Interrogating Explicit Solvent Effects on the Mechanism and Site-Selectivity of Aryl Halide Oxidative Addition to L2Pd(0)
Bibliographic record
Abstract
We report a study of solvent effects on the rate, selectivity, and mechanism of (hetero)aryl (pseudo)halide oxidative addition to Pd(PCy3)2 as an exemplar of L2Pd(0) species. First, 2-chloro-3-aminopyridine is observed to undergo faster oxidative addition in toluene compared to more polar solvents, which is not consistent with the trend we observe with many other 2-halopyridines. We attribute this to solvent basicity hydrogen-bonding between solvent and substrate. Greater hydrogen-bond donation from the substrate leads to a more electron-rich aromatic system, and therefore slower oxidative addition. We demonstrate how this affects rate and site-selectivity for hydrogen-bond donating substrates. Second, electron-deficient multihalogenated pyridines exhibit improved site-selectivity in polar solvents, which we attribute to different C–X sites undergoing oxidative addition by two different mechanisms. The C–X site that favours the more polar nucleophilic displacement transition state is preferred over the site that favours a less-polar 3-centered transition state. Finally, (hetero)aryl triflates consistently undergo faster oxidative addition in more polar solvents, which we attribute to highly polar nucleophilic displacement transition states. This leads to improved site-selectivity for C–OTf oxidative addition, even in the presence of highly reactive 2-pyridyl halides.
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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 | 0.000 |
| 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) | 0.002 | 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".