Toward a Mechanistic Understanding of Exciton-Mediated Hydrosilylation on Nanocrystalline Silicon
Bibliographic record
Abstract
White-light initiated hydrosilylation of nanocrystalline porous silicon was found to be far more efficient (in terms of both kinetics and yield) in the presence of electron-accepting molecules with suitably high reduction potentials, particularly halocarbons. It is known that absorption of visible light by nanocrystalline silicon results in the formation of excitons (electron/hole pairs) and that this exciton can be harnessed to drive a hydrosilylation reaction with an alkene; the Si-C bond forms as a result of attack of the π-electrons of the alkene on the positively charged holes. In order to better understand the white-light initiated mechanism through which this reaction takes place, and to compare with UV-mediated photoemission on Si(111)-H, a series of electron acceptors were screened for their effect on surface alkene hydrosilylation. A very strong correlation between reduction potentials (E(red)) of the oxidant and reaction efficiency was observed, with a minimum "turn-on" E(red) required for an increase to take place. The oxidant appears to accept, or remove, the electron from the nanocrystallite-bound exciton, favoring attack by the alkene on the positively charged Si nanocrystallite, leading to Si-C bond formation. Radical reactions were discounted for a number of reasons, including lack of effect of radical traps, no apparent Si-Cl bond formation, lack of oxidation of the surfaces, and others. Unlike with other oxidants such as nitro-aromatics, halocarbons do not cause additional surface reactions and promote very clean, fast, and selective hydrosilylation chemistry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".