The Influence of Morphology on the Hydrogen Storage Properties of Magnesium Based Materials Processed by Cold Rolling
Bibliographic record
Abstract
In this communication we report the effect of macro and microstructure on the hydrogen storage properties of magnesium based materials. Magnesium hydride is an attractive material for hydrogen storage applications since it has a high hydrogen volumetric density. Furthermore, the high enthalpy of hydride formation makes it attractive for thermal energy storage applications. Besides, magnesium is an abundant and low cost material. However, the Mg/MgH2 system requires high operating temperatures due to its thermodynamic stability and slow hydrogen absorption and desorption kinetics. Magnesium’s first hydrogenation is a very long and costly process. This work aims to ameliorate this process which would effectively reduce the cost of MgH2. Commercial pure magnesium samples were processed by cold rolling. After processing, the samples presented limited hydrogen absorption due to their small surface area to volume ratio. To overcome this problem the samples were then reduced to powder using a bastard file. The samples were characterized by scanning electron microscopy and presented different morphology. Hydrogen storage properties and morphology are discussed and correlated. Results show an important improvement on the hydrogen absorption and desorption kinetics for the comminuted samples.
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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 | 0.001 |
| 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".