RESEARCH PAPER
High-performance deep-learning based polarization computational ghost imaging with random patterns and orthonormalization
Chenxiang Xu, Dekui Li, Xueqiang Fan, Bing Lin, Kai Guo, Zhiping Yin, Zhongyi Guo
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
Chenxiang Xu, Dekui Li, Xueqiang Fan, Bing Lin, Kai Guo, Zhiping Yin, Zhongyi Guo. High-performance deep-learning based polarization computational ghost imaging with random patterns and orthonormalization. Physica Scripta (2023). https://doi.org/10.1088/1402-4896/acd089
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