Theoretical Study of Dispersion Binding of Hydrocarbon Molecules to Hydrogen-Terminated Silicon(100)-2×1
Bibliographic record
Abstract
Noncovalent interactions between organic molecules and hydrogen-terminated silicon(100)-2×1 influence surface chemistry and diffusion. To develop our understanding of these interactions, we studied the dispersion binding of two model hydrocarbons, methane and benzene, on hydrogen-terminated silicon(100)-2×1 using a density-functional theory (DFT) technique incorporating dispersion corrections. The corrections were implemented using previously developed carbon-centered potentials (DiLabio, G. A. Chem. Phys. Lett . 2008, 455, 348−353.) and newly developed silicon-centered potentials, designed to correct the well-known erroneous long-range behavior of DFT methods. Our calculations predict that the preferred position for methane binding to the surface occurs over the area between two dimer rows (the “gulley”) and between two sets of dimers (binding energy (BE) = 2.2 kcal/mol). When the methane is positioned over a dimer row, BE = 1.9 kcal/mol. Diffusion of methane along the dimer row direction was calculated to be only marginally more facile than diffusion across rows. In the case of benzene, the preferred binding position is over the dimer row with the benzene roughly parallel to the surface and centered over one surface hydrogen (BE = 5.6 kcal/mol). The complex in which the benzene is over the gulley is less strongly bound (BE = 5.3 kcal/mol). Additional higher energy structures were also found. A detailed potential energy surface map reveals that diffusion of benzene in the row direction is more facile than diffusion across dimer rows. The calculated, room-temperature rate constant associated with diffusion along the row is calculated to be 1.7 times larger than that for diffusion in the perpendicular-to-row direction. Our findings show that surface anisotropy and molecule shape contribute to a directional preference for diffusion.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".