Stochastic Modeling of Poly(acrylate) Distributions Obtained by Radical Polymerization under High‐Temperature Semi‐Batch Starved‐Feed Conditions: Investigation of Model Predictions versus Experimental Data
Bibliographic record
Abstract
Abstract Secondary reactions significantly affect acrylate polymerization rates as well as the architecture of polymer produced by high‐temperature solution radical polymerization. This impact is amplified under the semi‐batch starved‐feed policy used to keep monomer concentration low. Thus, the importance of intramolecular chain transfer (backbiting) is significantly increased, generating a tertiary radical center capable of termination, propagation, and scission. In this investigation, a comprehensive stochastic model is formulated to represent results from an experimental study designed to increase the fraction of reactive terminal double bonds (TDB) in the poly(butyl acrylate) product. Model predictions generated using three sets of literature kinetic parameters for backbiting and scission are compared. While each provides reasonable predictions of some reaction characteristics (e.g., free monomer levels, polymer molecular weights, polymer TDB content), none provide an adequate representation of all aspects of the polymerization. It is concluded that other reaction pathways might be needed to represent the system under semi‐batch conditions, thus explaining the discrepancies seen among the current parameter estimates.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".