Cationic Ruthenium-Catalyzed Bis-Homo-Diels-Alder Cycloaddition
Bibliographic record
Abstract
OBJECTIVE: The ruthenium-catalyzed Bis-Homo-Diels-Alder cycloaddition between 1,5- cyclooctadiene and alkynes was explored, and the use of commercially available cationic catalysts was investigated. It was noted that [CpRu(CH3CN)3]PF6 was effective at catalyzing this cycloaddition and yields of the desired tricyclo[4.2.2.02,5]dec-7-ene adduct ranging from 13 to 83% were achieved using this cationic catalyst. Several cycloadducts that were previously unobtainable with the use of the neutral (Cp*RuCl(COD) catalysts were also successfully made using [CpRu(CH3CN)3]PF6 albeit in low yields. METHODS: Commercially available, and previously synthesized alkynes were combined with 1,5-cyclooctadiene and treated with a ruthenium catalyst within a glovebox. The reaction mixture was stirred for 72h at temperatures ranging from 25 to 70oC. The desired cycloadduct was then isolated using flash column chromatography and analyzed and characterized using NMR, IR and MS. RESULTS: Several previously unattainable adducts were synthesized using the cationic [CpRu(CH3CN)3]PF6. When this catalyst was compared to the neutral Cp*RuCl(COD) greater yields were observed. CONCLUSION: The present study describes an improved method for the formation of the tricyclo[4.2.2.02,5]dec-7- ene framework using a commercially available cationic ruthenium catalyst. It was noted that the use of [CpRu(CH3CN)3]PF6 led to improved yields when compared to Cp*RuCl(COD).
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".