Examining the Impact of Heteroaryl Variants of PAd-DalPhos on Nickel-Catalyzed C(<i>sp</i><sup>2</sup>)-N Cross-Couplings
Bibliographic record
Abstract
We report herein on the synthesis of new heteroaryl analogues of PAd-DalPhos and related bis(di( o -tolyl)phosphino) ancillary ligand variants based on pyridine or thiophene backbone structures, and their application in nickel-catalyzed C( sp 2 )-N cross-couplings under challenging reaction conditions. The 3,4-disubstituted thiophene-based ancillary ligand ThioPAd-DalPhos ( L8 ) was observed to be particularly effective in the nickel-catalyzed C( sp 2 )-N cross-coupling of primary alkylamines, and the derived precatalyst ( L8 )NiCl( o -tolyl) ( C2 ) was found to offer improved performance versus the related PAd-DalPhos-derived precatalyst C1 in such transformations. In using C2, cross-couplings of various primary alkylamines and (hetero)aryl-X electrophiles (X = Cl, Br, OTs) proceeded under unprecedentedly mild reaction conditions (0.25–0.50 mol % Ni), including examples conducted at room temperature. Also reported herein are the results of our combined experimental/DFT computational study directed toward gaining insight regarding the improved catalytic performance of C2 versus C1 .
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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".