New perspectives on potential hydrogen storage materials using high pressure
Bibliographic record
Abstract
In addressing the global demand for clean and renewable energy, hydrogen stands out as the most suitable candidate for many fuel applications that require practical and efficient storage of hydrogen. Supplementary to the traditional hydrogen storage methods and materials, the high-pressure technique has emerged as a novel and unique approach to developing new potential hydrogen storage materials. Static compression of materials may result in significant changes in the structures, properties and performance that are important for hydrogen storage applications, and often lead to the formation of unprecedented phases or complexes that have profound implications for hydrogen storage. In this perspective article, 22 types of representative potential hydrogen storage materials that belong to four major classes--simple hydride, complex hydride, chemical hydride and hydrogen containing materials--were reviewed. In particular, their structures, stabilities, and pressure-induced transformations, which were reported in recent experimental works together with supporting theoretical studies, were provided. The important contextual aspects pertinent to hydrogen storage associated with novel structures and transitions were discussed. Finally, the summary of the recent advances reviewed and the insight into the future research in this direction were given.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".