Cobalt Catalyzed Enantio- and Regioselective Hydrosilation
Bibliographic record
Abstract
Enantioselective and regioselective cobalt-catalyzed hydrosilation has been scarcely reported. Although this transformation catalyzed with other transition metals such as iron or copper is well known, cobalt being an abundant and cheap transition metal makes having its catalyst as an option to expand the "toolbox" highly desirable. In addition, enantioselective and regioselective silyl products can serve as important building blocks for creating chiral synthetic products. For example, chiral silanes can be converted into chiral alcohols via Fleming–Tamao oxidation. To conduct this experiment, 2-vinylnaphlene was chosen as the model substrate and various conditions for the hydrosilation of diphenylsilane were investigated. A variety of cobalt catalysts was screened and uniquely, cobalt chloride triphenylphosphine was used offering a cobalt(I) complex. Then the optimal solvent for this reaction was found to be dioxane. With this information, we screened various chiral ligands. The conversion and the regioselectivity of the reaction were determined with proton 1H-NMR. The enantioselectivity was determined through an HPLC assay.In the study, we were able to perform hydrosilation targeting the Markovnikov product. enantioselectivity was achieved with the optimal ligands, albeit with mild regioselectivity. The implications of this finding show promise for using cobalt for an enantioselective and regioselective hydrosilation reaction. Further steps could be taken toward modifying the ligand to enhance conversion, regioselectivity, and enantioselectivity. In addition, various other substrates and silanes could be explored to improve the scope of this study.
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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 | 0.001 |
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".