Substituent effects and the role of negative hyperconjugation in siloxycarbene rearrangements
Bibliographic record
Abstract
Substituent effects and the role of negative hyperconjugation in 1,2-silyl migration and decarbonylation of methoxy(substituted-siloxy)carbenes have been investigated using quantum chemical calculations and natural bond orbital analysis. It has been found that sigma-electron-withdrawing substituents generally lower the barriers for 1,2-silyl migration and decarbonylation, consistent with symmetry-forbidden concerted rearrangements involving intramolecular front-side nucleophilic attack by the carbene lone pair at silicon and by the methoxy oxygen at silicon, respectively. However, while good linear Hammett correlations are obtained for 1,2-silyl migration, those obtained for decarbonylation are poor. In addition, there appears to be a relationship between the extent of pertinent hyperconjugative interactions in the siloxycarbene conformers and the ease of intramolecular reactivity. As a matter of fact, the finding that 1,2-silyl migration is more favorable than decarbonylation seems to be primarily related to stronger negative hyperconjugation between the carbene lone pair and the O-Si antibonding orbital, compared to that between the methoxy oxygen n(sigma) lone pair and the O-Si antibonding orbital. Moreover, the activation enthalpies for 1,2-silyl migration decrease linearly with stronger negative hyperconjugation, although no such correlation could be established for decarbonylation.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".