Florian Brette, Stéphane Célérier, Christine Canaff, Lola Loupias, Michael Paris, Aurélien Habrioux, Florent Boucher, Vincent Mauchamp
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引用次数: 0
Abstract
MXenes are prototypes of surface tunable 2D materials with vast potential for properties tuning. Accurately characterizing their surface functionalization and its role in electronic structure is crucial, X-ray photoelectron spectroscopy (XPS) being among the go-to methods to do so. Despite extensive use, XPS analysis remains however intricate. Focusing on the benchmark MXene Ti3C2Tz, Density Functional Theory (DFT) calculations of core-level binding energy shifts (BE.s.) are combined with experiments in order to provide a quantitative interpretation of XPS spectra. This approach demonstrates that BE.s. are driven by the complex interplay between chemical, structural, and subtle electronic structure effects preventing analysis from intuitive arguments or comparison with reference materials. In particular, it is shown that O terminations induce the largest BE.s. at Ti 2p levels despite lower electronegativity than F. Additionally, F 1s levels show weak sensitivity to the F local environment, explaining the single contribution in the spectrum, whereas O 1s states are significantly affected by the local surface chemistry. Finally, clear indicators of surface group vacancies are given at Ti 2p and O 1s levels. These results demonstrate the combination of calculations with experiments as a method of the highest value for MXenes XPS spectra analysis, providing guidelines for otherwise complex interpretations.
Small MethodsMaterials Science-General Materials Science
CiteScore
17.40
自引率
1.60%
发文量
347
期刊介绍:
Small Methods is a multidisciplinary journal that publishes groundbreaking research on methods relevant to nano- and microscale research. It welcomes contributions from the fields of materials science, biomedical science, chemistry, and physics, showcasing the latest advancements in experimental techniques.
With a notable 2022 Impact Factor of 12.4 (Journal Citation Reports, Clarivate Analytics, 2023), Small Methods is recognized for its significant impact on the scientific community.
The online ISSN for Small Methods is 2366-9608.