Melt Rheological Properties of Branched Polyethylenes Produced with Pd‐ and Ni–Diimine Catalysts
Bibliographic record
Abstract
Abstract Summary: Seven branched polyethylenes differing in chain topology from hyperbranched to linear structure were synthesized with chain walking Pd‐diimine catalyst, [(ArNC(Me)C(Me)NAr) Pd(CH 3 )(NCMe)]SbF 6 ( 1 ), and Ni‐diimine catalyst, (ArNC(An)C(An)NAr) NiBr 2 ( 2 )/MMAO, respectively. An extensive rheological study, employing steady‐shear, creep‐recovery, and dynamic oscillation tests, was conducted to examine and compare the melt rheological properties of this novel series of polymers. It was found that the change of chain topology dramatically affected the polymer flow behavior, flow activation energy, and dynamic moduli ( G ′( ω ) , G ″( ω )). The hyperbranched polymers exhibited typical Newtonian flow behavior and extremely low viscosity. The polymers with chain topology intermediate between hyperbranched and linear structures, however, were essentially viscoelastic materials. All the polymers obeyed the time‐temperature superposition and exhibited enhanced flow activation energy (43.8∼57.2 kJ/mol) compared to HDPE and LLDPE. In the terminal region, these polymers had different dependencies of dynamic moduli ( G ′( ω ), G ″( ω )) on angular frequency ( ω ) and different master curves in the log( G ′) versus log( G ″) plot. The hyperbranched polymer was also blended with more linear samples as a rheology modifier and was found to significantly lower the viscosity of the blends. Structure of the Pd‐ and Ni‐catalysts used in this study. image Structure of the Pd‐ and Ni‐catalysts used in this study.
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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".