Selective and Nonselective Aza-Michael Additions Catalyzed by a Chiral Zirconium Bis-Diketiminate Complex
Bibliographic record
Abstract
Reaction of the chiral bis-diketiminate complex rac - or ( R, R )-C 6 H 10 ( nacnac Xyl ) 2 ZrCl 2 with AgOTf yielded the corresponding bis-triflate complex. The complex geometry changes from distorted octahedral in the dichloride complex to a pseudotetrahedral coordination involving π coordination of the diketiminate ligands. The bis-triflate complex is highly active for aza-Michael additions with turnover frequencies of 20000/h for the addition of morpholine to acrylonitrile and 1000/h for the addition of morpholine to methacrylonitrile. The enantioselectivities of the latter reaction in various solvents were low, never surpassing 19% ee. The reaction is first-order in olefin concentration and second order in amine concentration, which is explained by its participation as a base in the reaction mechanism. The presence of catalytic amounts of triethylamine slightly increases the observed rate constants and reduces the reaction order in amine to first order. Other activated alkenes such as methacrylonitrile, crotonitrile, methyl acrylate, and cyclohexenone can be employed, but no reactivity is observed toward styrene or vinyl ethers. Primary amines, secondary amines, and anilines can be employed as nucleophiles with activities correlating with their nucleophilicity, but the catalyst is unstable in the presence of alcohols.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".