¹ Institute of Photonic Chips, University of Shanghai for Science and Technology, Shanghai 200093, China
中国 上海 上海理工大学 光子芯片研究院
² Centre for Artificial-Intelligence Nanophotonics, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
中国 上海 上海理工大学 光电信息与计算机工程学院 人工智能纳米光子学中心
Real-world passive radiative cooling requires highly emissive, selective, and omnidirectional thermal emitters to maintain the radiative cooler at a certain temperature below the ambient temperature while maximizing the net cooling power. Despite various selective thermal emitters have been demonstrated, it is still challenging to achieve these conditions simultaneously because of the extreme difficulty in controlling thermal emission of photonic structures in multidimension.
Here we demonstrated hybrid polar dielectric metasurface thermal emitters with machine learning inverse design, enabling a high emissivity of ~0.92 within the atmospheric transparency window 8–13 μm, a large spectral selectivity of ~1.8 and a wide emission angle up to 80 degrees, simultaneously. This selective and omnidirectional thermal emitter has led to a new record of temperature reduction as large as ~15.4 °C under strong solar irradiation of ~800 W/m2, significantly surpassing the state-of-the-art results.
The designed structures also show great potential in tackling the urban heat island effect, with modelling results suggesting a large energy saving and deployment area reduction. This research will make significant impact on passive radiative cooling, thermal energy photonics and tackling global climate change.
