Hydrogen Sorption Cycling Kinetic Stability and Microstructure of Single-Walled Carbon Nanotube (SWCNT) Magnesium Hydride (MgH<sub>2</sub>) Nanocomposites
Bibliographic record
Abstract
We have examined comilling with unpurified single-walled carbon nanotubes (SWCNTs) as a method to promote hydrogenation/dehydrogenation cycling kinetic stability in nanocrystalline magnesium hydride (MgH 2 ). The synthesized material was a true nanocomposite consisting of MgH 2 covered by highly defective SWCNTs coupled to catalytic metal nanoparticles and mixed with amorphous carbon. The nanocomposite was hydrogen sorption cycled at 300 °C using a volumetric Sievert’s type apparatus. Identically milled pure MgH 2 was used as a baseline. The microstructure of both materials was analyzed in detail using cryo-stage transmission electron microscopy (TEM) as well as other techniques. The nanocomposite shows markedly improved kinetic performance, both during initial postmilling desorption and during subsequent cycling. Activation energy analysis demonstrates that any catalytic effect due to the metallic nanoparticles is lost during cycling. Improved cycling performance is instead achieved as a result of the carbon allotropes preventing MgH 2 particle agglomeration and sintering. Even after 35 absorption/desorption cycles, the SWCNTs remain covering the MgH 2 surfaces. Sorption cycling creates a dramatic difference in the particle size distributions between the nanocomposite system and the baseline, whereas the two were nearly identical at the onset of testing. In a separate experiment performed at more aggressive pressure conditions, the nanocomposite received over 100 sorption cycles with fairly minor kinetic degradation.
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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".