DalPhos on Demand: Facile Ligand Generation Enables New Ligand Discovery and Expedient Catalyst Screening
Bibliographic record
Abstract
DalPhos/Ni-based catalysts have emerged as top performers in C–N and C–O cross-couplings. Expedient means of generating such ligands would facilitate the discovery of effective DalPhos ligand variants as well as accelerate reaction development processes for end users. A protocol for generating structurally varied phosphine- and phosphonite-type DalPhos ligands from a single ligand precursor upon treatment with commercial reagents and without the need for chromatographic purification is disclosed. The formation of DalPhos ligands via this divergent synthetic strategy was exploited in the expedited screening of representative Ni-catalyzed C–N and C–O cross-couplings, leading to the identification of the DalPhos ligand variants (i.e., BnPAd-DalPhos, L4, and OAdPAd-DalPhos, L9 ) that, in turn, were carried forward for reaction scope analysis in challenging cross-couplings of fluoroalkylamines, by use of prepared (DalPhos)Ni(aryl)Cl precatalyst complexes. The reported methodology offers a user-friendly means of generating DalPhos variants in reaction development.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".