Catalytic Hydrogen Evolution of NaBH4 Hydrolysis by Cobalt Nanoparticles Supported on Bagasse-Derived Porous Carbon
Bibliographic record
Abstract
As a promising hydrogen storage material, sodium borohydride (NaBH4) exhibits superior stability in alkaline solutions and delivers 10.8 wt.% theoretical hydrogen storage capacity. Nevertheless, its hydrolysis reaction at room temperature must be activated and accelerated by adding an effective catalyst. In this study, we synthesize Co nanoparticles supported on bagasse-derived porous carbon (Co@xPC) for catalytic hydrolytic dehydrogenation of NaBH4. According to the experimental results, Co nanoparticles with uniform particle size and high dispersion are successfully supported on porous carbon to achieve a Co@150PC catalyst. It exhibits particularly high activity of hydrogen generation with the optimal hydrogen production rate of 11086.4 mLH2∙min−1∙gCo−1 and low activation energy (Ea) of 31.25 kJ mol−1. The calculation results based on density functional theory (DFT) indicate that the Co@xPC structure is conducive to the dissociation of [BH4]−, which effectively enhances the hydrolysis efficiency of NaBH4. Moreover, Co@150PC presents an excellent durability, retaining 72.0% of the initial catalyst activity after 15 cycling tests. Moreover, we also explored the degradation mechanism of catalyst performance.
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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".