Modeling of Reversible Deactivation Radical Polymerization of Vinyl Monomers Promoted by Redox Initiation Using NHPI and Xanthone
Bibliographic record
Abstract
Abstract Two mathematical approaches for modeling of the kinetics and evolution of molar mass distributions in the reversible deactivation radical polymerization of vinyl monomers promoted by redox reaction with N‐hydroxyphthalimide (NHPI) and xanthone (XT) are presented. In the first modeling approach, the polymerization scheme is implemented in the standard version of the Predici commercial software. In the second case, an accelerated, self‐implemented version of the so called kinetic Monte Carlo (kMC) approach, considering binary trees, is used. The effect of concentrations of XT and monomer, as well as monomer type, on monomer conversion, NHPI efficiency, molar mass averages, molar mass dispersity, and full molar mass distributions are studied. The models are validated using literature available experimental data of polymerizations of methyl methacrylate (MMA) and styrene, in toluene, at 70 °C. The calculated results indicate that NHPI initiator efficiencies are low (<0.2); polymer end‐group functionalities present a maximum value of about 0.8 with a subsequent decrease with monomer conversion; molar mass distributions are broad, exhibiting low molar mass tails. In addition, the hypothetical copolymerization of styrene and MMA is also considered. Copolymer composition distributions for short molecules are broader than those for large ones.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".