Recent advancements in addressing environmental pollution caused by pharmaceutical chemicals have highlighted the effectiveness of cobalt-doped zinc oxide nanoparticles (Co-ZnO NPs) as catalysts for the degradation of ciprofloxacin (CIPF), a commonly prescribed antibiotic. Under optimum conditions described in the new study, these nanoparticles achieve remarkable degradation rates of over 99% within just 90 minutes of exposure to visible LED light.
Ciprofloxacin, widely used for treating bacterial infections, has become problematic due to its persistence as a contaminant found across various ecosystems, attributed to partial degradation and excretion through biological processes. This antibiotic not only poses risks to aquatic life but also contributes to the development of antibiotic resistance among pathogens, making the quest for effective degradation methods urgent.
To investigate the potential of Co-ZnO NPs for breaking down CIPF, researchers synthesized nanoparticles using the co-precipitation method, optimizing the doping concentration of cobalt ions, and characterizing the resulting structures. Through methods like X-ray diffraction (XRD) and scanning electron microscopy (SEM), the study confirmed the successful formation of nanoparticles with sizes ranging between 38.47 nm to 48.06 nm.
Experimental designs employed response surface methodology (RSM), which allowed for fine-tuning multiple parameters affecting CIPF photodegradation. Key parameters included pH, initial CIPF concentration, the weight of the catalyst, and shaking speed. The researchers determined the optimal conditions to maximize degradation efficiency were at a pH of 6.486, with shaking speeds of 134.39 rpm, catalyst doses of 54.071 mg, and initial drug concentrations at 31.04 ppm, resulting in approximately 93.99% CIPF degradation efficiency.
What stood out was the demonstration of the first-order kinetics governing the degradation process, supported by kinetic studies confirming the suitability of the reaction model with strong correlations observed (R2 = 0.9703). Remarkably, the Co-ZnO NPs maintained effective photocatalytic performance across multiple cycles of use, confirming their reusability as catalysts for wastewater treatment.
Artificial neural network (ANN) modeling played a significant role as well, where the study developed predictive models based on experimental data. The most effective configuration comprised three hidden layers, yielding very high correlation values (R2 = 0.9780) indicating the robustness of the predictions, facilitating more efficient experimental designs.
By integrating advanced techniques such as RSM and ANN, the researchers not only showcased the potential of Co-ZnO NPs but established a comprehensive approach for optimizing photocatalytic processes. This could offer promising pathways for wastewater management strategies aimed at reducing pharmaceutical waste, protecting aquatic environments, and combating antibiotic resistance.
The findings of this study signal significant contributions to both theoretical and practical realms of photocatalytic research, striving for sustainable solutions to combat pharmaceutical pollution. Follow-up studies could explore the economic feasibility of this technology and its applicability across diverse pollutant channels, paving the way for broader adoption.
Future research may focus on enhancing the photocatalytic efficiency of ZnO nanoparticles with other dopants and analyzing long-term operational impacts and costs associated with implementing such technology at industrial scales. This study undoubtedly lays the groundwork for more innovative approaches to tackle the pervasive challenge of antibiotic contamination.