Gas-phase reaction of ClO<sup>−</sup> with CH<sub><i>n</i></sub>Cl<sub>4-</sub><sub><i>n</i></sub> (<i>n</i> = 0, 1, 2, 3) and CX<sub>3</sub>H (X = F, Cl and Br): Substituent effect from a comparative study
Bibliographic record
Abstract
Substituent effects on reactivity are studied using the hybrid B3LYP and BHandHLYP methods of density functional theory with the aug-cc-pVDZ basis set. The chosen testing models includes two very representative reactions in chemical research, the bimolecular nucleophilic substitution (S N 2) reaction and the deprotonation reaction, in which the former is represented by ClO − + CH n Cl 4- n (n = 0, 1, 2, 3), and the latter is based on reactions of ClO − with CX 3 H (X = F, Cl, and Br). Our theoretical findings suggest that a heavier substituent X in substrate results in a higher activation energy, a slower S N 2 reaction, but a faster deprotonation reaction. Those are well confirmed by some presented results from bond orders, second-order perturbative energy E (2) , and activation strain model analysis. Moreover, we have further explored the reactivity difference derived from substituent effects in term of the relationships of reactive barrier with the charges transferred and the leaving-bond distance in TSs, respectively, especially the TSs in S N 2 reactions. Again, the rate constants at 298–1000 K are also evaluated for the S N 2 reactions presented through the transition state theory.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Computational chemistry study of substituent effects in gas-phase reactions.
The study examines chemical reaction mechanisms rather than research methods or institutions.
Computational chemistry of gas-phase reactions, domain science.
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) | 0.004 | 0.001 |
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".