Photo-Catalytic Activity Improvement for Organic Pollutant Removal in Wastewater using Zinc Oxide Quantum Dots: An Experimental and Modeling Study

Abstract

Photo-catalyst nanoparticles (NPs) find applications in many diverse fields, including environmental remediation, energy conversion, and organic synthesis. By optimizing the nanoparticle's composition, size, morphology, and surface properties, the photo-catalytic performance can be enhanced to develop more efficient and sustainable catalytic systems. This work aligns with this innovative approach and aims to improve the photo-catalytic degradation of Sulfamethoxazole (SMX) through the intensification of the photo-catalyst and the micro-reactor. ZnO-NPs were synthesized using the sol-gel method. Zinc Acetate (Z.A) and sodium hydroxide were used as precursor materials. The resulting ZnO-NPs were characterized for their structure and crystallinity using X-Ray Diffraction (XRD) and the photo-catalytic activity was assessed with a micro-structured polymer reactor. The degradation of SMX through photo-catalysis proceeds through several stages that involve coupled processes, such as the transportation of molecules and chemical reactions. To solve the mathematical equations governing the transport and photocatalytic reaction, COMSOL Multiphysics software was utilized. The characterization results demonstrate the excellent crystallinity and high purity of the synthesized ZnO-NPs, enabling the estimation of the average diameter of the NPs under different synthesis conditions. The grain growth is faster (3.5 hr) at higher temperatures (70, 80, and 90 °C), and slower (4 hr) at lower temperatures (50 and 60°C). The photo-catalytic degradation is significantly more efficient on 16 nm ZnO-NPs than 50 nm ZnO-NPs. At this size, the conversion rate reaches 96%, surpassing the performance of commercial ZnO-NPs, which only degrades 81% of SMX. The conversion rate obtained through simulation is slightly higher than that achieved in the experiments. However, this difference remains negligible, and overall, the model fits well with the experimental data. This validation of the chosen model confirms its reliability and accuracy.