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Metal-organic frameworks and their derivatives for green energy fuel: Hydrogen production

  • Indrashil University
  • Ulsan National Institute of Science and Technology
  • Institute for Basic Science
  • University of California at Los Angeles

Research output: Contribution to journalArticlepeer-review

Abstract

Sustainability concerns raised by the alarming increase in atmospheric CO2 concentration to 425 ppm driven by uncontrolled fossil fuel consumption can be mitigated through the adoption of environmentally friendly fuel. Hydrogen, as a clean fuel, has emerged as a sustainable solution to address pressing challenges such as the energy crisis and environmental degradation. Approximately 96% global hydrogen production relies on the cracking of fossil fuels: releasing substantial amount of CO2 contributing to around 2% of global emission. The alternate hydrogen production strategies, like electrochemical, photocatalytic, or the combination thereof, are considered green production methods and are envisioned to circumvent the use of carbon-emitting fuels. The efficiency of these processes largely depends on the catalysts, which is largely influenced by the synergy between active sites and surrounding microenvironment. This review places particular emphasis on the utilization, current challenges and limitation of metal–organic frameworks (MOFs) and their derivatives as catalysts for sustainable hydrogen production via electrochemical and photocatalytic process. In addition, we have highlighted artificial intelligence/machine learning (AI/ML) that is reshaping MOF-based hydrogen production by unifying literature grounded synthesis and structural data. We have discussed an AI-driven, modular workflow that links design, modelling, and experiment which optimizes MOF catalyst activity, durability, and manufacturability accelerating progress from data to devices.

Original languageEnglish
Article number116841
Number of pages25
JournalRenewable and Sustainable Energy Reviews
Volume232
DOIs
Publication statusPublished - May 2026

Keywords

  • Artificial Intelligence and Machine learning
  • Electrocatalysis
  • Green hydrogen generation
  • Metal-organic frameworks
  • Photocatalysis
  • Sustainable energy resources

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