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Enhanced hydrogen storage efficiency with sorbents and machine learning: a review

مؤلف البحث
Ahmed I. Osman· Walaa Abd‑Elaziem· Mahmoud Nasr· Mohamed Farghali· Ahmed K. Rashwan· Atef Hamada· Y. Morris Wang· Moustafa A. Darwish10 · Tamer A. Sebaey. Khatab, Ammar H. Elsheikh
ملخص البحث

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.
 

تاريخ البحث
مجلة البحث
Environmental Chemistry Letters
المشارك في البحث
الناشر
Springer
تصنيف البحث
Q1
عدد البحث
22
سنة البحث
2024
صفحات البحث
1703–1740