A Comparative Analysis of Hydrogen Storage Characteristics in AZ31 Magnesium Alloy with the Addition of Graphene and Carbon Nanotubes via Ball Milling Process
Bibliographic record
Abstract
In the present investigation, an examination was conducted on the hydrogen storage performance of industrial waste grade AZ31 magnesium alloy when combined with either Carbon Nanotubes or Graphene. This study aims to understand the enhancement of hydrogen storage properties reinforced with polymer materials, such as Graphene or Carbon Nanotubes. The experimental samples, composed of AZ31 Magnesium Alloy combined with either Carbon Nanotubes or Graphene, were crafted through gravity casting. Thereafter, a high-energy ball milling process was employed to further refine the hydrogen storage material powders. The micrographic structures of all the sample powders were analyzed by x-ray diffraction (XRD), and scanning electron microscopy (SEM) coupled with energy dispersive spectroscopy (EDS). Additionally, the average particle size distributions of the sample powders were quantified for comprehensive characterization. The absorbed and desorbed hydrogen capacity and kinetics was calculated by a Sievert's type apparatus. Overall, the performance of the sample powder AZ31-0.1G showed the highest absorption and desorption at a rate of 0.0036 wt%/s and 0.0084 wt%/s. Moreover, the hydrogen capacity of AZ31-0.1G reached the highest value at 5.32 wt%. The acquired data unveils that with the adding of either Graphene or Carbon Nanotubes as additives significantly improved the hydrogen storage capacity of AZ31 magnesium alloy.
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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 | 0.001 |
| 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".