2025

Nedelkovski, Vladan 
Radovanović, Milan B. 
Medić, Dragana 
Stanković, Sonja 
Hulka, Iosif
Tanikić, Dejan 
Antonijević, Milan 

MDPI

Processes

13

7

2240

English

M22 - Paper in a prominent international journal

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.

optimization, doped ZnO, nanoparticles, neural networks, photocatalysis

DOI

10.3390/pr13072240

ISSN

2227-9717

URI

https://repozitorijum.tfbor.bg.ac.rs/handle/...