Solid‐state Materials and Methods for Hydrogen Storage: A Critical Review
Bibliographic record
Abstract
Abstract Hydrogen is important as a new source of energy for automotive applications. It is clear that the key challenge in developing this technology is hydrogen storage. Current methods for hydrogen storage have yet to meet all the demands for on‐board applications. High‐pressure gas storage or liquefaction cannot fulfill the storage criteria required for on‐board storage. Solid‐state materials have shown potential advantages for hydrogen storage in comparison to other storage methods. In this article, the most popular solid‐state storage materials and methods including carbon based materials, metal hydrides, metal organic frameworks, hollow glass microspheres, capillary arrays, clathrate hydrates, metal nitrides and imides, doped polymer and zeolites, are critically reviewed. The survey shows that most of the materials available with high storage capacity have disadvantages associated with slow kinetics and those materials with fast kinetics have issues with low storage capacity. Most of the chemisorption‐based materials are very expensive and in some cases, the hydrogen absorption/desorption phenomena is irreversible. Furthermore, a very high temperature is required to release the adsorbed hydrogen. On the other hand, the main drawback in the case of physisorption‐based materials and methods is their lower capacity for hydrogen storage, especially under mild operating conditions. To accomplish the requisite goals, extensive research studies are still required to optimize the critical parameters of such systems, including the safety (to be improved), security (to be available for all), cost (to be lowered), storage capacity (to be increased), and the sorption‐desorption kinetics (to be improved).
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".