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Visualization of spatial inhomogeneity in the superconducting gap using micro-ARPES.
Yudai Miyai1, Shigeyuki Ishida2, Kenichi Ozawa3,4
1Graduate School of Advanced Science and Engineering, Hiroshima University, Higashi-Hiroshima, Japan.
Science and Technology of Advanced Materials
|November 19, 2024
Summary
High-resolution spatially-resolved ARPES reveals micro-scale superconducting gap and scattering rate inhomogeneity in cuprate superconductors. This technique offers a data-driven approach to understanding complex material properties.
Area of Science:
- Condensed Matter Physics
- Materials Science
- Superconductivity
Background:
- Electronic inhomogeneity is common in strongly correlated electron systems due to competing phases.
- Gap inhomogeneity in high-temperature cuprate superconductors is well-documented via scanning tunneling microscopy/spectroscopy.
- Angle-resolved photoemission spectroscopy (ARPES) has been limited in evaluating this inhomogeneity due to resolution constraints.
Purpose of the Study:
- To investigate the spatial dependence of electronic states in optimally-doped Bi2Sr2CaCu2O8+δ using high-resolution ARPES.
- To spatially map the superconducting gap and single-particle scattering rate at the micro-scale.
- To statistically evaluate the correlations between these physical parameters.
Main Methods:
- Utilized high-resolution spatially-resolved ARPES with a micrometric beam (micro-ARPES).
- Performed detailed spectral lineshape analysis on the spatial mapping dataset.
- Employed statistical evaluation of physical parameters and their correlations.
Main Results:
- Revealed the spatial inhomogeneity of the superconducting gap at the micro-scale.
- Identified spatial variations in the single-particle scattering rate.
- Established correlations between the superconducting gap and scattering rate inhomogeneity.
Conclusions:
- High-resolution spatially-resolved ARPES (micro-ARPES) is effective for probing micro-scale electronic inhomogeneity in cuprate superconductors.
- This technique enables a data-driven approach to understanding complex material properties.
- The findings provide key parameters for formulating physical properties of superconducting materials.
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