[9p-E203-1~7] 1.3 Novel technologies and interdisciplinary engineering

1:45 PM - 2:00 PM JST(4:45 AM - 5:00 AM UTC)

[9p-E203-2] Exploration for radiative cooling materials through the integration of combinatorial sputtering and machine learning

〇Masahiro Goto¹, Michiko Sasaki¹, Ryo Tamura^1,2, Koji Tsuda^2,1 (1.NIMS, 2.UTokyo FS)

Keywords:

sky radiator,machine Learning,sputter

As global warming progresses, there is a growing interest in energy-efficient radiative cooling technologies. SkyAs global warming progresses, there is a growing interest in energy-efficient radiative cooling technologies. Sky radiators dissipate heat into outer space through the atmospheric window (8–13 μm). However, conventional materials have limited cooling efficiency. This study employed combinatorial sputtering and materials informatics (MI) to explore thin-film materials composed of aluminum (Al), silicon (Si), oxygen (O), and nitrogen (N). Optimizing the deposition conditions resulted in a novel filler material that exhibits selective emissivity within the atmospheric window region. These findings demonstrate the material's potential application as a high-performance filler for efficient sky radiators.