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Open Access

ARTICLE

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

Tahir Mahmood1,, Muhammad Waseem Ashraf1,, Shahzadi Tayyaba2, Muhammad Munir3, Babiker M. A. Abdel-Banat3 and Hassan Ali Dinar3
1 Department of Electronics, Institute of Physics, Government College University, Lahore, 54000, Pakistan
2 Department of Information Sciences, Division of Science and Technology, University of Education, Township Campus, Lahore, 54000, Pakistan
3 Date Palm Research Centre of Excellence, King Faisal University, Al-Ahsa, 31982, Saudi Arabia
* Corresponding Authors: Tahir Mahmood. Email: tahirmahmood4489@gmail.com; Muhammad Waseem Ashraf. Email: dr.waseem@gcu.edu.pk
(This article belongs to the Special Issue: Advanced Modeling of Smart and Composite Materials and Structures)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.072436

Received 27 August 2025; Accepted 23 October 2025; Published online 10 December 2025

Abstract

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.

Keywords

Graphene oxide; nanocomposites; fuzzy logic; supercapacitor; optical properties; machine learning; energy storage

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