Hydroaminoalkylation: A Tool of Choice for the Catalytic Addition of Amines to Alkenes in Small Molecules and Materials
Bibliographic record
Abstract
ConspectusHydroaminoalkylation, the catalytic addition of amines to alkenes, has evolved as a powerful tool in modern synthetic chemistry, offering an atom-economic and green approach to the construction of C-C bonds. This reaction enables the direct amine functionalization of alkenes and alkynes without the need for protecting groups, directing groups, or prefunctionalization, thereby eliminating stoichiometric waste and minimizing synthetic steps. Over the past two decades, significant advances in catalyst development and mechanistic understanding have expanded the scope of hydroaminoalkylation, allowing for control over regio-, diastereo-, and enantioselectivity. In this Account, we provide a comprehensive overview of our contributions to this field, from fundamental mechanistic insights into early transition metal catalysis to the rational design of hydroaminoalkylation catalysts for small molecule and polymer functionalization. We discuss key breakthroughs, including the development of N,O-chelated early transition metal catalysts, and the use of hydroaminoalkylation in synthesis by providing direct access to valuable α- and β-alkylated amines that serve as key building blocks in pharmaceuticals, agrochemicals, and fine chemicals. The practical applications of hydroaminoalkylation extend beyond small molecule synthesis to the field of polymer chemistry, where it enables both pre- and postpolymerization amination strategies. These advances have unlocked new applications in materials science, particularly in the design of self-healing polymers, adhesives, antibacterial coatings, and polymeric binders for energy storage applications. Additionally, we demonstrate the compatibility of hydroaminoalkylation with other catalytic methods in both small molecule synthesis and polymer chemistry. Finally, we highlight remaining challenges and future opportunities, such as the development of earth-abundant metal catalysts, enantioselective hydroaminoalkylation strategies, and advanced polymer applications. By bridging the gap between small molecule synthesis and polymer chemistry, hydroaminoalkylation shows much promise as a transformative strategy for modern catalysis.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".