Bisphosphines: A Prominent Ancillary Ligand Class for Application in Nickel-Catalyzed C–N Cross-Coupling
Bibliographic record
Abstract
The Ni-catalyzed C sp2 –N cross-coupling of NH substrates and (hetero)aryl (pseudo)halides for the synthesis of (hetero)anilines is in the midst of a resurgence. Reactivity breakthroughs that have been achieved in this field within the past five years have served to establish Ni catalysis as being competitive with, and in some cases superior to, more well-established Pd- or Cu-based protocols. Whereas the repurposing of useful ancillary ligands from the Pd domain has been the most frequently employed approach in the quest to develop effective Ni-based catalysts for such transformations, considerable progress has been made as of late in the design of ancillary ligands tailored specifically for use with Ni. Bisphosphine ancillary ligands have proven to be well-suited for such an approach, given their modular and facile syntheses; several variants have emerged recently that are particularly effective in enabling a range of otherwise challenging Ni-catalyzed C sp2 –N cross-couplings. This Perspective presents a comprehensive summary of the advancements within the field of Ni-catalyzed C sp2 –N cross-coupling through the application of the bisphosphine ancillary ligand class. It is our intention that the discussion of key ancillary ligand design concepts and mechanistic considerations presented herein will provide a useful platform for researchers to initiate ancillary ligand design efforts for the development of high-performing Ni cross-coupling catalysts.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".