A response surface methodology optimization for efficient photocatalytic degradation over reusable CuxO/TiO2 nanocomposite on copper wire
Source abstract
Abstract
In this study, the CuxO/TiO2 (x=1,2) nanocomposite photocatalyst was prepared via chemical-thermal oxidation followed by the sol-gel method on the copper wire. The characterization analysis indicated the co-existence of two types of copper oxides along with TiO2 and proved the formation of multicomponent heterostructure between semiconductors. A mixture of nanowires nanoparticles morphology was observed for the sample. The estimated band gap energy of the nanocomposite was 1.85 eV, which can be suitable for photocatalytic activity under visible light irradiation. A three-level three-factor Box-Behnken design (BBD) based on the response surface methodology (RSM) approach was used to determine the effects of parameters including pH, initial dye concentration, and photocatalyst dosage on the photocatalytic degradation of methylene blue (MB) dye. The significance of the factors was confirmed by employing the analysis of variance (ANOVA) with a 95% of confidence level. The interaction effect evaluation revealed that the pH and dye concentration are the most important process variables. The optimal values of the operating factors, including a pH of 11, dye concentration of 2.5 mg L−1, and photocatalyst dosage of 0.011 g mL−1, were specified to achieve a superior dye degradation efficiency of around 99%. It has been demonstrated by the reusability experiments that its wire form allows it to have high stability and ease of recovery. The kinetics study demonstrated the well fitted of the Langmuir-Hinshelwood (L-H) model to the dye photodegradation by the nanocomposite. © 2023 Elsevier Ltd
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Advanced Photocatalysis Techniques · Copper-based nanomaterials and applications · TiO2 Photocatalysis and Solar Cells
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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