Greener solvent systems for copper wire-mediated living radical polymerisation
Bibliographic record
Abstract
Copper wire-mediated living radical polymerisation (LRP) is a powerful tool that provides numerous opportunities for the development of new materials. It requires very low catalyst loading and mild reaction conditions; however, it typically involves the use of organic solvents. The authors have investigated the use of greener solvent systems to replace traditional volatile organic solvents without detrimentally affecting polymerisation. The effects of these alternative solvents on the control over polymerisation and polymer characteristics were investigated for the polymerisation of methyl acrylate initiated by ethyl 2-bromoisobutyrate. Copper wire-mediated LRP was conducted in dimethyl sulfoxide (DMSO), polyethylene glycol (PEG) and polypropylene glycol (PPG) and binary mixtures of PEG–DMSO, PEG–ethanol, PPG–DMSO and PPG–ethanol with total solvent volume fractions of 33 and 50%. Secondary solvent fractions in the binary mixtures were examined at 10 and 25% of the total solvent volume. The two most effective greener solvent systems were 33% v/v of 75% PEG–25% ethanol and 33% v/v of 75% PPG–25% ethanol. Both were shown to provide excellent control over polymerisation and a high degree of livingness. The poly(methyl acrylate) produced in these solvent systems retained high bromine chain-end functionality (>90%) and low dispersity (∼1·1).
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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 | 0.001 | 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".