The Influence of Morphology on the Hydrogen Storage Properties of Magnesium Based Materials Processed by Cold Rolling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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/MgH 2 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 MgH 2 . 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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 it