Ligand Effect in Alkali‐Metal‐Catalyzed Transfer Hydrogenation of Ketones
Bibliographic record
Abstract
Abstract This work unveils the reactivity patterns, as well as ligand and additive effect on alkali‐metal‐base‐catalyzed transfer hydrogenation of ketones. Crucially to this reactivity is the presence of a Lewis acid (alkali cation), as opposed to a simple base effect. With aryl ketones, the observed reactivity order is Na + >Li + >K + , whereas for aliphatic substrates it follows the expected Lewis acidity, Li + >Na + >K + . Importantly, the reactivity pattern can be drastically changed by adding ligands and additives. Kinetic, labelling, and competition experiments as well as DFT calculations suggested that the reaction proceeds through a concerted direct hydride‐transfer mechanism, originally suggested by Woodward. The lithium cation was found to be intrinsically more active than heavier congeners, but in the case of aryl ketones a decrease in reaction rate was observed at ≈40 % conversion with lithium cations. Noncovalent‐interaction analysis revealed that this deceleration effect originated from specific noncovalent interactions between the aryl moiety of 1‐phenylethanol and the carbonyl group of acetophenone, which stabilize the product in the coordination sphere of lithium and thus poison the catalyst. The ligand/additive effect is a complicated phenomenon that includes a combination of several factors, such as the decrease of activation energy by ligation (confirmed by distortion/interaction calculations of N , N , N ’, N ’‐tetramethylethylenediamine, TMEDA) and the change in relative stabilization of reagents and substrates in the solution and the coordination sphere of the metal. Finally, we observed that lithium‐base‐catalyzed transfer hydrogenation can be further facilitated by the addition of an inexpensive and benign reagent, LiCl, which likely operates by re‐initiating the reaction on a new lithium center.
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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 | 0.001 | 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) | 0.002 | 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".