Alcohols as substrates in transition metal-catalyzed arylation, alkylation and related reactions
Bibliographic record
Abstract
Alcohols are abundant and attractive feedstock molecules for organic synthesis. Many methods for their functionalization require them to first be converted into a more activated derivative, while recent years have seen a vast increase in the number of complexity-building transformations that directly harness unprotected alcohols. This review discusses how transition metal catalysis can be used towards this goal. These transformations are broadly classified into three categories. Etherifications, characterized by derivatization of the O–H bond, represent classical reactivity that has been modernized to include mild reactions conditions, diverse reaction partners, and high selectivities. Deoxygenative functionalizations, representing derivatization of the C–O bond, enable the alcohol to act as a leaving group towards the formation of new C–C bonds. Lastly, chain functionalization reactions are described, wherein the alcohol group acts as a mediator in formal C–H functionalization reactions of the alkyl backbone. Each of these three classes of transformation will be discussed in context of intermolecular alkylation, arylation, and related reactions, illustrating how catalysis can enable alcohols to be directly harnessed for organic synthesis.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".