Remarkably Efficient Microwave‐Assisted Cross‐Metathesis of Lipids under Solvent‐Free Conditions
Bibliographic record
Abstract
Abstract Catalytic transformation of renewable feedstocks into fine chemicals is in high demands and olefin metathesis is a sophisticated tool for biomass conversion. Nevertheless, the large‐scale viability of such processes depends on the conversion efficiency, energy efficiency, catalytic activity, selective conversion into desired products, and environmental footprint of the process. Therefore, conversions of renewables by using simple, swift, and efficient methods are desirable. A microwave‐assisted ethenolysis and alkenolysis (using 1,5‐hexadiene) of canola oil and methyl esters derived from canola oil (COME) and waste/recycled cooking oil (WOME) was carried out by using ruthenium‐based catalytic systems. A systematic study using 1st and 2nd generation Grubbs and Hoveyda–Grubbs catalysts was carried out. Among all ruthenium catalysts, 2nd generation Hoveyda–Grubbs catalyst was found to be highly active in the range of 0.002–0.1 mol % loading. The conversions proved to be rapid providing unprecedented turnover frequencies (TOFs). High TOFs were achieved for ethenolysis of COME (21 450 min−1), direct ethenolysis of canola oil (19 110 min−1), for WOME (15 840 min−1) and for cross‐metathesis of 1,5‐hexadiene with COME (10 920 min−1). The ethenolysis of commercial methyl oleate was also performed with a TOF of 8000 min−1 under microwave conditions.
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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) | 0.001 | 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".