A systematic study of <scp><i>tert</i>‐</scp>butylacrylamide‐methyl acrylate‐acrylic acid radical solution terpolymerization
Bibliographic record
Abstract
Abstract Multi‐functional polymers used for personal care products can be synthesized by radical polymerization of acrylic acid (AA) in alcohol/water solutions with non‐functional monomers such as methyl acrylate (MA) and N‐tert‐butylacrylamide (t‐BuAAm). However, solvents capable of forming or disrupting hydrogen bonds cause the polymerization kinetics of these monomers to deviate from their polymerization behaviour in bulk and non‐polar solvents. In this work, a previous mechanistic model developed for MA/t‐BuAAm copolymerization is extended to represent the terpolymerization system MA/t‐BuAAm/AA. The additional kinetic coefficients required for the system are estimated from fitting to AA homopolymerizations and AA/MA and AA/t‐BuAAm copolymerizations conducted in an ethanol/water solution. In‐situ nuclear magnetic resonance (NMR) spectroscopy is used to follow monomer conversions and composition drift behaviour, with the molar mass distributions of the polymer products characterized by size‐exclusion chromatography. Although AA is more reactive than MA in non‐polar solvents, the reactivities of the two monomers equalize under the experimental conditions examined. Thus, the batch and semi‐batch terpolymerization data collected are represented equally well by a reduced acrylate/t‐BuAAm copolymerization model and the full terpolymerization implementation.
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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) | 0.001 | 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".