Hydrogen Energy Storage via Carbon-Based Materials: From Traditional Sorbents to Emerging Architecture Engineering and AI-Driven Optimization

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Publicado no:Energies vol. 18, no. 15 (2025), p. 3958-3994
Autor principal: Fu, Han
Outros Autores: Amin, Mojiri, Wang, Junli, Zhao, Zhe
Publicado em:
MDPI AG
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100 1 |a Fu, Han  |u NSF Nanosystems Engineering Research Center for Nanotechnology-Enabled Water Treatment, School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ 85287, USA; amojiri@asu.edu (A.M.); jwang821@asu.edu (J.W.); zzhao146@asu.edu (Z.Z.) 
245 1 |a Hydrogen Energy Storage via Carbon-Based Materials: From Traditional Sorbents to Emerging Architecture Engineering and AI-Driven Optimization 
260 |b MDPI AG  |c 2025 
513 |a Review 
520 3 |a Hydrogen is widely recognized as a key enabler of the clean energy transition, but the lack of safe, efficient, and scalable storage technologies continues to hinder its broad deployment. Conventional hydrogen storage approaches, such as compressed hydrogen storage, cryo-compressed hydrogen storage, and liquid hydrogen storage, face limitations, including high energy consumption, elevated cost, weight, and safety concerns. In contrast, solid-state hydrogen storage using carbon-based adsorbents has gained growing attention due to their chemical tunability, low cost, and potential for modular integration into energy systems. This review provides a comprehensive evaluation of hydrogen storage using carbon-based materials, covering fundamental adsorption mechanisms, classical materials, emerging architectures, and recent advances in computationally AI-guided material design. We first discuss the physicochemical principles driving hydrogen physisorption, chemisorption, Kubas interaction, and spillover effects on carbon surfaces. Classical adsorbents, such as activated carbon, carbon nanotubes, graphene, carbon dots, and biochar, are evaluated in terms of pore structure, dopant effects, and uptake capacity. The review then highlights recent progress in advanced carbon architectures, such as MXenes, three-dimensional architectures, and 3D-printed carbon platforms, with emphasis on their gravimetric and volumetric performance under practical conditions. Importantly, this review introduces a forward-looking perspective on the application of artificial intelligence and machine learning tools for data-driven sorbent design. These methods enable high-throughput screening of materials, prediction of performance metrics, and identification of structure–property relationships. By combining experimental insights with computational advances, carbon-based hydrogen storage platforms are expected to play a pivotal role in the next generation of energy storage systems. The paper concludes with a discussion on remaining challenges, utilization scenarios, and the need for interdisciplinary efforts to realize practical applications. 
653 |a Hydrogen 
653 |a Chemical bonds 
653 |a Bibliometrics 
653 |a Energy storage 
653 |a Artificial intelligence 
653 |a Pore size 
653 |a Graphene 
653 |a Sorbents 
653 |a Temperature 
653 |a Activated carbon 
653 |a Optimization 
653 |a Adsorption 
700 1 |a Amin, Mojiri  |u NSF Nanosystems Engineering Research Center for Nanotechnology-Enabled Water Treatment, School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ 85287, USA; amojiri@asu.edu (A.M.); jwang821@asu.edu (J.W.); zzhao146@asu.edu (Z.Z.) 
700 1 |a Wang, Junli  |u NSF Nanosystems Engineering Research Center for Nanotechnology-Enabled Water Treatment, School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ 85287, USA; amojiri@asu.edu (A.M.); jwang821@asu.edu (J.W.); zzhao146@asu.edu (Z.Z.) 
700 1 |a Zhao, Zhe  |u NSF Nanosystems Engineering Research Center for Nanotechnology-Enabled Water Treatment, School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ 85287, USA; amojiri@asu.edu (A.M.); jwang821@asu.edu (J.W.); zzhao146@asu.edu (Z.Z.) 
773 0 |t Energies  |g vol. 18, no. 15 (2025), p. 3958-3994 
786 0 |d ProQuest  |t Publicly Available Content Database 
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856 4 0 |3 Full Text + Graphics  |u https://www.proquest.com/docview/3239025077/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3239025077/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch