Highly emissive, selective and omnidirectional thermal emitters mediated by machine learning for ultrahigh performance passive radiative cooling

TL;DR

This paper presents a machine learning-designed hybrid metasurface thermal emitter with high emissivity, spectral selectivity, and wide emission angle, achieving record temperature reduction and potential urban heat island mitigation.

Contribution

It introduces a novel machine learning approach to design thermal emitters with simultaneous high emissivity, selectivity, and omnidirectionality, surpassing previous limitations.

Findings

Achieved emissivity of ~0.92 in 8-13 μm window

Realized temperature reduction of ~15.4°C under strong solar irradiation

Demonstrated potential for urban heat island mitigation

Abstract

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 complexity of controlling thermal emission of photonic structures in multidimension. Here we demonstrated machine learning mediated hybrid metasurface thermal emitters with a high emissivity of ~0.92 within the atmospheric transparency window 8-13 {\mu}m, 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 degree 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.