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Hauptverfasser: Zhang, Zhaocheng, Xu, Jiahao, Hou, Pengran, Deng, Yang
Format: Preprint
Veröffentlicht: 2024
Schlagworte:
Online-Zugang:https://arxiv.org/abs/2407.02050
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author Zhang, Zhaocheng
Xu, Jiahao
Hou, Pengran
Deng, Yang
author_facet Zhang, Zhaocheng
Xu, Jiahao
Hou, Pengran
Deng, Yang
contents We designed a multilayered self-adaptive absorber/emitter metamaterial, which can smartly switch between a solar absorber and a radiative cooler based on temperature change. The switching capability is facilitated by the phase change material and the structure is optimized by machine learning. Our design not only advances the machine-learning-based development of metamaterials but also has the potential to significantly reduce carbon emissions and contribute to the goal of achieving carbon neutrality.
format Preprint
id arxiv_https___arxiv_org_abs_2407_02050
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Machine-learning designed smart coating: temperature-dependent self-adaptation between a solar absorber and a radiative cooler
Zhang, Zhaocheng
Xu, Jiahao
Hou, Pengran
Deng, Yang
Optics
Applied Physics
We designed a multilayered self-adaptive absorber/emitter metamaterial, which can smartly switch between a solar absorber and a radiative cooler based on temperature change. The switching capability is facilitated by the phase change material and the structure is optimized by machine learning. Our design not only advances the machine-learning-based development of metamaterials but also has the potential to significantly reduce carbon emissions and contribute to the goal of achieving carbon neutrality.
title Machine-learning designed smart coating: temperature-dependent self-adaptation between a solar absorber and a radiative cooler
topic Optics
Applied Physics
url https://arxiv.org/abs/2407.02050