A Promising Approach to Solid-State Hydrogen Storage: Mechanical Nanostructuring Synthesis of Magnesium by High Pressure Torsion Extrusion
Bibliographic record
Abstract
This article presents an investigation into the impact of High Pressure Torsion Extrusion (HPTE) on the microstructural features, hardness and hydrogen storage, focusing on pure magnesium. HPTE is a modern mechanical nanostructuring technique that can refine the microstructural properties and subsequently affects the mechanical and functional properties of the materials. Two HPTE regimes were used in this study: (1) Direct Extrusion without rotation (DE), and (2) an extrusion speed of 6 mm/min along with a rotational speed of 1.8 rpm (v6w1.8). One sample in as-received conditions was also tested as a reference. Results showed increased hardness in the material after HPTE processing, with the DE sample reaching 60 HRB and the v6w1.8 sample exhibiting a gradient distribution of hardness from 71 to 83 HRB. X-ray diffraction analysis revealed significant microstructural refinement in the v6w1.8 sample. Results of hydrogenation kinetics showed that the DE sample absorbed up to 1.2 wt.% of hydrogen, while the v6w1.8 sample displayed 7.2 wt.% of hydrogen absorption, approaching the theoretical hydrogen storage capacity for magnesium (7.6 wt.%). These findings highlight the positive effects of HPTE on microstructural refinement and hydrogen storage, showcasing its potential for advancements in materials science and hydrogen-based energy technologies.
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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".