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Enhanced hydrogen storage efficiency with sorbents and machine learning: a review
Environmental Chemistry Letters ( IF 15.7 ) Pub Date : 2024-05-16 , DOI: 10.1007/s10311-024-01741-3
Ahmed I. Osman , Walaa Abd-Elaziem , Mahmoud Nasr , Mohamed Farghali , Ahmed K. Rashwan , Atef Hamada , Y. Morris Wang , Moustafa A. Darwish , Tamer A. Sebaey , A. Khatab , Ammar H. Elsheikh

Hydrogen is viewed as the future carbon–neutral fuel, yet hydrogen storage is a key issue for developing the hydrogen economy because current storage techniques are expensive and potentially unsafe due to pressures reaching up to 700 bar. As a consequence, research has recently designed advanced hydrogen sorbents, such as metal–organic frameworks, covalent organic frameworks, porous carbon-based adsorbents, zeolite, and advanced composites, for safer hydrogen storage. Here, we review hydrogen storage with a focus on hydrogen sources and production, advanced sorbents, and machine learning. Carbon-based sorbents include graphene, fullerene, carbon nanotubes and activated carbon. We observed that storage capacities reach up to 10 wt.% for metal–organic frameworks, 6 wt.% for covalent organic frameworks, and 3–5 wt.% for porous carbon-based adsorbents. High-entropy alloys and advanced composites exhibit improved stability and hydrogen uptake. Machine learning has allowed predicting efficient storage materials.



中文翻译:

利用吸附剂和机器学习提高储氢效率:综述

氢被视为未来的碳中性燃料,但氢存储是发展氢经济的关键问题,因为当前的存储技术价格昂贵,而且由于压力高达 700 bar,可能不安全。因此,最近的研究设计了先进的氢吸附剂,例如金属有机骨架、共价有机骨架、多孔碳基吸附剂、沸石和先进复合材料,以实现更安全的储氢。在这里,我们回顾氢存储,重点关注氢源和生产、先进吸附剂和机器学习。碳基吸附剂包括石墨烯、富勒烯、碳纳米管和活性炭。我们观察到金属有机骨架的存储容量高达 10 wt.%,共价有机骨架的存储容量为 6 wt.%,多孔碳基吸附剂的存储容量为 3–5 wt.%。高熵合金和先进复合材料表现出更高的稳定性和氢吸收率。机器学习可以预测高效的存储材料。

更新日期:2024-05-16
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