Effects of molecular structure on the rheology and processability of blow-molding high-density polyethylene resins
Bibliographic record
Abstract
The influence of molecular structure on the rheology and processability of blow-molding grade high-density polyethylene (HDPE) resins is studied using capillary and extensional rheometers, a melt indexer, and a blow-molder unit. Twenty-four commercial HDPE resins were analyzed in terms of their shear and extensional flow properties, extrudate swell characteristics, and melt strength. The resins had varying molecular weight characteristics and were produced using a variety of polymerization technologies (gas, slurry, and solution phase). It was found that shear viscosity is not only influenced by the weight average molecular weight (Mw) and polydispersity index (PI), but also technology dependent, irrespective of molecular characteristics. Increasing Mw was found to increase both shear and extensional viscosity, while increasing PI by increasing the concentration of smaller molecules increases the tendency of the resin to shear thin. In relating melt strength and temperature sensitivity of shear viscosity to molecular parameters, resins had to be grouped according to ranges of PI < 8, 8. < PI < 10, and PI > 10. Moreover, it was possible to relate melt strength to the Hencky strain obtained from creep experiments and to the melt index of the resins. Finally, it was found that extrudate swell behavior and melt strength are important parameters to be considered during parison formation, as observed during blow-molding experiments. © 2001 John Wiley & Sons, Inc. Adv Polym Techn 20: 1–13, 2001
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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 | 0.001 |
| 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".