Rhodium‐ and Iridium‐Catalyzed Hydroamination of Alkenes
Bibliographic record
Abstract
Abstract The hydroamination of alkenes represents an atom‐economical strategy for the synthesis of nitrogen‐containing molecules from readily available components. In recent years, the application of Group 9 transition metal catalysts in this reaction has enabled significant progress to be made toward addressing several major challenges within the field of metal‐mediated hydroamination. Using Rh‐ and Ir‐based catalysts for the intermolecular hydroamination reaction, advances have been made in the regioselective addition of amines to olefins in an anti‐Markovnikov fashion producing industrially relevant linear amine products, as well as the concise synthesis of chiral amines by asymmetric hydroamination. The intramolecular addition of a variety of amine groups to pendant alkenes has also been studied in the context of developing expedient routes to nitrogen‐containing heterocycles; using simple Rh‐ and Ir‐based catalysts, a wide range of substrates including those that contain functional groups that are poised for further synthetic elaboration are readily cyclized. Extension of these catalyst systems to include the asymmetric synthesis of a variety of functionalized 1‐methylpyrrolidine compounds has recently been achieved. To complement these catalytic investigations, thorough stoichiometric and kinetic studies have unveiled diverse mechanistic pathways that originate from either initial amine or olefin activation. The understanding gained through these mechanistic investigations provides the framework for the design of increasingly effective alkene hydroamination catalysts.
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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 | 0.000 |
| 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 | 0.001 |
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".