Anionic Disorder and Its Impact on the Surface Electronic Structure of Oxynitride Photoactive Semiconductors
The conversion of solar energy into chemical energy, stored in the form of hydrogen, bears enormous potential as a sustainable fuel for powering emerging technologies. Photoactive oxynitrides are promising materials for splitting water into molecular oxygen and hydrogen. However, one of the issues limiting widespread commercial use of oxynitrides is degradation during operation. While recent studies have shown the loss of nitrogen, its relation to reduced efficiency has not been directly and systematically addressed with experiments. In this study, we demonstrate the impact of the anionic stoichiometry of BaTaOxNy on its electronic structure and functional properties. Through experimental ion scattering, electron microscopy, and photoelectron spectroscopy investigations, we determine the anionic composition ranging from the bulk toward the surface of BaTaOxNy thin films. This further serves as input for band structure computations modeling the substitutional disorder of the anion sites. Combining our experimental and computational approaches, we reveal the depth-dependent elemental composition of oxynitride films, resulting in downward band bending and the loss of semiconducting character toward the surface. Extending beyond idealized systems, we demonstrate the relation between the electronic properties of real oxynitride photoanodes and their performance, providing guidelines for engineering highly efficient photoelectrodes and photocatalysts for clean hydrogen production.
Authors
- Thorsten Schmitt (ORCID: https://orcid.org/0000-0002-5737-1094)
- Procopios Constantinou (ORCID: https://orcid.org/0000-0001-9769-9345)
- Vladimir N. Strocov (ORCID: https://orcid.org/0000-0002-1147-8486)
- Daniele Pergolesi (ORCID: https://orcid.org/0000-0002-6231-0237)
- Arnold Müller (ORCID: https://orcid.org/0009-0008-9552-075X)
- Thomas K. Lippert (ORCID: https://orcid.org/0000-0001-8559-1900)
- J. Minář (ORCID: https://orcid.org/0000-0001-9735-8479)
- Anna Hartl (ORCID: https://orcid.org/0000-0002-8615-2755)
- Fatima Alarab (ORCID: https://orcid.org/0000-0003-0491-5120)
- Nick A. Shepelin (ORCID: https://orcid.org/0000-0002-5446-9213)
- Christof Vockenhuber (ORCID: https://orcid.org/0000-0002-2141-0733)
- Vladimir Roddatis (ORCID: https://orcid.org/0000-0002-9584-0808)
Institutions
- Paul Scherrer Institute (CH)
- ETH Zurich (CH)
- GFZ Helmholtz Centre for Geosciences (DE)
- University of West Bohemia in Pilsen (CZ)
Publication Details
- Journal
- Chemistry of Materials
- Published
- 2024-11-27
- DOI
- https://doi.org/10.1021/acs.chemmater.4c01832
- Citations
- 1
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.12
Funders
- Ministerstvo Školství, Mládeže a Tělovýchovy
- Paul Scherrer Institut
- European Regional Development Fund