A review of enhanced hydrogen storage in MgH2: the role of high-energy reactive ball milling and catalysis
Bibliographic record
Abstract
Due to its significant hydrogen capacity (7.6 wt%), availability, and reversibility, magnesium hydride (MgH2) is considered one of the most promising solid-state hydrogen storage materials, making it attractive for sustainable energy systems. The excellent thermodynamic stability and slow absorption/desorption kinetics, which require elevated operating temperatures, limit its practical application. This paper addresses the important issue of how advanced synthesis methods, specifically high-energy reactive ball milling and catalytic doping, can overcome inherent challenges and enable the practical use of MgH2 for hydrogen storage. The methodology adopted is a systematic and integrative review of state-of-the-art experimental and theoretical studies, focusing on thermodynamic and kinetic fundamentals, synthesis routes, catalytic additives, and nanostructuring strategies. Results indicate that high-energy ball milling significantly improves hydrogen diffusion by reducing particle sizes to the nanoscale and lowering the desorption onset temperature by approximately 45 °C. Catalysts such as 2 mol% Nb2O5 further reduce activation energy barriers, enabling rapid hydrogen release of 6.4 wt% in 114 s, while polymorphic transitions (γ-MgH2 formation) enhance structural stability. Despite these advances, challenges such as grain coarsening and cycling capacity loss remain, highlighting the importance of nanoencapsulation, alloying, and scalable fabrication techniques. In conclusion, the review provides a critical framework for understanding the synergistic role of ball milling and catalysis in tailoring MgH2 properties and outlines future research directions toward efficient, scalable, and application-ready hydrogen storage systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".