Rheological and thermomechanical properties of long‐chain‐branched polyethylene prepared by slurry polymerization with metallocene catalysts
Bibliographic record
Abstract
Abstract A series of polyethylene (PE) samples were prepared in a slurry polymerization with bis(cyclopentadienyl) zirconium dichloride (Cp2ZrCl2)/modified methylaluminoxane (MMAO) using a semibatch reactor. The samples had long‐chain branch densities (LCBDs) of a 0.03–1.0 branch per 10,000 carbons and long‐chain branch frequencies (LCBFs) up to a 0.22 branch per polymer molecule. The rheological and dynamic mechanical behaviors of these long‐chain branched PE samples were evaluated. Increasing the LCBF significantly increased the η0's and enhanced shear thinning. Long‐chain branching (LCB) also influenced the loss modulus and storage modulus. Increasing the LCBF led to enhanced G′ and G″ values at low shear rates and broader relaxation spectrums. The samples exhibited thermorheologically complex behavior. LCB also played a significant role in the dynamic mechanical behavior. Increasing the LCBF increased the stiffness of the polymer and enhanced the damping or energy dissipation. However, LCB had little influence on the crystalline structure of the PE. The α‐ and γ‐relaxations showed little dependence on the LCBF. © 2004 Wiley Periodicals, Inc. J Appl Polym Sci 92: 307–316, 2004
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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".