Ethylene/1‐octene copolymerization studies with <i>in situ</i> supported metallocene catalysts: Effect of polymerization parameters on the catalyst activity and polymer microstructure
Bibliographic record
Abstract
Abstract Ethylene and 1‐octene copolymerizations were carried out with an in situ supported rac‐[dimethylsilylbis(methylbenzoindenyl)] zirconium dichloride catalyst. In a previous study, it was found that some in situ supported metallocenes produced polyethylene/α‐olefin copolymers with broad and bimodal short chain branching distributions and narrow molecular weight distributions. The ability to produce polyolefins with multimodal microstructural distributions in a single metallocene and a single reactor is attractive for producing polymers with balanced properties with simpler reactor technology. In this study, a factorial experimental design was carried out to examine the effects of the polymerization temperature and ethylene pressure, the presence of hydrogen and an alkylaluminum activator, and the level of the comonomer in the feed on the catalyst activity, short chain branching distribution, and molecular weight distribution of the polymer. The temperature had the most remarkable effect on the polymer microstructure. At high 1‐octene levels, the short chain branching distribution of the copolymer broadened significantly with decreasing temperature. Several factor interactions, including the hydrogen and alkylaluminum concentrations, were also observed, demonstrating the sensitivity of the catalyst to the polymerization conditions. For this catalyst system, the responses to the polymerization conditions are not easily predicted from typical polymerization mechanisms, and several two‐factor interactions seem to play an important role. Given the multiple‐site nature of the catalyst, it has been shown that predicting the polymerization activity and the resulting microstructure of the polymer is a challenging task. © 2002 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem 40: 4426–4451, 2002
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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".