How Do Nucleophiles Accelerate the Reactions of Dialkylstannylene Acetals? The Effects of Adding Fluoride to Dialkoxydi-<i>n</i>-butylstannanes
Bibliographic record
Abstract
Dialkylstannylene acetals are organotin intermediates widely used to facilitate regioselective monofunctionalization of diols or polyols by electrophiles. Alkylation is both the slowest and the most useful reaction of these intermediates, and this reaction is markedly accelerated by the addition of nucleophiles to the reaction media, usually cesium fluoride in dimethylformamide (DMF) or tetrabutylammonium iodide or bromide in toluene. The regioselectivity may be influenced by aggregation of the dialkylstannylene acetals into dimers and higher oligomers, and by the addition of these nucleophiles. The stabilities and the geometries of the species potentially involved in these processes were examined by using theoretical chemistry methods with di-n-butyldialkoxytin derivatives as examples and fluoride as the nucleophile. Geometry optimizations were performed at B3LYP/6-31G(d,p) level, and single point energies obtained at the MP2/6-311G(2d,p) level with diffuse functions added for fluorine. The LANL2DZdp basis set with diffuse and polarization functions and its effective core potential were used to describe tin. The addition of fluoride to monomeric di-n-butyldialkoxytin derivatives to give fluoridated monomers is predicted to be strongly exothermic, by 187 to 209 kJ/mol, depending on the alkoxyl group. The fluoridated monomers are calculated to react with monomers exothermically to give monofluoridated dimers, except for the di-t-butoxy derivative. Dimer formation on average is about 20 kJ mol(-1) more exothermic than for the nonfluoridated monomers alone. Monofluoridated monomers strongly prefer to exist as monomers because the difluoridated dimers are estimated to be 209 to 278 kJ mol(-1) less stable at 298 K.
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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) | 0.002 | 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".