Role of Fluoride in Accelerating the Reactions of Dialkylstannylene Acetals
Bibliographic record
Abstract
The most common method for achieving the regioselective monoalkylation of diols involves formation of dialkylstannylene acetals as intermediates. Reactions of dialkylstannylene acetals with alkyl halides are slow, but rates are enhanced by addition of fluoride or other nucleophiles. The mechanism of the fluoride-accelerated alkylation of dialkylstannylene acetals was studied at several levels of theory in the gas phase, in N,N-dimethylformamide (DMF) solution, and in DMF solution in the presence of tetramethylammonium ions. The reactive species were adducts involving addition of fluoride to tin. Under the conditions that most closely simulated experiment, reactions from fluoridated monomers and monofluoridated dimers were calculated to have similar activation energies. In the transition states in the rate-determining steps for the two pathways, carbon-oxygen bond formation was between 60 and 75% complete while tin-oxygen bond cleavage was much less advanced, between 6 and 16% complete. A test of Sn-O bond dissociation indicated that the "Sn-O bond cleavage first" mechanism is not a minimum energy pathway.
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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) | 0.001 | 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".