Slip behavior of high-density polyethylene at small shear stresses in the presence of esterified polyethylene glycol
Bibliographic record
Abstract
Viscoelastic instabilities in polymer melts can be mitigated using polymer processing aids (PPAs) that impose slip between melts and substrates. In this study, the effect of a newly synthesized esterified polyethylene glycol (PEG) on the slip behavior of a high-molecular-weight high-density polyethylene at small shear stresses was investigated. Rheological measurements were employed to capture the dependence of slip velocities on shear stress and calculate extrapolation lengths and friction coefficients. Our findings showed that the incorporation of PEG-based PPA increased slip velocities while an increase at temperature suppressed slip. At 190 °C, there was a strong slip zone at shear stresses smaller than 9 kPa for all samples with and without PEG. In this zone, the extrapolation length showed ascending and descending behavior while its values were almost constant beyond this zone. The incorporation of PEG-based PPA at 190 °C doubled the extrapolation length from 300 to 600 μm and amplified its variation in the first zone. The study of the slip behavior at 210 °C revealed that the samples did not experience the first zone. They showed a second zone with extrapolation lengths well below 100 μm, signifying the presence of a weak slip regime. This study highlights the importance of PPAs in altering the slip mechanisms for high-molecular-weight polymer melts.
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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) | 0.000 | 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 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".