journal article·2023-08

CuxSey@polyaniline core-shell nanocomposites based type-II heterostructures as high-performance photocatalytic materials

Abdolali Moghaddam Saray, Hassanali Azimi, Abbas Shirmardi, Morteza Nouri, Ramin Yousefi

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Abstract

Cu xSe y @polyaniline core-shell nanocomposites with different polyaniline (PANI) concentrations (5 %, 10 %, and 15 %) have been synthesized as the photocatalytic materials with high performance under visible-light irradiation. Characterization results indicated a Cu xSe y /PANI core-shell heterostructure has been formed. In addition, the scanning electron microscope (SEM) and transmission electron microscope (TEM) images indicated the morphology of the pristine Cu xSe y nanostructures from disk morphology with an average diameter of around several micrometers has been changed to nanoparticle (NP) morphology with an average diameter of around 25 nm. The results of the photocatalytic tests of the samples showed that PANI had a synergistic role in the photocatalytic performance of the Cu xSe y nanostructures. However, the results showed that PANI has an optimal concentration to perform this role, which was 10 %. In fact, Cu xSe y /PANI (10 %) nanocomposites were able to degrade almost 100 % of methylene blue (MB) molecules in only 75 min. The results of the characterization techniques showed that PANI increased the porosity in nanocomposites and also created a type-II heterostructure with Cu xSe y nanostructures. Based on these results, the role of PANI as a synergistic factor in the photocatalytic process is to increase the absorption of more photons and inhibit the recombination of electron-holes created in the nanocomposite. In addition, PANI caused the creation of new defect levels in the structure of nanocomposites, and the resulting these new levels caused further degradation of dye molecules.
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Research context

OpenAlex topics: Advanced Photocatalysis Techniques · Conducting polymers and applications · Quantum Dots Synthesis And Properties

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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