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利用微角分辨光电子能谱可视化超导能隙中的空间不均匀性。

Visualization of spatial inhomogeneity in the superconducting gap using micro-ARPES.

作者信息

Miyai Yudai, Ishida Shigeyuki, Ozawa Kenichi, Yoshida Yoshiyuki, Eisaki Hiroshi, Shimada Kenya, Iwasawa Hideaki

机构信息

Graduate School of Advanced Science and Engineering, Hiroshima University, Higashi-Hiroshima, Japan.

Research Institute for Advanced Electronics and Photonics, National Institute of Advanced Industrial Science and Technology, Ibaraki, Japan.

出版信息

Sci Technol Adv Mater. 2024 Aug 5;25(1):2379238. doi: 10.1080/14686996.2024.2379238. eCollection 2024.

Abstract

Electronic inhomogeneity arises ubiquitously as a consequence of adjacent and/or competing multiple phases or orders in strongly correlated electron systems. Gap inhomogeneity in high- cuprate superconductors has been widely observed using scanning tunneling microscopy/spectroscopy. However, it has yet to be evaluated by angle-resolved photoemission spectroscopy (ARPES) due to the difficulty in achieving both high energy and spatial resolutions. Here, we employ high-resolution spatially-resolved ARPES with a micrometric beam (micro-ARPES) to reveal the spatial dependence of the antinodal electronic states in optimally-doped Bi Sr CaCu O . Detailed spectral lineshape analysis was extended to the spatial mapping dataset, enabling the identification of the spatial inhomogeneity of the superconducting gap and single-particle scattering rate at the micro-scale. Moreover, these physical parameters and their correlations were statistically evaluated. Our results suggest that high-resolution spatially-resolved ARPES holds promise for facilitating a data-driven approach to unraveling complexity and uncovering key parameters for the formulation of various physical properties of materials.

摘要

在强关联电子系统中,由于相邻和/或竞争的多个相或序,电子不均匀性普遍存在。利用扫描隧道显微镜/能谱已广泛观察到高铜酸盐超导体中的能隙不均匀性。然而,由于难以同时实现高能量分辨率和空间分辨率,尚未通过角分辨光电子能谱(ARPES)进行评估。在此,我们采用具有微米级光束的高分辨率空间分辨ARPES(微ARPES)来揭示最佳掺杂的Bi₂Sr₂CaCu₂O₈中反节点电子态的空间依赖性。详细的谱线形状分析扩展到空间映射数据集,从而能够在微观尺度上识别超导能隙和单粒子散射率的空间不均匀性。此外,对这些物理参数及其相关性进行了统计评估。我们的结果表明,高分辨率空间分辨ARPES有望促进采用数据驱动的方法来揭示复杂性,并揭示用于描述材料各种物理性质的关键参数。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/debd/11573342/787537e5f760/TSTA_A_2379238_UF0001_OC.jpg

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