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Perspective:imaging atomic step geometry to determine surface terminations of kagome materials and beyond

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摘要 Here we review scanning tunneling microscopy research on the surface determination for various types of kagome materials,including 11-type(CoSn,FeSn,FeGe),32-type(Fe_(3)Sn_(2)),13-type(Mn_(3)Sn),135-type(AV_(3)Sb_(5),A=K,Rb,Cs),166-type(TbMn6Sn_(6),YMn_(6)Sn_(6)and ScV_(6)Sn_(6)),and 322-type(Co_(3)Sn_(2)S_(2)and Ni_(3)In_(2)Se_(2)).We first demonstrate that the measured step height between different surfaces typically deviates from the expected value of±0.4∼0.8Å,which is owing to the tunneling convolution effect with electronic states and becomes a serious issue for Co_(3)Sn_(2)S_(2)where the expected Sn-S interlayer distance is 0.6Å.Hence,we put forward a general methodology for surface determination as atomic step geometry imaging,which is fundamental but also experimentally challenging to locate the step and to image with atomic precision.We discuss how this method can be used to resolve the surface termination puzzle in Co_(3)Sn_(2)S_(2).This method provides a natural explanation for the existence of adatoms and vacancies,and beyond using unknown impurity states,we propose and use designer layer-selective substitutional chemical markers to confirm the validity of this method.Finally,we apply this method to determine the surface of a new kagome material Ni_(3)In_(2)Se_(2),as a cousin of Co_(3)Sn_(2)S_(2),and we image the underlying kagome geometry on the determined Se surface above the kagome layer,which directly visualizes the p-d hybridization physics.We emphasize that this general method does not rely on theory,but the determined surface identity can provide guidelines for first-principles calculations with adjustable parameters on the surface-dependent local density of states and quasi-particle interference patterns.
出处 《Quantum Frontiers》 2024年第1期82-94,共13页 量子前沿(英文)
基金 support from the National Key R&D Program of China(No.2023YFA1407300) the National Science Foundation of China(No.12374060) Project funded by China Postdoctoral Science Foundation(No.2023M741546,No.2023M731530) The work at Princeton is supported by Gordon and Betty Moore Foundation(GBMF4547 and GBMF9461,M.Z.H.).
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