Development of AgCuS nanostructures with optimized photocatalytic efficiency under solar irradiation
Source abstract
Abstract
Wastewater generated by the textile industry contains high levels of various pollutants. Advanced conventional methods, such as chemical and electrical treatments, are effective in addressing these contaminants. However, the significant operational and capital costs associated with these conventional systems limit their accessibility for industrial stakeholders. In contrast, more economically viable methods tend to be less efficient. This study aims to identify a suitable approach for integrating photocatalytic degradation (PCD) with a low-cost method to enhance the cost-effectiveness of wastewater treatment processes in the textile sector. The study utilized silver copper sulfide (AgCuS) nanocomposites as a photocatalyst, annealed at 200 °C for varying durations. A sustainable and cost-effective approach was implemented by combining photocatalytic degradation with floating treatment wetlands. Initially, the textile wastewater underwent 12 hours of PCD treatment using AgCuS nanocomposites. Subsequently, the entire batch was subjected to FTW treatment. The integrated treatment of wastewater with PCD and FTW achieved compliance with the National Environmental Quality Standards (NEQS) within nine days. Analysis revealed that the integrated system significantly reduced chemical oxygen demand (COD) by 89% and biological oxygen demand (BOD) by 90% in the effluent. Plants in the FTWs receiving PCD-pretreated effluent exhibited reduced stress compared to those in other treatments. Toxicity tests confirmed that the treated wastewater was non-toxic. The combination of photocatalysts and FTWs emerges as a highly suitable approach for optimal wastewater treatment in the textile industry.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: TiO2 Photocatalysis and Solar Cells · Advanced Photocatalysis Techniques · Water Quality Monitoring and Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (30)
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