Rhodium-catalyzed hydroborations of allylamine and allylimines<sup>1</sup>
Bibliographic record
Abstract
The in situ rhodium-catalyzed addition of catecholborane (HBcat, cat = 1,2-O2C6H4) and pinacolborane (HBpin, pin = 1,2-O2C2Me4) to allylamine, allylimine, 2- and 4-vinylpyridines, and a thienyl imine has been examined using multinuclear NMR spectroscopy. Although reactions of allylamine (H2NCH2CH=CH2) and HBcat gave complex product distributions arising from competing dehydrogenative borylation pathways, addition of HBpin to allylamine using a rhodium catalyst afforded only products arising from hydroboration (RN(Bpin)CH2CH2CH2Bpin, where R = H, Bpin) and hydrogenation (RN(Bpin)CH2CH2CH3). Hydroboration of allylimines (RHC=NCH2CH=CH2, R = Ar) with HBcat occurs initially at the more reactive imine functionality to give unsaturated borylamines (RCH2N(Bcat)CH2CH=CH2). Further reaction with HBcat gives varying amounts of hydroboration products RCH2N(Bcat)CH2CH2CH2Bcat and RCH2N(Bcat)CH2CH(Bcat)CH3 as well as the diboration product RCH2N(Bcat)CH2CH2CH(Bcat)2, depending on the choice of catalyst. Reactions with related unsaturated pyridine derivatives are complicated by extensive degradation, which can be avoided by coordination of the pyridine nitrogen to a Lewis acid. The first examples of metal-catalyzed hydroboration of imines using HBpin are also reported.Key words: catalysis, hydroboration, boronate esters, dehydrogenative borylation, allylimines.
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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".