Bibliographic record
Abstract
In this study, we used computational quantum chemistry to investigate the cation affinity for a range of nucleophiles to gauge the possibility of using organochalcogens as catalysts for cation transfer (reference data and geometries are provided in the repository https://github.com/armanderch/ca176 ). In general, the calculated gas-phase cation affinities decrease in the order Cl+ > Br+ > I+ > carbon-centered cation, the anionic nucleophiles have significantly larger cation affinities than the neutral ones, sulfides have larger cation affinities than selenides, and solvation lowers the cation affinities and especially for anionic nucleophiles. These observations are consistent with general chemical intuitions. The energies for the resulting condensed-phase cation transfer reactions show that transferring a carbon-centered cation from a neutral source (e.g., Me2CO3) to a chalcogen nucleophile (e.g., Me2S) is thermochemically viable. However, they are associated with large kinetic barriers. Overall, we find that SeMeC6H5 may be a suitable catalyst for transferring a carbon-centered cation from an active source such as MeCO3R or MeSO4R. In this study, we also find that double-hybrid DFT methods, e.g., DSD-PBEP86 to be reasonable for the study of these cation transfer processes.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".