Hydrogen Cycling of Niobium and Vanadium Catalyzed Nanostructured Magnesium
Bibliographic record
Abstract
The reaction of hydrogen gas with magnesium metal, which is important for hydrogen storage purposes, is enhanced significantly by the addition of catalysts such as Nb and V and by using nanostructured powders. In situ neutron diffraction on MgNb(0.05) and MgV(0.05) powders give a detailed insight on the magnesium and catalyst phases that exist during the various stages of hydrogen cycling. During the early stage of hydriding (and deuteriding), a MgH(1< x < 2) phase is observed, which does not occur in bulk MgH(2) and, thus, appears characteristic for the small particles. The abundant H vacancies will cause this phase to have a much larger hydrogen diffusion coefficient, partly explaining the enhanced kinetics of nanostructured magnesium. It is shown that under relevant experimental conditions, the niobium catalyst is present as NbH(1). Second, a hitherto unknown Mg-Nb perovskite phase could be identified that has to result from mechanical alloying of Nb and the MgO layer of the particles. Vanadium is not visible in the diffraction patterns, but electron micrographs show that the V particle size becomes very small, 2-20 nm. Nanostructuring and catalyzing the Mg enhance the adsorption speed that much that now temperature variations effectively limit the absorption speed and not, as for bulk, the slow kinetics through bulk MgH(2) layers.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".