Elucidating Raman Auger Behavior in Cu, Cu2O and CuO by Resonant Auger Electron Spectroscopy to Interpret X-ray Absorption Spectroscopy.

IF 10.7 2区 材料科学 Q1 CHEMISTRY, PHYSICAL
Hui Zhang, Jing Zhou, Xiaobao Li, Yong Han, Nian Zhang, Zhi Liu
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引用次数: 0

Abstract

The understanding and modulation of electronic structures on copper and copper oxides are of great value in the fields of material science and condensed matter physics. Due to the full or nearly full 3d electrons, the band structures of Cu and its oxides have not been clearly investigated in terms of experiments and calculations. Here mapping of resonant Auger electron spectroscopy (mRAS) is utilized to understand the non-radiative decay process of core hole around the absorption threshold. The mRAS can be used for X-ray absorption spectroscopy (XAS) in high energy resolution electron detection mode (HERED), which can help interpret the Cu L3,2-edge XAS. Additionally, the mRAS results clearly distinguish the band-structure and exitonic contribution in XAS spectra. Together with the spectral calculations, the origin of asymmetrical main absorption peak in Cu L3-edge XAS of Cu2O can be explained. Moreover, the non-linear Raman profile around absorption threshold is also found in the mRAS of Cu2O and CuO, which correlated with the core-hole intermediate state in the XAS process. This work demonstrates the progressiveness of mRAS method for traditional absorption spectrum, which can enhance the capability to study the complex core-hole state in the copper and related systems.

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来源期刊
Small Methods
Small Methods Materials 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.
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