Carbonyls as Latent Alkyl Carbanions for Conjugate Additions
Bibliographic record
Abstract
Abstract Conjugate addition of carbon nucleophiles to electron‐deficient olefins is one of the most powerful methods for forming carbon–carbon bonds. Despite great achievements in controlling the selectivity, variation of the carbon nucleophiles remains largely underexplored, with this approach relying mostly on organometallic reagents. Herein, we report that naturally abundant carbonyls can act as latent carbon nucleophiles for conjugate additions through a ruthenium‐catalyzed process, with water and nitrogen as innocuous byproducts. The key to our success is homogeneous ruthenium(II) catalysis, combined with phosphines as spectator ligands and hydrazine as the reducing agent. This chemistry allows the incorporation of highly functionalized alkyl fragments into a vast array of electron‐deficient olefins under mild reaction conditions in a reaction complementary to the classical organometallic‐reagent‐based conjugate additions mediated or catalyzed by “soft” transition metals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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 teacher head, 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".