RESEARCH PAPER
High-quality 3D shape recovery from scattering scenario via deep polarization neural networks
Kui Yang, Pingli Han, Rui Gong, Meng Xiang, Jinpeng Liu, Jinpeng Liu, Fan Zhao, Teli Xi, Fei Liu, Bingjian Wang, Xiaopeng Shao
Research context
Research on electrical energy conversion, storage and generation dates back to the nineteenth century, but only in recent years have scientists begun to investigate the impact of electron spin on these processes. The ability to control and manipulate this intrinsically quantum property of matter opens new approaches to addressing energy science challenges. The chiral-induced spin selectivity (CISS) effect is central to this effort, as it enables control over the transport and generation of both pure spin currents and spin-polarized charge currents. In this Review, we first introduce design strategies for implementing CISS in materials and then describe examples of how CISS has been used to improve electrocatalysis and spintronics. We conclude with a forward-looking perspective on the next steps for leveraging CISS in energy science.
Source & review
Bibliographic record reviewed for relevance and publication quality. Full-text findings have not been extracted; consult the original publication for methods and results.
Cite / 引用
Kui Yang, Pingli Han, Rui Gong, Meng Xiang, Jinpeng Liu, Jinpeng Liu, Fan Zhao, Teli Xi, Fei Liu, Bingjian Wang, Xiaopeng Shao. High-quality 3D shape recovery from scattering scenario via deep polarization neural networks. Optics and Lasers in Engineering (2023). https://doi.org/10.1016/j.optlaseng.2023.107934
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