Radical Initiated Hydrosilylation on Silicon Nanocrystal Surfaces: An Evaluation of Functional Group Tolerance and Mechanistic Study
Bibliographic record
Abstract
Hydrosilylation is among the most common methods used for modifying silicon surface chemistry. It provides a wide range of surface functionalities and effective passivation of surface sites. Herein, we report a systematic study of radical initiated hydrosilylation of silicon nanocrystal (SiNC) surfaces using two common radical initiators (i.e., 2,2'-azobis(2-methylpropionitrile) and benzoyl peroxide). Compared to other widely applied hydrosilylation methods (e.g., thermal, photochemical, and catalytic), the radical initiator based approach is particle size independent, requires comparatively low reaction temperatures, and yields monolayer surface passivation after short reaction times. The effects of differing functional groups (i.e., alkene, alkyne, carboxylic acid, and ester) on the radical initiated hydrosilylation are also explored. The results indicate functionalization occurs and results in the formation of monolayer passivated surfaces.
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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".