Evaluating 1,1′-Bis(phosphino)ferrocene Ancillary Ligand Variants in the Nickel-Catalyzed C–N Cross-Coupling of (Hetero)aryl Chlorides
Bibliographic record
Abstract
Abstract Previous reports in the literature have established the utility of 1,1?-bis(diphenylphosphino)ferrocene (DPPF, LPh) in the nickel-catalyzed cross-coupling of (hetero)aryl electrophiles with primary or secondary amines. In an effort to evaluate the effect of varying the PR2-donor groups on catalytic performance in such transformations, a series of 10 structurally varied 1,1?-bis(bis(alkyl/aryl)phosphino)ferrocene ancillary ligands (LX) were systematically examined in selected competitive test cross-couplings of (hetero)aryl halides with furfurylamine, morpholine, and indole employing Ni(COD)2/LX catalyst mixtures. In addition to the excellent performance observed for the parent ligand LPh in a number of the test transformations explored, selected dialkylphosphino (e.g., DiPPF, LiPr) and meta-disubstituted diarylphosphino variants of LPh also proved highly effective. In particular, the electron-deficient ligand variant LCF3 featuring 3,5-bis(trifluoromethyl)phenyl groups on phosphorus was found to exhibit superior catalytic performance relative to LPh in most of the test transformations involving the N-arylation of indole. Our efforts to prepare Ni(II) precatalysts of the type (LX)Ni(o-tolyl)Cl, in analogy with known (LPh)Ni(o-tolyl)Cl, by employing several literature methods met with mixed results. Whereas (LiPr)Ni(o-tolyl)Cl was prepared straightforwardly and was crystallographically characterized, the use of LCF3 or ligands featuring tert-butyl (LtBu), o-tolyl (Lo-tol), or 4-methoxy-3,5-dimethylphenyl (LOMe) groups on phosphorus under similar conditions resulted in poor conversion to product and/or the formation of poorly soluble materials, highlighting the limitations of this commonly used precatalyst design.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".