Ethylene Polymerization with Silica‐Supported Nickel‐Diimine Catalyst: Effect of Support and Polymerization Conditions on Catalyst Activity and Polymer Properties
Bibliographic record
Abstract
Abstract Ethylene was polymerized using both homogeneous and modified methylaluminoxane (MMAO)‐treated silica supported nickel‐diimine catalysts (1,4‐bis(2,6‐diisopropylphenyl) acenaphthene diimine nickel(II) dibromide) in a slurry semibatch reactor. The effects of catalyst support and polymerization conditions (ethylene pressure and reaction temperature) on catalyst activity and polymer properties were systematically investigated. The supported catalyst gave far lower activity than the homogeneous catalyst. The activities of both catalyst systems increased with polymerization temperature with a maximum at 40 °C. Compared with the homogeneous catalyst, the supported catalyst system produced polyethylene with a different microstructure. Due to steric effects, the supported catalyst system exhibited lower chain walking rates than the homogeneous catalyst, producing polymers with less branching content and, thus higher melting points. Depending on polymerization conditions, two active site populations were observed during polymerization using supported catalyst; one population remained fixed on the surface of the support, and the other was extracted from the support, exhibiting the same polymerization behavior as the homogeneous catalyst. DSC thermograms for polyethylene produced with homogeneous and supported catalysts at an ethylene pressure of 50 psig (3.45 · 105 Pa) and reaction temperature 40 °C. magnified image DSC thermograms for polyethylene produced with homogeneous and supported catalysts at an ethylene pressure of 50 psig (3.45 · 105 Pa) and reaction temperature 40 °C.
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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) | 0.001 | 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".