Comparing B–H Bond Activation in Ni<sup>II</sup>X(NNN)-Catalyzed Nitrile Dihydroboration (X = Anionic N-, C-, O-, S-, or P-donor)
Bibliographic record
Abstract
One of the key steps in many metal complex-catalyzed hydroboration reactions is B–H bond activation, which results in metal hydride formation. Anionic ligands that include multiple lone pairs of electrons, in cooperation with a metal center, have notable potential in redox-neutral B–H bond activation through metal–ligand cooperation. Herein, using an easily prepared N pyridine N imine N pyrrolide ligand (L 2 ) −, a series of divalent Ni II X(NNN) complexes were synthesized, with X = bromide ( 2 ), phenoxide ( 3 ), thiophenoxide ( 4 ), 2,5-dimethylpyrrolide ( 5 ), diphenylphosphide ( 6 ), and phenyl ( 7 ). The complexes were characterized using 1 H and 13 C NMR spectroscopy, mass spectrometry, and X-ray crystallography and employed as precatalysts for nitrile dihydroboration. Superior activity of the phenoxy derivative ( 3 ) [vs thiophenoxy ( 4 ) or phenyl ( 7 )] suggests that B–H bond activation occurs at the Ni–X (vs ligand Ni–N pyrrolide ) bond. Furthermore, stoichiometric treatment of 2–7 with a nitrile showed no reaction, whereas stoichiometric reactions of 2–7 with pinacolborane (HBpin) gave the same Ni–H complex for 2, 3, and 5 . Considering that only 2, 3, and 5 successfully catalyzed nitrile dihydroboration, we suggest that the catalytic cycle involves a conventional inner sphere pathway initiated by substrate insertion into Ni–H.
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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) | 0.001 | 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".