journal article·2023-04Open access

Steered photogenerated carrier separation via mixed-crystal-homojunction-mediated permanent negative charge electric field

Xinyu Xiao, Ying Han, Chao Liu, Xing Wang

Abstract

The chelation between heavy metal ions and small organic molecules limits the prospects of traditional adsorption methods in dealing with the complex water environment, annihilating the non-negligible reutilization value of heavy metal ions. Photocatalysis enables the reduction of heavy metal ions and the mineralization of small molecule pollutants to be accomplished simultaneously, but the enhancement of photogenerated carrier separation efficiency is a technical challenge to be overcome. Herein, a photocatalyst loaded with mixed-crystal titanium dioxide (TiO 2 ) on the surface of montmorillonite (MMT) was prepared, which is reported for the exceptional performance in the recovery of heavy metals Cr(VI) and the mineralization of organic pollutants Rh-B (10 ppm chelate in total) within 60 min. Scientific characterization and theoretical calculations demonstrate that the generation of oxygen vacancies in mixed-crystal homojunctions driven by a permanent negative charge electric field will maximize the adsorption capacity for H 2 O and O 2 . The MMT surface is converted into a reduction platform for Cr(VI), and anatase-TiO 2 acts as an active site for Rh-B mineralization, under the synergistic effect of permanent negative charge electric field and mixed-crystal homojunctions. The Cr(III) obtained by photocatalytic reduction is collected by cation exchange between the interlayers of MMT with corresponding selective adsorption. This novel structural design concept provides a highly innovative orientation for the application of photocatalytic technology to the remediation of complex aqueous environments.
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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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