Enhanced hydrogen adsorption on graphene by manganese and manganese vanadium alloy decoration
Bibliographic record
Abstract
In this work, two kinds of novel manganese decorated (G + Mn) and manganese-vanadium co-decorated (G + MnV) graphene composites are synthesized by in situ wet chemical reduction, and their hydrogen storage properties and microstructures are characterized by Sievert-type adsorption apparatus, BET, SEM, TEM/STEM, EDX and EELS. Compared with pristine graphene, Mn decoration marginally increases the hydrogen adsorption capacity of graphene at room temperature and 4 MPa hydrogen pressure from 0.25 wt% to 0.36 wt%. On the other hand, the co-decoration of Mn and V increases the room temperature hydrogen storage capacity of graphene significantly to 1.81 wt% under 4 MPa hydrogen pressure, which is 1.56 wt% higher than the capacity of pristine graphene. The microstructures and valence states of the decorated Mn and Mn-V nanoparticles are investigated by TEM, EDX and EELS analyses, and strong interactions between the decorated nanoparticles and graphene are observed. Based on the results from structural analyses, potential enhancement mechanisms are suggested in terms of the catalytic effects of nanoparticles on graphene hydrogen adsorption. Given the relatively low cost of Mn and V metals compared to noble metals such as Pd, Pt and Au, these results demonstrate a low cost and effective way to significantly enhance the room temperature hydrogen adsorption properties of graphene for potential hydrogen storage applications.
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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".