Kinetics of MMA Atom Transfer Radical Polymerization Initiated by Reducing Agent in Stirred Batch Emulsion Reactor
Bibliographic record
Abstract
Abstract Ab initio emulsion atom transfer radical polymerization (ATRP) differs from regular emulsion polymerization because the kinetic and thermodynamic aspects of each process are very unlikely alike. This paper presents a kinetic analysis of activator generated by electron transfer (AGET) ATRP of methyl methacrylate (MMA) in a stirred emulsion reactor. The focus of the study is to assess the variation of the monomer content in the organic phase and the rate polymerization for different reaction temperatures, as well as the impact of surfactant content and stirring speed on latex stability. Poly(methyl methacrylate) (PMMA) polymer samples are analyzed by means of gravimetry, dynamic light scattering, gel permeation chromatography, and HNMR techniques to determine monomer conversion, polymer particle diameter, molecular weight distribution, and polymer molecular structure, respectively. The results show the production of PMMA of narrow polydispersity index (Ð) and low molar mass (Mn) for MMA conversion around 60% and detect two‐regions instead of a three‐regions trend of rate of polymerization (Rp) as conventional emulsion polymerization. The reaction rate increases during the nucleation stage and then flattens over an invariant trend which confirms a living feature of the AGET ATRP polymerization. The average number of radicals per latex particle (ñ) is also debated.
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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 | 0.001 |
| 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".