An unusual chiral-at-metal mechanism for BINOL-metal asymmetric catalysis
Bibliographic record
Abstract
Chiral binaphthols (BINOL)-metal combinations serve as powerful catalysts in asymmetric synthesis. Their chiral induction mode, however, typically relies on multifarious non-covalent interactions between the substrate and the BINOL ligand. In this work, we demonstrate that the chiral-at-metal stereoinduction mode could serve as an alternative mechanism for BINOL-metal catalysis, based on mechanistic studies of BINOL-aluminum-catalyzed asymmetric hydroboration of heteroaryl ketones. Theoretical calculations reveal that an octahedral stereogenic-at-metal aluminum alkoxide species is the most stable species within the reaction system, and also is the catalytic relevant intermediate, promoting the stereo-determining hydroboration reaction through a ligand-assisted hydride transfer mechanism rather than the conventional hydroalumination mechanism. These computations reproduce the experimental selectivities and also rationalize the stereoinduction mechanism, which arises from the aluminum-centered chirality induced by chiral BINOL ligands during diastereoselective assembly. The reliability of the proposed mechanism could be verified by the single-crystal X-ray diffraction characterization of the octahedral aluminum alkoxide complex. Additional NMR and Electronic Circular Dichroism (ECD) experiments elucidated the behavior of the hexacoordinated aluminum alkoxide in the solution phase. We anticipate that these findings will extend the applicability of BINOL-metal catalysis to a broader range of reactions. Chiral binaphthols have been staples as ligands in homogeneous asymmetric catalysis for decades, inducing chirality in transformations of the substrate by noncovalent interactions between the catalyst microenvironment and ligand. Here, the authors demonstrate that the chiral-at-metal stereoinduction mode is also a viable method for stereoinduction when using this ligand class.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".