Role of adatom defects in the adsorption of polyaromatic hydrocarbons on metallic substrates
Bibliographic record
Abstract
The adsorption of polyaromatic hydrocarbons (PAHs) on metallic substrates has been of interest in the field of optoelectronics due to the possibility of designing complex materials with tunable properties through surface functionalization with organic molecules. Much of the modelling research in this field has focused on perfectly symmetrical (idealized) substrates. Limited research has investigated the effect of substrate irregularities, such as adatoms, on the binding of PAHs onto substrates. Here, we examine how the presence of substrate-bound adatoms affects the binding of coronene and hexahelicene monomers and dimers onto Au(111) and Cu(111) substrates using a density functional theory approach. We found that helicene monomers were more effectively able to adapt to the presence of the adatoms than coronene by coiling around the adatoms. Whereas upon adsorption on an ideal (111) surface, coronene can establish significantly stronger dispersive interactions than helicenes, adatom defects reverse the trend. For helicenes, the degree of flattening near the surface and molecular coiling were strongly influenced by the extent of the adatom defect, as a result of the interplay between the molecule’s drive to maximize overlap with the underlying surface and the enhanced reactivity of lower-coordinated adatoms.
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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 | 0.001 | 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".