<i>Ab initio</i>DFT study of 6-mercapto-hexane SAMs: effect of Au surface defects on the monolayer assembly
Bibliographic record
Abstract
This paper reports a novel ab initio density functional theory (DFT) simulation approach to the study of surface defects in self-assembled monolayers (SAMs) on gold (0 0 1) surfaces. Surfaces defects can cause changes in gold atom spacing and the ligand footprint. DFT calculations of 6-mercapto-hexane on Au(0 0 1) surfaces have been performed as a function of ligand footprints of 18, 20, 22, 24 and 26 Å2, using a slab geometry and periodic boundary conditions with varying gold atom spacing. The simulation results indicate that as the ligand packing density increases, the position of ideal binding sites changes from fcc to bridge sites. The packing density of 24 Å2 corresponds to the highest binding energy and to a 28.85° cant angle. These results are in good agreement with the experiments from ligand-capped gold nanoparticles. The simulation results also prove that the SAM structure of short-chain alkanethiols on gold is dominated by surface interactions rather than ligand interactions. The electron density mapping study confirms this observation, where only the top two layers of gold atoms contributed charge transfer to sulphur atom and no charge transfer was observed from the alkane chain.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".