Terpolymerization of Triisopropylsilyl Acrylate, Methyl Methacrylate, and Butyl Acrylate: Reactivity Ratio Estimation
Bibliographic record
Abstract
Abstract Ternary monomer reactivity ratios of triisopropylsilyl acrylate (SiA), methyl methacrylate (MMA), and n‐butyl acrylate (BA), as common monomers in self‐polishing coatings (SPCs) binders are obtained using experimental data collected from free radical bulk polymerization at 70 °C. Different terpolymerizations at low and medium‐high conversions are performed at optimized feed compositions. Estimations are made using the error‐in‐variables model (EVM) framework, applying the recast form of the Alfrey–Goldfinger (AG) model and a direct numerical integration (DNI) approach to the collected data. Estimations from individual low and medium‐high conversion data are compared to those found with the combined data (full conversion range data). The highest certainty in point estimates are obtained with analysis of the full conversion range data. Furthermore, the reactivity ratios determined from the combined data fall between those found with analysis of individual low and medium‐high conversion data, another corroboration of reliable data collection. Reactivity ratios determined from analysis of the combined data (rSiA/MMA = 0.4185, rMMA/SiA = 1.3754, rSiA/BA = 0.8739, rBA/SiA = 0.5736, rBA/MMA = 0.3692, rMMA/BA = 1.7919) are used in the recast AG model to predict cumulative terpolymer composition as a function of conversion. The experimental data and model prediction show satisfactory agreement.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".