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

Institutions

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

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Anionic Disorder and Its Impact on the Surface Electronic Structure of Oxynitride Photoactive Semiconductors

Thorsten Schmitt, Procopios Constantinou, Vladimir N. Strocov, Daniele Pergolesi et al.
1 citations
Chemistry of Materials
Machine Learning in Materials Science
0.12
article

Anionic Disorder and Its Impact on the Surface Electronic Structure of Oxynitride Photoactive Semiconductors

Thorsten Schmitt, Procopios Constantinou, Vladimir N. Strocov, Daniele Pergolesi, Arnold Müller, Thomas K. Lippert, J. Minář, Anna Hartl, Fatima Alarab, Nick A. Shepelin, Christof Vockenhuber, Vladimir Roddatis
article en
1 citations

Abstract

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.

Chemistry of MaterialsVol. 36(23)
Paul Scherrer Institute (CH), ETH Zurich (CH), GFZ Helmholtz Centre for Geosciences (DE), University of West Bohemia in Pilsen (CZ)
Ministerstvo Školství, Mládeže a Tělovýchovy, Paul Scherrer Institut, European Regional Development Fund
Openalex Percentile: Top 58%
Machine Learning in Materials Science
0.12
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.