Accelerated photonic design of coolhouse film for photosynthesis via machine learning
Abstract 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 consumpti...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-54983-8 |
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| author | Jinlei Li Yi Jiang Bo Li Yihao Xu Huanzhi Song Ning Xu Peng Wang Dayang Zhao Zhe Liu Sheng Shu Juyou Wu Miao Zhong Yongguang Zhang Kefeng Zhang Bin Zhu Qiang Li Wei Li Yongmin Liu Shanhui Fan Jia Zhu |
| author_facet | Jinlei Li Yi Jiang Bo Li Yihao Xu Huanzhi Song Ning Xu Peng Wang Dayang Zhao Zhe Liu Sheng Shu Juyou Wu Miao Zhong Yongguang Zhang Kefeng Zhang Bin Zhu Qiang Li Wei Li Yongmin Liu Shanhui Fan Jia Zhu |
| author_sort | Jinlei Li |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-4ba0a45b789c4d2696ed6425e08867d9 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-4ba0a45b789c4d2696ed6425e08867d92025-02-09T12:45:50ZengNature PortfolioNature Communications2041-17232025-02-0116111110.1038/s41467-024-54983-8Accelerated photonic design of coolhouse film for photosynthesis via machine learningJinlei Li0Yi Jiang1Bo Li2Yihao Xu3Huanzhi Song4Ning Xu5Peng Wang6Dayang Zhao7Zhe Liu8Sheng Shu9Juyou Wu10Miao Zhong11Yongguang Zhang12Kefeng Zhang13Bin Zhu14Qiang Li15Wei Li16Yongmin Liu17Shanhui Fan18Jia Zhu19National Laboratory of Solid State Microstructures, Nanjing UniversityNational Laboratory of Solid State Microstructures, Nanjing UniversityGPL Photonics Laboratory, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of SciencesDepartment of Mechanical and Industrial Engineering and Department of Electrical and Computer Engineering, Northeastern UniversitySchool of Civil and Environmental Engineering, University of New South WalesNational Laboratory of Solid State Microstructures, Nanjing UniversityCollege of Horticulture, Nanjing Agricultural UniversitySchool of Geography and Ocean Science, Nanjing UniversityCollege of Horticulture, Nanjing Agricultural UniversityCollege of Horticulture, Nanjing Agricultural UniversityCollege of Horticulture, Nanjing Agricultural UniversityNational Laboratory of Solid State Microstructures, Nanjing UniversitySchool of Geography and Ocean Science, Nanjing UniversitySchool of Civil and Environmental Engineering, University of New South WalesNational Laboratory of Solid State Microstructures, Nanjing UniversityCollege of Optical Science and Engineering, Zhejiang UniversityGPL Photonics Laboratory, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of SciencesDepartment of Mechanical and Industrial Engineering and Department of Electrical and Computer Engineering, Northeastern UniversityDepartment of Electrical Engineering, Ginzton Laboratory, Stanford UniversityNational Laboratory of Solid State Microstructures, Nanjing UniversityAbstract 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.https://doi.org/10.1038/s41467-024-54983-8 |
| spellingShingle | Jinlei Li Yi Jiang Bo Li Yihao Xu Huanzhi Song Ning Xu Peng Wang Dayang Zhao Zhe Liu Sheng Shu Juyou Wu Miao Zhong Yongguang Zhang Kefeng Zhang Bin Zhu Qiang Li Wei Li Yongmin Liu Shanhui Fan Jia Zhu Accelerated photonic design of coolhouse film for photosynthesis via machine learning Nature Communications |
| title | Accelerated photonic design of coolhouse film for photosynthesis via machine learning |
| title_full | Accelerated photonic design of coolhouse film for photosynthesis via machine learning |
| title_fullStr | Accelerated photonic design of coolhouse film for photosynthesis via machine learning |
| title_full_unstemmed | Accelerated photonic design of coolhouse film for photosynthesis via machine learning |
| title_short | Accelerated photonic design of coolhouse film for photosynthesis via machine learning |
| title_sort | accelerated photonic design of coolhouse film for photosynthesis via machine learning |
| url | https://doi.org/10.1038/s41467-024-54983-8 |
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