Catalyst and Medium Control over Rebound Pathways in Manganese-Catalyzed Methylenic C-H Bond Oxidation
Bibliographic record
Abstract
The C(sp3)-H bond oxygenation of a variety of cyclopropane containing hydrocarbons with hydrogen peroxide catalyzed by manganese complexes containing aminopyridine tetradentate ligands were carried out. Oxidations were performed in 1,1,1,3,3,3-hexafluoro-2-propanol (HFIP) and 2,2,2-trifluoroethanol (TFE) using different manganese catalysts and carboxylic acid co-ligands, where steric and electronic properties were systematically modified. Functionalization selectively occurs at the most activated C-H bonds that are - to cyclopropane, providing access to carboxylate- or 2,2,2-trifluoroethanolate transfer products, with no competition from the generally dominant hydroxylation reaction. The formation of mixtures of unrearranged and rearranged esters (oxidation in HFIP in the presence of a carboxylic acid) and ethers (oxidation in TFE) with full control over diastereoselectivity was observed, confirming the involvement of delocalized cationic intermediates in these transformations. Despite such a complex mechanistic scenario, by fine tuning of catalyst and carboxylic acid sterics and electronics and leveraging on the relative contribution of cationic pathways to the reaction mechanism, control over product chemoselectivity could be systematically achieved. Taken together, the results reported herein provide powerful catalytic tools to rationally manipulate ligand transfer pathways in C-H oxidations of cyclopropane containing hydrocarbons, delivering novel products in good yields and, in some cases, outstanding selectivities, expanding the available toolbox for the development of synthetically useful C-H functionalization procedures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
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 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".