The use of biodiesel as a green polymerization solvent at elevated temperatures
Bibliographic record
Abstract
Abstract BACKGROUND: Many important polymers are produced via solution polymerization. The solvent maintains a low viscosity, which provides many practical advantages related to heat transfer, mixing and material handling. Despite these advantages, commonly used solvents often present health and environmental problems. In an effort to replace these toxic solvents, a ‘green’ polymerization solvent, namely canola‐based FAME (fatty acid methyl ester or biodiesel), was used for solution polymerizations at an elevated temperature. RESULTS: Homopolymerizations of methyl methacrylate, styrene, butyl acrylate and vinyl acetate in FAME were studied at different solvent concentrations at 120 °C. Chain transfer to solvent rate constants (Cfs) were obtained for each polymer system and Arrhenius parameters for Cfs, i.e. Ea and A, were also calculated. These new solvent data were employed in a polymerization simulator to predict rate of polymerization and number‐ and weight‐average molecular weights for these commercially important systems. Model predictions showed reasonable agreement with experimental data. CONCLUSION: FAME fulfills the demands as a polymerization solvent. From an ecological perspective, FAME provides an environmentally friendly alternative to common solvents. From an industrial perspective, using FAME as a high‐boiling polymerization solvent can increase productivity by enabling polymerizations at elevated temperatures. Copyright © 2008 Society of Chemical Industry
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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 | 0.000 | 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 | 0.000 |
| 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".