Construction of NiS/carbon fibers confined NiS composite: high catalytic activity for enhancing the hydrogen storage performances of MgH <sub>2</sub>
Bibliographic record
Abstract
Abstract To effectively enhance the catalytic activity of NiS, NiS particles confined into carbon fibers were prepared by electrostatic spinning followed pyrolyzation and NiS particles decorating was performed by further hydrothermal loading. The decorated NiS exhibits particle (NiS@PAN‐NiS) and needle‐like (NiS@PAN‐NiS*) morphologies. After adding the catalysts into MgH 2 , the synthesized MgH 2 ‐5 wt% NiS@PAN‐NiS composite can absorb 2.6 wt% hydrogen at 353 K and release 5.0 wt% hydrogen within 1 h at 573 K. The initial hydrogen desorption temperature was reduced to 539 K. The activation energies for hydrogen absorption/desorption were greatly reduced to 66.76 and 89.95 kJ mol −1 , respectively. The method of confining by electrospinning and particle decoration by hydrothermal loading reduce NiS particle agglomeration. The Mg 2 Ni/Mg 2 NiH 4 hydrogen pump formed by reaction between NiS and MgH 2 effectively enhanced hydrogen absorption and desorption kinetics. The formed MgS also improved the catalytic activity on the transformation of Mg and MgH 2 . Moreover, the carbon fibers should influence the contact between in situ formed MgS and Mg 2 Ni, providing more catalytic sites and hydrogen diffusion pathways. The construction of NiS/carbon fibers confined NiS composite by carbon fibers derived from pyrolyzation as medium provides considerable way for designing NiS‐based catalysts to enhance the hydrogen storage performances of MgH 2 .
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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".