Influence of support friability and concentration of α‐olefins on gas‐phase ethylene polymerization over polymer‐supported metallocene/methylaluminoxane catalysts
Bibliographic record
Abstract
Abstract The effects of support friability (Φ) and ethylene/comonomer ratios were investigated over supported metallocene/methylaluminoxane catalysts prepared with nine different porous polymeric supports and various comonomer concentrations with a 2‐L reactor operated in the semibatch gas‐phase mode at 80°C and 1.4 MPa. Φ of the supports was measured with a newly devised method. The performance of the supported catalysts depended on support Φ as follows. The average homopolymerization activities varied from less than 6 t of polyethylene (PE) (mol of Zr)−1 h−1 for low‐Φ catalysts to 10–20 t of PE (mol of Zr)−1 h−1 for moderate‐Φ catalysts and up to 100 t of PE (mol of Zr)−1 h−1 for the high‐Φ catalysts. The presence of 1‐hexene and propylene comonomers increased the activity of the low‐Φ catalysts by up to 20‐fold and 50‐fold, respectively; that is, there were very marked comonomer effects. Activity enhancement by 1‐hexene was less than 3‐fold for the moderate‐Φ catalysts, whereas the high‐Φ catalysts showed little activity enhancement. Sometimes, 1‐hexene even resulted in activity reductions. Very different particle morphologies were obtained with the catalysts of different Φ's. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci 104: 514–527, 2007
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".