Oxidation state depth profiling by self-consistent fitting of all emission peaks in the X-ray photoelectron spectrum of SnTe
• ARXPS model resolves gradual SnO/SnTe interface from full-spectrum fitting. • Multi-peak analysis reduces uncertainty in chemical state depth profiling. • Unexplored low-intensity peaks constrain peak fitting with internal consistency. • Full-spectrum ARXPS fitting reduces error compared to single-peak fitting. • Method applied to SnTe, a topological crystalline insulator, reveals oxide depth profile. X-ray photoelectron spectroscopy (XPS) is among the most widely used methods for surface characterization. Currently, the analysis of XPS data is almost exclusively based on the main emission peak of a given element and the rest of the spectrum is discarded. This makes quantitative chemical state analyses by peak fitting prone to substantial error, especially in light of incomplete and flawed reference literature. However, most elements give rise to multiple emission peaks in the X-ray energy range, which are virtually never analyzed. For samples with an inhomogeneous depth distribution of chemical states, these peaks show different but interdependent ratios of signal components, as they correspond to different information depths. In this work, we show that self-consistent fitting of all emission peaks lends additional reliability to the depth profiling of chemical states by angular-resolved (AR-)XPS. We demonstrate this using a natively oxidized thin film of the topological crystalline insulator tin telluride (SnTe). This approach is not only complementary to existing depth profiling methods, but may also pave the way towards complete modelling of the XPS spectrum, facilitating a more comprehensive and holistic understanding of the surface chemistry of solids.
Authors
- G. Jakob (ORCID: https://orcid.org/0000-0001-9466-0840)
- Martin Wortmann (ORCID: https://orcid.org/0000-0002-5101-4643)
- J. Schmalhorst (ORCID: https://orcid.org/0000-0003-1622-9726)
- Maik Gaerner (ORCID: https://orcid.org/0000-0002-5578-0046)
- Natalie Frese
- Timo Kuschel (ORCID: https://orcid.org/0000-0002-9371-8876)
- Gabi Schierning (ORCID: https://orcid.org/0000-0003-2591-2463)
- Beatrice Bednarz (ORCID: https://orcid.org/0000-0002-2419-8918)
- Klaus Viertel (ORCID: https://orcid.org/0000-0001-8851-8434)
- Inga Ennen
- Olga Kuschel
- Negin Beryani Nezafat (ORCID: https://orcid.org/0000-0003-1636-5146)
- Andreas Hütten
- J. Wollschläger (ORCID: https://orcid.org/0000-0002-3043-3718)
Institutions
- Osnabrück University (DE)
- Johannes Gutenberg University Mainz (DE)
- Bielefeld University (DE)
- Hochschule Bielefeld (DE)
- Hochschule Osnabrück (DE)
- University of Duisburg-Essen (DE)
- Ruhr University Bochum (DE)
Publication Details
- Journal
- Applied Surface Science
- Published
- 2025-08-18
- DOI
- https://doi.org/10.1016/j.apsusc.2025.164356
- Citations
- 1
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.52
Funders
- European Commission
- Deutsche Forschungsgemeinschaft
- Universität Bielefeld
- Horizon 2020 Framework Programme
- European Research Council
- H2020 Marie Skłodowska-Curie Actions