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Machine learning for spectral precision:a new horizon in radiative cooling material design
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作者 Xinpeng Hu Mingxiang Liu +3 位作者 Xuemei Fu Guangming Tao Xiang Lu Jinping Qu 《Science Bulletin》 2025年第24期4122-4124,共3页
Radiative cooling has emerged as a sustainable strategy for passive thermal management,offering a promising route to reduce global energy demand and mitigate the effects of climate change[1-3].By leveraging the atmosp... Radiative cooling has emerged as a sustainable strategy for passive thermal management,offering a promising route to reduce global energy demand and mitigate the effects of climate change[1-3].By leveraging the atmospheric window in the mid-infrared region(8-13μm),thermal radiation can be emitted directly into outer space,enabling cooling without the need for power input[4].This approach,grounded in fundamental thermodynamic principles,has attracted significant attention in various fields,including materials science,photonics,architecture,and atmospheric physics[5]. 展开更多
关键词 radiative cooling thermodynamic principleshas passive thermal managementoffering spectral precision atmospheric window mitigate effects climate change reduce global energy demand machine learning
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