Catalyzed Light Hydride Nanomaterials Embedded in a Micro-Channels Hydrogen Storage Container
Bibliographic record
Abstract
Activated alloys synthesized by arc-melting were examined as catalysts for improving the hydrogen sorption characteristics of nanostructured magnesium hydride, proposed as a reversible hydrogen storage material. The MgH(2)-catalyst absorbing materials were prepared by ball milling of pure MgH(2) with hydrided Zr(47)Ni(53), Zr(9)Ni(11), and other alloys investigated. The nanostructured MgH(2)-intermetallic systems were tested at 250 degrees C and catalyst addition of eutectoid Zr(47)Ni(53) resulted in the fastest desorption time and highest initial desorption rate. The catalyzed Mg-hydride with activated Zr(9)Ni(11) and Zr(7)Ni(10) phases showed fast desorption kinetics. Moreover, the results demonstrated that the composition of dispersed Zr(x)Ni(y)catalysts has a strong influence on the amount of accumulated hydrogen and desorption rate of Mg-nanocomposite. Part two covers advanced micro-channels hydrogen storage module design based on the results of semi-empirical computer simulations of heat and mass transfers in the container. The micro-channels reservoir concept offers many advantages over the conventional metal hydride hydrogen storage system. It is a micro-structured system that can pack a lot of power into a small space and dissipate effectively the heat of the sorption reactions. This review summarizes recent patents related to CNTS.
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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 | 0.001 |
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".