Effect of medium on reactivity for alkaline hydrolysis of <i>p</i>-nitrophenyl acetate and <i>S</i>-<i>p</i>-nitrophenyl thioacetate in DMSO–H<sub>2</sub>O mixtures of varying compositions: ground state and transition state contributions
Bibliographic record
Abstract
Second-order rate constants (kN) for reactions of p-nitrophenyl acetate (1) and S-p-nitrophenyl thioacetate (2) with OH– have been measured spectrophotometrically in DMSO–H2O mixtures of varying compositions at 25.0 ± 0.1 °C. The kN value increases from 11.6 to 32 800 M–1 s–1 for the reactions of 1 and from 5.90 to 190 000 M–1 s–1 for those of 2 as the reaction medium changes from H2O to 80 mol % DMSO, indicating that the effect of medium on reactivity is more remarkable for the reactions of 2 than for those of 1. Although 2 possesses a better leaving group than 1, the former is less reactive than the latter by a factor of 2 in H2O. This implies that expulsion of the leaving group is not advanced in the rate-determining transition state, i.e., the reactions of 1 and 2 with OH– proceed through a stepwise mechanism, in which expulsion of the leaving group from the addition intermediate occurs after the rate-determining step. Addition of DMSO to H2O would destabilize OH– through electronic repulsion between the anion and the negative-dipole end in DMSO. However, destabilization of OH– in the ground state is not solely responsible for the remarkably enhanced reactivity upon addition of DMSO to the medium. The effect of medium on reactivity has been dissected into the ground state and transition state contributions through combination of the kinetic data with the transfer enthalpies (ΔΔHtr) from H2O to DMSO–H2O mixtures for OH– ion.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".