Getting Ring‐Closing Metathesis off the Bench: Reaction‐Reactor Matching Transforms Metathesis Efficiency in the Assembly of Large Rings
Bibliographic record
Abstract
Reported is the first study of the influence of reactor configuration on the efficiency of a challenging ring-closing metathesis (RCM) reaction. With the intention of increasing the generality of RCM scaleup and reducing its dependence on substrate modification, macrocyclization of an unmodified, low effective-molarity diene was explored using different reactor types, in conjunction with a commercial, homogeneous Grubbs catalyst. Optimized performance is compared for a conventional batch reactor (BR), a continuous plug-flow reactor (PFR), and a continuous stirred-tank reactor (CSTR). In the PFR, maximum conversion is achieved most rapidly, but product yields and selectivity are adversely affected by co-entrapment of ethylene with the catalyst, substrate, and product in the traveling "plug". Use of the CSTR, in which ethylene is efficiently swept out, affords an order-of-magnitude increase in total turnover numbers, and reduces the required catalyst loadings by 25× relative to the BR (to 0.2 mol %), while improving RCM yields and selectivity to quantitative levels. Continuous-flow methodologies that support liberation of the ethylene co-product thus show great promise for industrial uptake of RCM.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".