Li Jinlei, Jiang Yi, Li Bo, Xu Yihao, Song Huanzhi, Xu Ning, Wang Peng, Zhao Dayang, Liu Zhe, Shu Sheng, Wu Juyou, Zhong Miao, Zhang Yongguang, Zhang Kefeng, Zhu Bin, Li Qiang, Li Wei, Liu Yongmin, Fan Shanhui, Zhu Jia
National Laboratory of Solid State Microstructures, Nanjing University, Nanjing, 210093, China.
College of Engineering and Applied Sciences, Nanjing University, Nanjing, 210093, China.
Nat Commun. 2025 Feb 6;16(1):1396. doi: 10.1038/s41467-024-54983-8.
Controlling the suitable light, temperature, and water is essential for plant photosynthesis. While greenhouses/warm-houses are effective in cold or dry climates by creating warm, humid environments, a cool-house that provides a cool local environment with minimal energy and water consumption is highly desirable but has yet to be realized in hot, water-scarce regions. Here, using a synergistic genetic algorithm and machine learning, we propose and demonstrate a coolhouse film that regulates temperature and water for photosynthesis without requiring additional energy or water. This scalable film, selected from hundreds of potential designs, selectively and precisely transmits sunlight needed for photosynthesis while reflecting excess heat, thereby reducing thermal load and evapotranspiration. Its optical properties also exhibit weak angle dependence. In demonstrations in subtropical and arid regions, the film reduces temperatures by 5-17 °C and cuts water loss by half, resulting in more than doubled biomass yield and survival rates. It also improves crop resistance to heat and drought in greenhouse cultivation. The integration of machine learning and photonics provides a powerful toolkit for designing photonic structures and devices aimed at sustainability.
控制适宜的光照、温度和水分对植物光合作用至关重要。虽然温室/暖房通过营造温暖、潮湿的环境在寒冷或干燥气候中很有效，但能以最少的能源和水消耗提供凉爽局部环境的凉房在炎热、缺水地区非常理想却尚未实现。在此，我们运用协同遗传算法和机器学习，提出并展示了一种用于光合作用的调节温度和水分且无需额外能源或水的凉房薄膜。这种可扩展的薄膜从数百种潜在设计中筛选出来，能选择性且精确地透射光合作用所需的阳光，同时反射多余热量，从而降低热负荷和蒸散量。其光学特性也显示出较弱的角度依赖性。在亚热带和干旱地区的示范中，该薄膜使温度降低5 - 17摄氏度，水分流失减半，生物量产量和存活率提高一倍多。它还能提高温室栽培中作物的耐热性和耐旱性。机器学习与光子学的结合为设计旨在实现可持续性的光子结构和器件提供了强大的工具包。
