Nanostructured, complex hydride systems for hydrogen generation
Bibliographic record
Abstract
Complex hydride systems for hydrogen (H2) generation for supplying fuel cells are being reviewed. In the first group, the hydride systems that are capable of generating H2 through a mechanical dehydrogenation phenomenon at the ambient temperature are discussed. There are few quite diverse systems in this group such as lithium alanate (LiAlH4) with the following additives: nanoiron (n-Fe), lithium amide (LiNH2) (a hydride/hydride system) and manganese chloride MnCl2 (a hydride/halide system). Another hydride/hydride system consists of lithium amide (LiNH2) and magnesium hydride (MgH2), and finally, there is a LiBH4-FeCl2 (hydride/halide) system. These hydride systems are capable of releasing from ~4 to 7 wt.% H2 at the ambient temperature during a reasonably short duration of ball milling. The second group encompasses systems that generate H2 at slightly elevated temperature (up to 100 °C). In this group lithium alanate (LiAlH4) ball milled with the nano-Fe and nano-TiN/TiC/ZrC additives is a prominent system that can relatively quickly generate up to 7 wt.% H2 at 100 °C. The other hydride is manganese borohydride (Mn(BH4)2) obtained by mechano-chemical activation synthesis (MCAS). In a ball milled (2LiBH4 + MnCl2) nanocomposite, Mn(BH4)2 co-existing with LiCl can desorb ~4.5 wt.% H2 at 100 °C within a reasonable duration of dehydrogenation. Practical application aspects of hydride systems for H2 generation/storage are also briefly discussed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".