Orbit- and Atom-Resolved Spin Textures of Intrinsic, Extrinsic and Hybridized Dirac Cone States

Combining first-principles calculations and spin- and angle-resolved photoemission spectroscopy measurements, we identify the helical spin textures for three different Dirac cone states in the interfaced systems of a 2D topological insulator (TI) of Bi(111) bilayer and a 3D TI Bi2Se3 or Bi2Te3. The spin texture is found to be the same for the intrinsic Dirac cone of Bi2Se3 or Bi2Te3 surface state, the extrinsic Dirac cone of Bi bilayer state induced by Rashba effect, and the hybridized Dirac cone between the former two states. Further orbit- and atom-resolved analysis shows that s and pz orbits have a clockwise (counterclockwise) spin rotation tangent to the iso-energy contour of upper (lower) Dirac cone, while px and py orbits have an additional radial spin component. The Dirac cone states may reside on different atomic layers, but have the same spin texture. Our results suggest that the unique spin texture of Dirac cone states is a signature property of spin-orbit coupling, independent of topology

The fate of the 2√3 × 2√3R(30°) silicene phase on Ag(111)

Silicon atoms deposited on Ag(111) produce various single layer silicene sheets with different buckling patterns and periodicities. Low temperature scanning tunneling microscopy reveals that one of the silicene sheets, the hypothetical √7 × √7 silicene structure, on 2√3 × 2√3 Ag(111), is inherently highly defective and displays no long-range order. Moreover, Auger and photoelectron spectroscopy measurements reveal its sudden death, to end, in a dynamic fating process at ∼300 °C. This result clarifies the real nature of the 2√3 × 2√3R(30°) silicene phase and thus helps to understand the diversity of the silicene sheets grown on Ag(111)

Advances of deep learning in electrical impedance tomography image reconstruction

Electrical impedance tomography (EIT) has been widely used in biomedical research because of its advantages of real-time imaging and nature of being non-invasive and radiation-free. Additionally, it can reconstruct the distribution or changes in electrical properties in the sensing area. Recently, with the significant advancements in the use of deep learning in intelligent medical imaging, EIT image reconstruction based on deep learning has received considerable attention. This study introduces the basic principles of EIT and summarizes the application progress of deep learning in EIT image reconstruction with regards to three aspects: a single network reconstruction, deep learning combined with traditional algorithm reconstruction, and multiple network hybrid reconstruction. In future, optimizing the datasets may be the main challenge in applying deep learning for EIT image reconstruction. Adopting a better network structure, focusing on the joint reconstruction of EIT and traditional algorithms, and using multimodal deep learning-based EIT may be the solution to existing problems. In general, deep learning offers a fresh approach for improving the performance of EIT image reconstruction and could be the foundation for building an intelligent integrated EIT diagnostic system in the future