Hydrogen Storage Performance in Pd/Graphene Nanocomposites
Bibliographic record
Abstract
We have developed a Pd-graphene nanocomposite for hydrogen storage. The spherically shaped Pd nanoparticles of 5-45 nm in size are homogeneously distributed over the graphene matrix. This new hydrogen storage system has favorable features like desirable hydrogen storage capacity, ambient conditions of hydrogen uptake, and low temperature of hydrogen release. At a hydrogen charging pressure of 50 bar, the material could yield a gravimetric density of 6.7 wt % in the 1% Pd/graphene nanocomposite. As we increased the applied pressure to 60 bar, the hydrogen uptake capacity reached 8.67 wt % in the 1% Pd/graphene nanocomposite and 7.16 wt % in the 5% Pd/graphene nanocomposite. This system allows storage of hydrogen in amounts that exceed the capacity of the gravimetric target announced by the U.S. Department of Energy (DOE).
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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".