Heterogeneous olefin‐metathesis: Comparative perspective of the activity with respect to unsaturated fatty acid methyl esters
Bibliographic record
Abstract
Abstract Olefin metathesis is one of the most exiting tools in synthetic chemistry that can explicitly be employed to produce higher value products from unsaturated fats and oil feedstock. Contrary to the early stage metathesis catalysts, being extremely sensitive to humidity and to the substrate functionalities, redesigned organometallic species not only exhibit greater activity and selectivity but also offer pronounced stability towards functional olefins and ambient atmosphere. Though a number of highly efficient homogeneous catalysts have been developed, these are generally costly and also lead to product contamination and, consequently, pose environmental concerns. Heterogeneous catalysis can be an alternate route; however it faces a number of hurdles such as support compatibility, multiple active sites, and abridged efficacy. Nevertheless, through careful designing, certain heterogeneous catalyst systems have been suggested for metathesis of functionalized olefins like fatty acid methyl esters (FAMEs). A number of metallic species such as organometallic compounds bearing metal‐alkylidene moieties (e.g. Grubbs‐/Schrock‐type systems), alkylidene‐free complexes (e.g. methyltrioxorhenium), and inorganic (W, Mo, Re) oxides/chlorides have been probed in this regard. Accordingly this mini review deals with the various aspects like heterogenization/immobilization, activity criteria, and reusability of such catalyst systems in the context of olefin metathesis of natural fats/oils derivatives.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".