A Review of Recent Advances on the Effects of Microstructural Refinement and Nano-Catalytic Additives on the Hydrogen Storage Properties of Metal and Complex Hydrides
Bibliographic record
Abstract
The recent advances on the effects of microstructural refinement and various nano-catalytic additives on the hydrogen storage properties of metal and complex hydrides obtained in the last few years in the allied laboratories at the University of Waterloo (Canada) and Military University of Technology (Warsaw, Poland) are critically reviewed in this paper. The research results indicate that microstructural refinement (particle and grain size) induced by ball milling influences quite modestly the hydrogen storage properties of simple metal and complex metal hydrides. On the other hand, the addition of nanometric elemental metals acting as potent catalysts and/or metal halide catalytic precursors brings about profound improvements in the hydrogen absorption/desorption kinetics for simple metal and complex metal hydrides alike. In general, catalytic precursors react with the hydride matrix forming a metal salt and free nanometric or amorphous elemental metals/intermetallics which, in turn, act catalytically. However, these catalysts change only kinetic properties i.e. the hydrogen absorption/desorption rate but they do not change thermodynamics (e.g., enthalpy change of hydrogen sorption reactions). It is shown that a complex metal hydride, LiAlH4, after high energy ball milling with a nanometric Ni metal catalyst and/or MnCl2 catalytic precursor, is able to desorb relatively large quantities of hydrogen at RT, 40 and 80 °C. This kind of behavior is very encouraging for the future development of solid state hydrogen 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 | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".