The role of a precursor state in thiophene chemisorption on Si(111)–7×7
Bibliographic record
Abstract
Abstract The adsorption of thiophene on Si(111)–7×7 has been studied with scanning tunneling microscopy (STM) and kinetic Monte Carlo (kMC) modelling. Previous experimental studies of this system clearly demonstrated that thiophene prefers to chemisorb on the faulted half of the 7×7 unit cell. The STM studies reported here concur with this and provide further information about thiophene site preference as a function of coverage. Additionally, an ab initio theoretical investigation of this system demonstrated that the occupancy of available adsorption sites could not be explained using equilibrium binding energies, as these were calculated to be the same for all experimentally identified adsorption sites (≈1.0 eV). To investigate the possibility that site selection is kinetically controlled, a kMC model was developed. This model places the molecule in a mobile precursor state, allowing the molecule to traverse the surface before chemisorbing. The kMC model was found to reproduce the STM data, providing compelling evidence that site occupancy in this system is indeed kinetically controlled at room temperature. Activation energy differences, for each the four unique chemisorption geometries, could be extracted from a fit of the kMC model predictions to the experimental data (© 2010 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
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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) | 0.000 | 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 | 0.001 | 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".