Gas‐phase ethylene polymerization using zirconocene supported on mesoporous molecular sieves
Bibliographic record
Abstract
Abstract Mesoporous molecular sieves, with pore diameters of 2.6–25 nm, were impregnated with methylaluminoxane and bis(butylcyclopentadienyl)zirconium dichloride and tested as catalysts for the gas‐phase homopolymerization of ethylene at ethylene pressures of 200 psi and temperatures of 50–100°C and for 1‐hexene/ethylene copolymerization at 70°C. The activities and activity profiles, at constant Zr and Al contents, depended on the pore size of the supports and the polymerization temperature. Maximum activities for both the homopolymerizations and copolymerizations were observed for catalysts made with supports having pore diameters of 2.6 and 5.8 nm. Homopolymerization activities were highest at temperatures of 70–80°C; average homopolymerization and copolymerization activities up to 9000 kg of polyethylene/(mol of Zr h) were obtained. The weight‐average molecular weights ( M w 's) were not a function of the support pore size but decreased with increasing reaction temperatures, from about 260,000 at 50°C to about 165,000 at 100°C. The polydispersities were essentially constant at 2.5 ± 0.2 for the homopolymers. M w 's for the 1‐hexene/ethylene copolymers had an average value of 117,000 with an average polydispersity of 2.8. The amount of triisobutyl aluminum added to the reactor significantly affected the activity and activity profiles. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 87: 1161–1177, 2003
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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".