A van der Waals density functional theory comparison of metal decorated graphene systems for hydrogen adsorption
Bibliographic record
Abstract
Previous Density Functional Theory (DFT) studies on metal decorated graphene generally use local density approximation (LDA) or generalized gradient approximation (GGA) functionals which can cause inaccuracies in hydrogen binding energies as they neglect van der Waals (vdW) interactions and are difficult to compare due to their widely varying simulation parameters. We investigated the hydrogen binding ability of several metals with a consistent set of simulations using the GGA functional and incorporated vdW forces through the vdW-DF2 functional. Metal adatom anchoring on graphene and hydrogen adsorption ability for both single and double sided decoration were studied for eight metals (Al, Li, Na, Ca, Cu, Ni, Pd, and Pt). It was found that the vdW correction can have a significant impact on both metal and hydrogen binding energies. The vdW-DF2 functional led to stronger metal adatom and hydrogen binding for light metals in comparison to GGA results, while heavier transition metals displayed the opposite behaviour but still produced stronger hydrogen binding energies than light metals. Nickel was found to be the best balance between hydrogen binding ability for reversible storage and low weight. The effects on hydrogen binding energy and maximum achievable hydrogen gravimetric density were analyzed for Ni-graphene systems with varying metal coverage. Lower metal coverage was found to improve hydrogen binding but decrease hydrogen gravimetric density. The highest achieved Ni-graphene system gravimetric density was 6.12 wt. %.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".