A Modified Dynamic Model to Estimate the Reactivity Ratios of Ethylene/1-Olefin Copolymers
Bibliographic record
Abstract
Polymerization kinetic models play a pivotal role in the prediction of copolymer microstructures and polymerization rates, but estimating reactivity ratios still poses challenges for olefin coordination polymerization. The conventional estimation approach uses data collected from low-conversion batch or semibatch copolymerizations across a relatively narrow range of initial monomer/comonomer ratios, with the reactivity ratios being estimated fitting the Mayo–Lewis equation to the experimental data. However, if significant composition drift is significant, one must use a dynamic model to estimate the reactivity ratios. The hard-to-avoid comonomer composition drift is considered a foe of reactivity ratio estimation methods and thus avoided at all costs. In this article, we propose a dynamic mathematical model to estimate reactivity ratios for ethylene/1-olefin copolymerizations in semibatch reactors under substantial composition drift, thus turning a foe into a friend. We applied our estimation method to ethylene/1-hexene copolymerizations using a constrained geometry catalyst. The results underscore how our approach can successfully estimate reactivity ratios of ethylene/1-olefin copolymerizations, considering comonomer composition drift.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".