Probing the effect of donor-fragment substitution in Mor-DalPhos on palladium-catalyzed C–N and C–C cross-coupling reactivity
Bibliographic record
Abstract
The competitive catalytic screening of 18 known and newly prepared Mor-DalPhos ligand variants in the palladium-catalyzed cross-coupling of chlorobenzene with aniline, octylamine, morpholine, indole, ammonia, or acetone is presented, including ligands derived from the new secondary phosphine HP(Me2Ad)2 (Me2Ad = 3,5-dimethyladamantyl). Although triarylphosphine ancillary ligand variants performed poorly in these test reactions, ligands featuring either PAd2 or P(Me2Ad)2 donors (Ad = 1-adamantyl) gave rise to superior catalytic performance. Multiple Mor-DalPhos variants proved effective in cross-couplings involving aniline, octylamine, or morpholine; conversely, only a smaller subset of ligands proved useful in related cross-couplings of indole, ammonia, or acetone. In the case of the N-arylation of indole, a Mor-DalPhos ligand variant featuring ortho-disposed PAd2 and dimethylmorpholino donor fragments (L13) proved superior to all other ligands surveyed, including the parent ligand Mor-DalPhos (L5). Conversely, L5 was found to be superior to all other ligands in the palladium-catalyzed monoarylation of ammonia. Ligand L6 (i.e., the P(Me2Ad)2 variant of L5) proved superior to all other ligands in the monoarylation of acetone and, with the exception of indole N-arylation, was the most broadly useful of the Mor-DalPhos ligands surveyed herein.
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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".