Decoding the Mechanism of Intramolecular Cu-Directed Hydroxylation of sp<sup>3</sup> C–H Bonds
Bibliographic record
Abstract
The use of copper in directed C–H oxidation has been relatively underexplored. In a seminal example, Schönecker showed that copper and O 2 promoted the hydroxylation of steroid-containing ligands. Recently, Baran ( J. Am. Chem. Soc. 2015, 137, 13776) improved the reaction conditions to oxidize similar substrates with excellent yields. In both reports, the involvement of Cu 2 O 2 intermediates was suggested. In this collaborative article, we studied the hydroxylation mechanism in great detail, resulting in the overhaul of the previously accepted mechanism and the development of improved reaction conditions. Extensive experimental evidence (spectroscopic characterization, kinetic analysis, intermolecular reactivity, and radical trap experiments) is provided to support each of the elementary steps proposed and the hypothesis that a key mononuclear LCu II (OOR) intermediate undergoes homolytic O–O cleavage to generate reactive RO • species, which are responsible for key C–H hydroxylation within the solvent cage. These key findings allowed the oxidation protocol to be reformulated, leading to improvements of the reaction cost, practicability, and isolated yield.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".