Influence of Poly(ethylene glycol) Structure in Catalytic Macrocyclization Reactions
Bibliographic record
Abstract
The first evaluation of the structural effects of six different poly(ethylene glycol) (PEG)-derived polymers in MeOH mixtures on their aggregation abilities, ability to control dilution effects, and catalysis has been performed through examining surface tension measurements and the isolated yields of a model Glaser–Hay macrocyclization reaction of diyne 3 . Three different structural effects were studied involving (1) the presence of capping groups on the terminal hydroxyl functionalities of the polymers, (2) the length of the polymer chain, and (3) the effects of branching alkyl groups in the polymer backbone. The data obtained provides important guidelines for conducting macrocyclizations using PEG/solvent mixtures, suggesting that macrocyclizations are most efficient at high ratios of PEG/MeOH and when employing medium-length lipophilic branched poly(propylene glycol) (PPG) polymers. In particular, the use of PPG bearing terminal uncapped hydroxyl groups allows for a significant reduction in the catalyst loading. The macrocyclization studies reinforce that the aggregation characteristics of PEG-derived solvents can be harnessed in catalysis, particularly in reactions in which control of concentration effects is important.
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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 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".