Photocatalytic dye degradation and antibacterial activity of gold nanoparticles: a DFT and machine learning study

Abstract

This study explores the kinetic and thermodynamic factors influencing the photocatalytic degradation of methyl orange (MO) using gold nanoparticles (AuNPs) synthesized via a green route with Acacia nilotica extract as a reducing agent. The biosynthesized AuNPs exhibited excellent photocatalytic efficiency, achieving 92.5% degradation of MO within 10 minutes under visible light in the presence of NaBH₄, and retained 88.4% activity after four successive cycles, demonstrating high reusability. Antibacterial activity was also confirmed against Salmonella typhi and Lactobacillus acidophilus. To validate and interpret the experimental outcomes, density functional theory (DFT) simulations were performed, examining the Fermi level, HOMO–LUMO gap, work function, topological properties (ELF, LOL), and thermal stability of AuNPs. In parallel, machine learning (ML) models, including XGBoost, LightGBM, and Neural Networks, were employed to predict electronic band gaps. The XGBoost model showed the highest accuracy with a root mean square error of 0.9878 and a mean squared error of 0.00035, while other models also produced results consistent with DFT values. This combined experimental, theoretical, and data-driven approach highlights the promise of AuNPs for efficient dye degradation and antibacterial applications, offering sustainable solutions for environmental remediation.