Tunable interaction between metal clusters and graphene
Bibliographic record
Abstract
We describe the interaction between small transition metal clusters and graphene using first principles calculations. The coupling is analyzed in terms of different features of the system: binding energy, decomposition into atomic orbitals, the presence of defects on the graphene layer, and both the band and geometrical structures. The binding strength is found to follow the d-band model, which anticipates the binding energies of clusters on graphene layers from the position of the cluster's d-band centers relative to the their highest-occupied and lowest-unoccupied molecular orbital levels. These findings are verified for 6-atom and 13-atom transition metal clusters (Ti, Pd, Pt, and Au) and considering different types of defects. The adhesion of the TM clusters is substantially larger on defective graphene layers than on pristine ones. Buckling of the graphene layer may arise from the presence of defects but it does not necessarily imply strong binding. However, buckling can sometimes offer configurational paths through which the adsorbed cluster is stabilized changing its original shape. Insights into this work offer mechanisms to tailor the electronic properties of the combined nanoparticle-graphene system by changing the size and composition of transition metal clusters.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".