 μm. Energy-dispersive X-ray spectroscopy (EDX) confirmed the elemental composition, and Fourier-transform infrared spectroscopy (FTIR) revealed the impact of GO on functional groups and electrochemical behavior. Photoluminescence (PL) wavelength of (439 nm) and band gap of (2.81 eV) show that the material is suitable for energy applications in nanocomposites. Smart nanocomposite materials with improved performance in energy storage and related applications were fabricated by combining synthesis, characterization, fuzzy logic, and machine learning in this work.