Effects of aluminum alkyls on ethylene/1‐hexene polymerization with supported metallocene/MAO catalysts in the gas phase
Bibliographic record
Abstract
Abstract The effects of aluminum alkyls on the gas‐phase ethylene homopolymerization and ethylene/1‐hexene copolymerization over polymer‐supported metallocene/methylaluminoxane [(n‐BuCp)2ZrCl2/MAO] catalysts were investigated. Results with triisobutyl aluminum (TIBA), triethyl aluminum (TEA), and tri‐n‐octyl aluminum (TNOA) showed that both the type and the amount of aluminum alkyl influenced the polymerization activity profiles and to a lesser extent the polymer molar masses. The response to aluminum alkyls depended on the morphology and the Al : Zr ratio of the catalyst. Addition of TIBA and TEA to supported catalysts with Al : Zr >200 reduced the initial activity but at times resulted in higher average activities due to broadening of the kinetic profiles, i.e., alkyls can be used to control the shape of the activity profiles. A catalyst with Al : Zr = 110 exhibited relatively low activity when the amount of TIBA added was <0.4 mmol, but the activity increased fivefold by increasing the TIBA amount to 0.6 mmol. The effectiveness of the aluminum alkyls in inhibiting the initial polymerization activity is in the following order: TEA > TIBA >> TNOA. A 2‐L semibatch reactor, typically run at 80°C and 1.4 MPa ethylene pressure for 1 to 5 h was used for the gas‐phase polymerization. © 2004 Wiley Periodicals, Inc. J Appl Polym Sci 92: 3549–3560, 2004
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".