Role of compositionally complex and high entropy alloys for hydrogen storage: A bibliometric and mechanistic assessment
B.S. Ramatsoma, Mamookho Elizabeth Makhatha, Desmond Klenam, Michael Oluwatosin Bodunrin
Abstract
Compositionally complex and high entropy alloys have emerged as promising candidates for hydrogen storage in the transition towards sustainable energy systems. This study provides a comprehensive bibliometric and systematic review of HEA-based hydrogen storage, analysing 214 publications from the Scopus database (2010–2024). Using VOSviewer, the analysis identifies key research trends, influential authors, and collaborative networks while pinpointing research gaps. Results highlight the dominance of BCC and Laves phase HEAs due to their superior hydrogen storage capacities, structural stability, and tunable thermodynamic properties. Mechanistic insights reveal that high configurational entropy, severe lattice distortions, and sluggish diffusion significantly impact hydrogen absorption/desorption kinetics. Computational modeling and machine learning are identified as crucial for accelerating alloy design and optimization. Challenges remain, including slow kinetics, limited cycling stability, and activation requirements. Overcoming these challenges necessitates interdisciplinary research, focusing on optimizing storage capacity and kinetics, investigating microstructural influences, and expanding datasets for improved machine learning predictions. This study integrates bibliometric and mechanistic insights, providing a valuable resource for researchers and practitioners. By addressing these challenges and fostering global collaboration, HEAs can pave the way for the widespread adoption of hydrogen energy and contribute significantly to sustainable energy innovations. • HEA metal hydrides show varied hydrogen storage behaviors and thermodynamic properties. • Arc melting is the dominant manufacturing method for HEA hydrogen storage alloys. • Microstructure critically influences hydrogen storage and activation in HEA hydrides. • DFT supports machine learning frameworks for predicting HEA hydrogen storage behaviors.