Trends of elemental adsorption on graphene
Bibliographic record
Abstract
Adding impurities or doping through adsorption is an effective way to tailor the properties of graphene-based materials. The capability of making predictions with regard to the trends of elemental adsorption on graphene is crucial to a better understanding of the more complex adsorption cases. It also provides useful guidelines for fabricating 2D graphene materials with novel properties. In this review, we show trends of elemental adsorption on graphene with elements of the periodic table, based on previous studies and supplemented with our recent calculations. We also discuss the effects of atomic ratios on some properties of this element-adsorbed graphene system. Trends of properties studied include binding energy, most stable site, adatom height, migration energy, Fermi energy shift, graphene distortion, magnetization, charge transfer, and electronic energy band gap at Fermi energy. Certainly, there is ample scope to investigate the electronic structures of elemental adsorption on graphene based on period and group of the periodic table, and atomic ratio.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".