Enhancing Wastewater Treatment Through Python ANN-Guided Optimization of Photocatalysis with Boron-Doped ZnO Synthesized via Mechanochemical Route
Loading...
Date
2025
Authors
Nedelkovski, Vladan
Radovanović, Milan B.
Medić, Dragana
Stanković, Sonja
Hulka, Iosif
Tanikić, Dejan
Antonijević, Milan
Journal Title
Journal ISSN
Volume Title
Publisher
MDPI
Source
Processes
Volume
13
Issue
7
Abstract
This study explores the enhanced photocatalytic performance of boron-doped zinc oxide (ZnO) nanoparticles synthesized via a scalable mechanochemical route. Utilizing X-ray diffraction (XRD) and scanning electron microscopy with energy-dispersive spectroscopy (SEM-EDS), the structural and morphological properties of these nanoparticles were assessed. Specifically, nanoparticles with 1 wt%, 2.5 wt%, and 5 wt% boron doping were analyzed after calcination at temperatures of 500 °C, 600 °C, and 700 °C. The obtained results indicate that 1 wt% B-ZnO nanoparticles calcined at 700 °C show superior photocatalytic efficiency of 99.94% methyl orange degradation under UVA light—a significant improvement over undoped ZnO. Furthermore, the study introduces a predictive model using the artificial neural network (ANN) technique, developed in Python, which effectively forecasts photocatalytic performance based on experimental conditions with R2 = 0.9810. This could further enhance wastewater treatment processes, such as heterogeneous photocatalysis, through ANN-guided optimization.
Description
Keywords
optimization, doped ZnO, nanoparticles, neural networks, photocatalysis
Citation
DOI
10.3390/pr13072240
Scopus
ISSN
2227-9717
ISBN
License
CC-BY