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

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

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

1 Citation (Scopus)
12 Downloads (Pure)


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.

Original languageEnglish
Number of pages38
JournalEnvironmental Chemistry Letters
Early online date16 May 2024
Publication statusEarly online date - 16 May 2024


  • Economics of hydrogen storage
  • High-entropy alloys
  • Hydrogen storage
  • Machine learning
  • Sorbent materials
  • Storage efficiency

ASJC Scopus subject areas

  • Environmental Chemistry


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