Halide Salts Alleviate TMSOK Inhibition in Suzuki–Miyaura Cross-Couplings
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The Suzuki–Miyaura cross-coupling (SMC) remains one of the most widely used transformations available to chemists. Recently, robust new conditions achieving rapid reactivity under homogeneous aprotic conditions enabled by the use of potassium trimethylsilanolate (TMSOK) as a base were reported. However, the strong inhibitory effect of TMSOK restricts the generality of such conditions. Moreover, the basic nature of TMSOK impedes the use of protic heterocycles as substrates, as these latter anionic species are even more potent catalyst inhibitors. Herein, we report a thorough mechanistic study of these novel SMC conditions. Halide salt additives were found to provide a dramatic rate acceleration and mitigate the inhibitory effect of TMSOK. NMR experiments revealed that this is largely achieved by impacting the unexpected formation of inactive [LnPd(Ar)(μ–OH)] 2, favoring the formation of active LnPd(Ar)(X) instead. These findings enabled an impressive substrate scope even at low catalyst loadings (0.1 mol %). Finally, halide additives were observed to enable the use of protic heterocyclic substrates, which could otherwise completely inhibit reactivity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".