The influence of SWCNT–metallic nanoparticle mixtures on the desorption properties of milled MgH<sub>2</sub>powders
Bibliographic record
Abstract
We have examined the effect of single-walled carbon nanotube (SWCNT)-metallic nanoparticle additions on the hydrogen desorption behavior of MgH(2) after high-energy co-milling. The metallic nanoparticles were the catalysts used for the SWCNT growth. The co-milling consisted of high-energy planetary milling in an inert argon environment of the hydride powder mixed with the SWCNTs. Identically milled pure MgH(2) powders were used as a baseline. The composites were tested using a combined differential scanning calorimeter and thermogravimetric analyzer, while the microstructures were examined using a variety of techniques including x-ray diffraction and transmission electron microscopy (TEM). We found that the SWCNT-nanoparticle additions do have an influence on the desorption kinetics. However, the degree to which they are effective depends on the composite's final state. The optimum microstructure for sorption, obtained after 1 h of co-milling, consists of highly defective SWCNTs in intimate contact with metallic nanoparticles and with the hydride. This microstructure is optimum, presumably because of the dense and uniform coverage of the defective SWCNTs on the MgH(2) surface. Prolonged co-milling of 7 h destroys the SWCNT structure and reduces the enhancement. Even after 72 h of co-milling, when the SWCNTs are completely destroyed, the metallic nanoparticles remain dispersed on the hydride surfaces. This indicates that the metallic nanoparticles alone are not responsible for the enhanced sorption and that there is indeed something catalytically unique about a defective SWCNT-metal combination. Cryo-stage TEM analysis of the hydride powders revealed that they are nanocrystalline and in some cases multiply twinned. To our knowledge this is the first study where the structure of milled alpha- MgH(2) has been directly imaged. Since defects are an integral component of hydride-to-metal phase transformations, such analysis sheds new insight regarding the fundamental microstructural origins of the sorption enhancement due to mechanical milling.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".