Alternative to the traditional force cooling technologies, daytime radiative cooling (DRC) has drawn widespread attention for its zero-power. A porous polymer coating based on poly (vinylidene fluoride-hexafluoropropylene) (PVDF-HFP) has been reported as it has excellent DRC capacity. However, performance of the PVDF-HFP coating is affected substantially by its preparation conditions, restricting its application. To resolve the issue, we utilize an artificial neural network (ANN) to predict its DRC capacity and obtain the best preparation condition by siftings. In this work, the predicted solar reflectance (${\bar{R}_{solar}}$) and emittance of the atmospheric transmittance window (${\bar{\varepsilon }_{atw}}$), under the optimal preparation condition, reach 0.983 and 0.932, with a 1.865% and 0.107% error from the experimental value, correspondingly. Noticeably, the optimal PVDF-HFP coating achieves about 6℃ temperature drops below ambient temperature during daytime. In addition, to extend its applications in space, we conduct the extreme environmental experiment on the PVDF-HFP coating. After exposing in the extreme environment, ${\bar{R}_{solar}}$ of the coating has degradation rate over 11%. Consequently, these simulative methods and experimental results provide a positive direction for fabricating the high-performance DRCs.

中文翻译：

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辐射冷却涂层的逆向设计、制造和对极端环境的耐受性

替代传统的强制冷却技术，日间辐射冷却（DRC）因其零功耗而受到广泛关注。据报道，基于聚偏二氟乙烯-六氟丙烯 (PVDF-HFP) 的多孔聚合物涂层具有出色的 DRC 容量。然而，PVDF-HFP 涂层的性能受其制备条件的影响很大，限制了其应用。为了解决这个问题，我们利用人工神经网络 (ANN) 来预测其 DRC 容量并通过筛选获得最佳制备条件。在这项工作中，预测的太阳反射率 ( ${\bar{R}_{solar}}$ ) 和大气透射率窗口的发射率 ( ${\bar{\varepsilon }_{atw}}$)，在最佳制备条件下，分别达到0.983和0.932，与实验值的误差分别为1.865%和0.107%。值得注意的是，最佳的 PVDF-HFP 涂层在白天实现了低于环境温度约 6℃ 的温度下降。此外，为了扩展其在太空中的应用，我们对 PVDF-HFP 涂层进行了极端环境实验。暴露于极端环境后，${\bar{R}_{solar}}$的涂层降解率超过11%。因此，这些模拟方法和实验结果为制造高性能 DRC 提供了积极的方向。