Measurement and Modeling of Semi-Batch Solution Radical Copolymerization of N-tert-Butyl Acrylamide with Methyl Acrylate in Ethanol/Water
Bibliographic record
Abstract
The synthetic polymer industry is transitioning from the use of organic solvents to aqueous media in order to reduce environmental impact. However, with radical polymerization kinetics affected by hydrogen-bonding solvents, there is limited information regarding the use of water as a solvent for sparingly soluble monomers. Thus, in this paper, the radical polymerization of methyl acrylate (MA) and N-tert-butylacrylamide (t-BuAAm) is studied in water and ethanol (EtOH), as the copolymer product is of commercial interest. A series of semi-batch reactions are conducted under a range of operating conditions (i.e., reaction temperature, solvent-to-monomer ratio, and comonomer composition) to demonstrate that the copolymer can be successfully synthesized without significant drifts in product molar masses or composition. The experiments provide additional data to probe the influence of the solvent on the polymerization rate and copolymer properties, as the low monomer concentration maintained under starved-feed operation leads to a solvent-to-monomer ratio different from that in a batch system. A model that captures the influence of backbiting and solvent effects on rate, previously developed and tested against batch polymerizations, also provides an excellent description of semi-batch operation, validating the set of mechanisms and kinetic coefficients developed to represent the system.
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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 | 0.001 | 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".