Free radical copolymerization kinetics of bio-based dibutyl itaconate and n-butyl acrylate
Bibliographic record
Abstract
• 50% of bio-based itaconate has been incorporated into acrylic polymer resin. • Highly reactive acrylate facilitates the incorporation of itaconate. • The copolymer is synthesized under industrially-relevant operating conditions. • Radical copolymerization kinetics are studied in batch and semi-batch modes. • A comprehensive mechanistic model is developed to guide further development. The free radical copolymerization of dibutyl itaconate (DBI), a monomer derived from renewable resources, and n -butyl acrylate (BA) is investigated to explore the potential of incorporating itaconate monomers into commercial acrylic resins to enhance the sustainability of coating materials. Batch experiments were conducted at 50 °C and 80 °C, varying initial monomer and initiator concentrations as well as monomer compositions, with proton NMR and size exclusion chromatography used to analyze monomer conversion profiles and polymer molar mass distributions (MMDs). The data were analyzed using a kinetic model developed to guide the experimental studies and estimate key kinetic parameters controlling copolymer composition, polymerization rate and polymer MMDs. The use of the more reactive BA as comonomer significantly increases the conversion and molar mass of the resulting copolymer. Furthermore, semi-batch operating conditions similar to current industrial practice can incorporate a substantial fraction of DBI into acrylic copolymer resins, thus enhancing their sustainability profile.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".