Experimental and Modeling Investigation of Radical Homopolymerization of 2‐(Methacryloyloxyethyl) Trimethylammonium Chloride in Aqueous Solution
Bibliographic record
Abstract
Abstract The radical homopolymerization kinetics of 2‐(methacryloyloxyethyl) trimethylammonium chloride (TMAEMC) in aqueous solution is investigated across a wide range of initial monomer contents (5–35 wt%), ionic strengths, and pH levels using an in‐situ NMR technique to track monomer consumption over the complete conversion range. Molar mass distributions (MMD) of the final homopolymers are also examined, with additional batch and semi‐batch experiments conducted in a stirred vessel. The rates of monomer conversion and polymer MMDs are dependent on initial monomer content but almost entirely independent of pH and the presence of salts, with some acceleration of rate observed for low monomer levels at very high salt concentration. To aid with the interpretation of these results, the conductivity and counterion activity of monomer and polymer mixtures are measured to determine the extent of electrostatic interactions at various levels of conversion. These results are combined with recently reported measurements of TMAEMC homopropagation kinetics to develop a TMAEMC homopolymerization model that captures the systematic decrease in rates of monomer conversion observed with increased initial monomer content during batch polymerization as well as provides a good representation of semi‐batch polymerization.
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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".