Ethylene/1‐Hexene Copolymerization Kinetics and Microstructure of Copolymers Made with a Supported Metallocene Catalyst
Bibliographic record
Abstract
Abstract Ethylene/1‐olefin copolymers made with supported metallocenes in slurry or gas‐phase polymerization are often less homogeneous than those made with the same unsupported metallocene in solution polymerization. In particular, their molecular weight distributions are broader, having polydispersities higher than two, and sometimes their chemical composition distributions may even be bimodal. In our previous publication, we developed a mathematical model to describe the polymerization kinetics and polymer microstructure of ethylene homopolymers made with a supported metallocene catalyst. In this article, we extended that model to also cover the copolymerization of ethylene and 1‐hexene with the same supported catalyst. The copolymerizations are performed in parallel semibatch reactors using a metallocene catalyst supported on an inorganic porous carrier. 1‐Hexene concentration and polymerization time are the factors changed to investigate this system. Modeling results show that, as for the ethylene homopolymerization case, a three‐site model is needed to describe the molecular weight distributions of the copolymers, but their chemical compositions can be described with a single set of reactivity ratios. A single set of parameters is also enough to describe the copolymerization kinetics with this supported catalyst. A new method is also developed and tested to estimate reactivity ratios under composition drift in this article.
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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 | 0.000 |
| 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".