Insights into unlocking the latent photocatalytic H2 production activity in the protonated Aurivillius-phase layered perovskite Na0.5Bi2.5Nb2O9
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Accepted version
Author(s)
Type
Journal Article
Abstract
The introduction of protonated interlayers in layered perovskite compounds has already demonstrated promising results in terms of photocatalytic activity. However, the mechanisms behind the observed enhancements remain unexplored. Here, we report a rapid and efficient proton exchange process for Na0.5Bi2.5Nb2O9 (ABNO), involving selective leaching of (Bi2O2)2- layers accompanied by the introduction of interlayer H+. This process, using acid treatment at room temperature is completed within only 24 h, the fastest method to date for a layered perovskite. Protonation induces changes at the molecular and electronic level, investigated using Synchrotron-based techniques, diffused reflectance spectroscopy (DRS), DFT calculation, and transient absorption spectroscopy (TAS), influencing the electronic band structure, surface properties, and charge carrier dynamics of the compounds. After protonation, BET surface area increases by > 20 times, to 156.19 m2/g. These structural and surface modifications unlock the material's latent photocatalytic potential, enabling H+ exchanged Na0.5Bi2.5Nb2O9 (HABNO) to achieve a H2 production rate of 242 μmol/h/g. This work delves into the photocatalytic mechanism, revealing how substitution by H+ provides more active sites and enhances the ability of the material to generate more highly reactive electrons that can participate in H2O reduction. This study highlights the promising strategy of altering the structure and electronic properties of layered materials through protonation to improve their performance for applications in photocatalysis for a cleaner and more sustainable future.
Date Issued
2025-06-01
Date Acceptance
2025-02-08
Citation
Materials Research Bulletin, 2025, 186
ISSN
0025-5408
Publisher
Elsevier BV
Journal / Book Title
Materials Research Bulletin
Volume
186
Copyright Statement
© 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Publication Status
Published
Article Number
113352
Date Publish Online
2025-02-09