Synthesis of reactor blend of linear and branched polyethylene using metallocene/Ni‐diimine binary catalyst system in a single reactor
Bibliographic record
Abstract
Abstract This work reports the synthesis of a series of reactor blends of linear and branched polyethylene materials using a combination of [1,4‐bis(2,6‐diisopropylphenyl) acenaphthene diimine nickel(II) dibromide] ( 1 )/MMAO, known as an active catalyst for the production of branched polyethylene, and [rac‐ethylenebis(indenyl) zirconium dichloride] ( 2 )/MMAO, which is active for the production of linear polyethylene. The polymerization runs were performed at various levels of temperature, pressure, and catalyst 2 molar fractions. At 5°C, there was very low influence of catalyst 2 molar fraction on the overall catalyst activity. However, at 30°C and 50°C, the overall catalyst activity increased linearly with catalyst 2 molar fraction. The same linear dependency was also found for the polymerization reactions carried out at 60°C and 100°C. At various levels of temperature and ethylene pressure, higher melting temperature and crystallinity were observed with an increase in catalyst 2 molar fraction. At 60°C and 100 psig, the DSC thermograms of the polymers produced with 1 / 2 /MMAO exhibited two distinct peaks with melting temperatures closely corresponding to the melting temperatures of the polymers produced with the individual catalysts, 1 /MMAO and 2 /MMAO. The GPCV analysis of all polyethylene samples showed monomodal molecular weight distributions with low polydispersities. © 2005 Wiley Periodicals, Inc. J Appl Polym Sci 96: 2212–2217, 2005
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".