Mathematical Modeling of Atom‐Transfer Radical Polymerization Using Bifunctional Initiators
Bibliographic record
Abstract
Abstract Summary: Bifunctional initiators can produce polymers with higher molecular weight at higher initiator concentrations than monofunctional initiators. In this study, we developed a mathematical model for ATRP with bifunctional initiators. The most important reactions in ATRP were included in the model. The method of moments was used to predict monomer conversion, average molecular weights and polydispersity index as a function of polymerization time in batch reactors. The model was used to understand the mechanism of ATRP and to quantify how polymerization conditions affect monomer conversion and polymer properties by examining the effect of several rate constants (activation, deactivation, propagation and chain termination) and of catalyst and initiator concentration on polymerization kinetics and polymer properties. When compared to monofunctional initiators, bifunctional initiators not only produce polymers with higher molecular weight averages at higher polymerization rates, but also control their molecular weight distributions more effectively. Effect of initial catalyst concentration on polydispersity index as a function of time. magnified image Effect of initial catalyst concentration on polydispersity index as a function of time.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".