RESEARCH PAPER
Deep learning-driven underwater polarimetric target detection based on the dispersion of polarization characteristics
Guochen Wang, Jie Gao, Yanfa Xiang, Yuhua Li, Khian‐Hooi Chew, Rui‐Pin Chen
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
Guochen Wang, Jie Gao, Yanfa Xiang, Yuhua Li, Khian‐Hooi Chew, Rui‐Pin Chen. Deep learning-driven underwater polarimetric target detection based on the dispersion of polarization characteristics. Optics & Laser Technology (2024). https://doi.org/10.1016/j.optlastec.2024.110549
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