Advancing CuO/2D-clay nano-architectures for the photocatalytic removal of rhodamine B and atrazine from aqueous systems
Source abstract
Abstract
The growing demand for environmentally friendly and sustainable advanced oxidation processes necessitates the development of efficient, mineral-based photocatalysts for water remediation. This study evaluated the effectiveness of a novel CuO−2D layered clay composite for the heterogeneous photocatalytic degradation of significant organic pollutants, rhodamine B (RhB) dye and atrazine (ATZ) herbicide. The composite was synthesized via a simple hydrothermal method involving the reaction of an aqueous dispersion of Cu 2+ -exchanged clay and an ammonium hydroxide solution. The resulting hybrid material was thoroughly characterized using XRD, UV-Vis DRS, FTIR, FE-SEM-EDX mapping, and TEM, confirming the successful and uniform immobilization of spherical CuO nanoparticles onto the 2D layered clay sheets. The CuO-Clay composite demonstrated superior photocatalytic performance, achieving an impressive degradation efficiency of approximately 92.72% for RhB and 75.9% for ATZ within 90 min. The electrocatalytic performance for the oxygen evolution reaction (OER) was further evaluated, revealing that the CuO-clay composite exhibited superior catalytic activity and long-term stability compared to the pristine CuO. This enhanced performance is attributed to the synergistic interaction between the metal oxide and the layered silicate matrix, which promotes efficient charge transfer. Consequently, the CuO-clay composite emerges as a highly promising, durable, and cost-effective candidate for integrated environmental remediation, demonstrating exceptional versatility in both the electrochemical generation of reactive species and the simultaneous degradation of multi-class organic pollutants.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced oxidation water treatment · Advanced Photocatalysis Techniques · Copper-based nanomaterials and applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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