Orientation-dependent electron-phonon coupling in interfacial superconductors LaAlO3/KTaO3
Xiaoyang Chen, Tianlun Yu, Yuan Liu, Yanqiu Sun, Minyinan Lei, Nan, Guo, Yu Fan, Xingtian Sun, Meng Zhang, Fatima Alarab, Vladimir N. Strokov,, Yilin Wang, Tao Zhou, Xinyi Liu, Fanjin Lu, Weitao Liu, Yanwu Xie, Rui Peng,, Haichao Xu, Donglai Feng

TL;DR
This study reveals that orientation-dependent electron-phonon coupling at LaAlO3/KTaO3 interfaces influences superconductivity, with a quasi-three-dimensional electron gas and varying coupling strengths across different orientations, impacting transition temperatures.
Contribution
It demonstrates that interfacial orientation affects electron-phonon coupling strength and the dimensionality of the electron gas, providing insights into orientation-dependent superconductivity in oxide interfaces.
Findings
Electron-phonon coupling strength varies with interface orientation.
A quasi-three-dimensional electron gas exists at all orientations.
Stronger coupling correlates with higher superconducting transition temperature.
Abstract
The emergent superconductivity at the LaAlO3/KTaO3 interfaces exhibits a mysterious dependence on the KTaO3 crystallographic orientations. Here we show, by soft X-ray angle-resolved photoemission spectroscopy, that the interfacial superconductivity is contributed by mobile electrons with unexpected quasi-three-dimensional character, beyond the "two-dimensional electron gas" scenario in describing oxide interfaces. At differently-oriented interfaces, the quasi-three-dimensional electron gas ubiquitously exists and spatially overlaps with the small q Fuchs-Kliewer surface phonons. Intriguingly, electrons and the Fuchs-Kliewer phonons couple with different strengths depending on the interfacial orientations, and the stronger coupling correlates with the higher superconducting transition temperature. Our results provide a natural explanation for the orientation-dependent superconductivity,…
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Taxonomy
TopicsElectronic and Structural Properties of Oxides · Semiconductor materials and devices · Machine Learning in Materials Science
