Solvent Effects on Free-Radical Copolymerization Propagation Kinetics of Styrene and Methacrylates
Bibliographic record
Abstract
Solvent effects on free-radical copolymer composition and propagation kinetics of styrene (ST) with three methacrylates, 2-hydroxyethyl methacrylate (HEMA), glycidyl methacrylate (GMA), and n -butyl methacrylate (BMA), are investigated using pulsed-laser polymerization combined with size exclusion chromatography and proton NMR. Three representative solvents, n -butanol, toluene, and DMF ( N,N -dimethylformamide), are selected based on their polarity and structure. It was found that all three solvents have an effect on ST/HEMA copolymer composition compared to bulk copolymerization, with a systematic variation in monomer reactivity ratios observed with solvent polarity. Only butanol affects ST/BMA copolymer composition, and solvent choice has no effect on composition for the ST/GMA system. Butanol increases the composition-averaged copolymerization propagation rate coefficient, k p,cop, only for the ST/BMA system, while DMF causes a uniform decrease of k p,cop for all three systems and toluene has no observable effect on k p,cop . These rate effects are linked to corresponding solvent effects on homopropagation kinetics. It is difficult to reconcile the combined copolymer composition and k p,cop data for ST/HEMA systems and for ST/BMA in butanol using the implicit penultimate model of chain-growth, suggesting that H-bonding interactions need to be explicitly considered.
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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) | 0.000 | 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 | 0.000 |
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".