UV-Initiated Hydrosilylation on Hydrogen-Terminated Silicon (111): Rate Coefficient Increase of Two Orders of Magnitude in the Presence of Aromatic Electron Acceptors
Bibliographic record
Abstract
UV-initiated (254 nm) hydrosilylation of hexadecene on Si(111)-H has been studied in the presence of various aliphatic and aromatic molecules (additives). Many of these additives cause an enhancement in the pseudo-first-order rate coefficient (k(obs)) of hydrosilylation, some up to 200× faster than observed in neat hexadecene. It is proposed that these additives capture the photoejected electron from the surface, thereby increasing the probability of reaction of the alkene with the surface hole (h(+)), leading to Si-C bond formation. While the ability of these additives to increase k(obs) is related to their reduction potential, aromatic additives are particularly efficient; we suspect this is due to the relatively strong physisorption of the aromatic molecules leading to a favorable geometry for electron transfer. The presence of these additives permits the use of a much lower intensity of UV light (~30 μW/cm(2)), reducing the probability of photodegradation of the monolayer, and maximum coverage can be reached within minutes.
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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 | 0.000 | 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".