ZnO@Pt nanocomposites for enhanced amaranth dye degradation under UV–vis irradiation
Source abstract
Abstract
• ZnO@Pt nanocomposites studied for efficient amaranth dye degradation. • Nanocomposites with relatively high Pt loading show high photocatalytic activity. • QM calculations show enhanced AZO bond degradation in the dye due to Pt on ZnO surface. • Nanocomposites are suitable for wastewater treatment solutions as ecofriendly alternative. This work focuses on ZnO@Pt nanocomposites with Pt contents of 0, 2, 6, 10, and 20 wt% for the efficient degradation of red amaranth dye found in textile industry effluents. Nanocomposites were prepared using the Sol-Gel method from aqueous salt solutions of Zn nitrate, commercial Pt nanoparticles in water, and citric acid as a complexing agent. The powders exhibit a characteristic ZnO structure, as detected by X-ray diffraction measurements. Transmission electron microscopy was employed to measure nanoparticle diameters at different Pt concentrations, with the diameters measuring less than 100 nm. Results on photocatalytic activity highlight degradation efficiencies η eff between 57.3 % and 82.5 % for the corresponding Pt contents. The K constant values become nearly 1.3 times higher than those in pure ZnO as the Pt content increases, indicating a significant enhancement in the degradation rate. Quantum mechanical calculations, which consider a small Pt cluster on the surface of the ZnO nanoparticle, demonstrate an increase in the degradation of the AZO bond in red amaranth due to the presence of Pt, which reduces the bond-breaking energy by 0.23 eV. We have shown that ZnO@Pt nanocomposite materials can be effectively used for wastewater treatment through photodegradation as an environmentally friendly remediation alternative.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Dye analysis and toxicity · Mercury impact and mitigation studies · Dyeing and Modifying Textile Fibers
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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