Rheological and thermomechanical properties of long‐chain‐branched polyethylene prepared by slurry polymerization with metallocene catalysts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract A series of polyethylene (PE) samples were prepared in a slurry polymerization with bis(cyclopentadienyl) zirconium dichloride (Cp 2 ZrCl 2 )/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 η 0 '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
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it