LFER Studies Evaluating Solvent Effects on an α-Chloro- and two β,β,β-Trichloro-Ethyl Chloroformate Esters
Bibliographic record
Abstract
To provide insight and to identify the occurrence of mechanistic changes in relation to variance in solvent-type, the solvent effects on the rates of solvolysis of three substrates, 2,2,2-trichloro-1,1-dimethylethyl chloroformate, 2,2,2-trichloroethyl chloroformate, and 1-chloroethyl chloroformate, are analyzed using linear free energy relationships (LFERs) such as the extended Grunwald-Winstein equation, and a similarity-based LFER model approach that is based on the solvolysis of phenyl chloroformate. At 25.0 °C, in four common solvents, the α-chloroethyl chloroformate was found to react considerably faster than the two β,β,β-trichloro-substituted analogs. This immense rate enhancement can be directly related to the proximity of the electron-withdrawing α-chlorine atom to the carbonyl carbon reaction center. In the thirteen solvents studied, 1-chloroethyl chloroformate was found to strictly follow a carbonyl addition process, with the addition-step being rate-determining. For the two β,β,β-trichloro-substrates, in aqueous mixtures that are very rich in a fluoroalcohol component, there is compelling evidence for the occurrence of side-by-side addition-elimination and ionization mechanisms, with the ionization pathway being predominant. The presence of the two methyl groups on the α-carbon of 2,2,2-trichloro-1,1-dimethylethyl chloroformate has additive steric and stereoelectronic implications, causing its rate of reaction to be significantly slower than that of 2,2,2-trichloroethyl chloroformate.
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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 | 0.001 | 0.001 |
| 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".