Sustainable Metal-Organic Framework Water Harvesters in the Artificial Intelligence Era 文章

ArXiv CS.AI2026-05-29NEWSen作者: Reid A. Coyle (Department of Chemistry, Washington University, St. Louis, MO, United States), Shyam Chand Pal (Department of Chemistry, Washington University, St. Louis, MO, United States), Peter Walther (Department of Chemistry, Washington University, St. Louis, MO, United States), Saeun Park (Department of Chemistry, Washington University, St. Louis, MO, United States), Bin Feng (Department of Chemistry, Washington University, St. Louis, MO, United States, Institute of Materials Science & Engineering, Washington University, St. Louis, MO, United States), Zhiling Zheng (Department of Chemistry, Washington University, St. Louis, MO, United States, Institute of Materials Science & Engineering, Washington University, St. Louis, MO, United States)

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arXiv:2605.29179v1 Announce Type: cross Abstract: Metal-organic frameworks (MOFs) are excellent candidates for water harvesting due to their tunable pore environments, which can be precisely engineered to capture and release water in arid conditions. Integrating artificial intelligence (AI) into MOF discovery can further accelerate the design of high-performance sorbents by identifying structural features that enhance atmospheric water harvesting (AWH), stability, and cycling efficiency. In this Perspective, we examine key MOF design principles, including cooperative adsorption, operational relative humidity (RH), uptake capacity, hysteresis, and scalability. We highlight recent design advancements such as multivariate strategies and long-arm linker extension, and examine how these principles tune pore capacity and hydrophilicity, while preserving stability and crystallinity.

Sustainable Metal-Organic Framework Water Harvesters in the Artificial Intelligence Era 文章

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