Nanoscale Grain Refinement and H‐Sorption Properties of MgH<sub>2</sub> Processed by High‐Pressure Torsion and Other Mechanical Routes
Bibliographic record
Abstract
Abstract MgH2 is a promising material for solid‐state hydrogen storage due to its high gravimetric and volumetric storage capacity and its relatively low cost. Severe plastic deformation (SPD) processing techniques are being explored as an alternative to high‐energy ball‐milling (HEBM) in order to obtain more air resistant materials and reduce processing times. In this work, Mg, MgH2, and MgH2–Fe mixtures were severely mechanically processed by different techniques such as high‐pressure torsion (HPT), extensive cold forging, and cold rolling. A very significant grain refinement was achieved when using MgH2 instead of Mg as raw material. The mean crystallite sizes observed ranged from 10 to 30 nm, depending on the processing conditions. Enhanced H‐sorption properties were observed for the MgH2‐based nanocomposites processed by HPT when compared with MgH2 mixtures. Additionally, cold forging and cold rolling also proved effective in nanostructuring MgH2. These results suggest a high potential for innovative application with the use of low cost mechanical processing routes to produce Mg‐based nanomaterials with attractive hydrogen storage properties.
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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 | 0.000 | 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".