The Baeyer–Villiger reaction: solvent effects on reaction mechanisms
Bibliographic record
Abstract
This study focuses on the Baeyer-Villiger reaction of propanone and performic acid, with formic acid as catalyst. Continuum solvation methods (EIF-PCM and CPCM) and two density functionals (B3LYP and MPWB1K) are used to study solvent effects on two types of reaction mechanisms: concerted non-ionic and stepwise ionic. The ionic mechanism is the one found in most organic chemistry textbooks; it begins with the protonation of the ketone by the acid catalyst, even though this reaction normally takes place in non-polar solvents such as dichloromethane. Our calculations show that the concerted non-ionic pathway, which is the least energetic in non-polar solvents such as dichloromethane, becomes more energetic the more polar the solvent. After investigating a variety of non-ionic and ionic pathways in water, it is found that the addition step seems to be ionic but the migration step, which is rate-determining, is uncatalyzed, non-ionic and fully concerted. These results confirm the experimental findings in solvents of low to medium polarity that the rate constant of the reaction decreases as the solvent polarity increases. Moreover, we find that contrary to what is commonly accepted, in the addition and migration ionic steps the deprotonation of the ionic species occurs in a concerted manner with the other chemical events taking place.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".