Interfacial instabilities in coextrusion flows of low‐density polyethylenes: Experimental studies
Bibliographic record
Abstract
Abstract A fundamental investigation into the interfacial instability phenomenon was performed. Coextrusion experiments were carried out using well‐characterized low‐density (LDPE) resins in an effort to gain a better understanding of interfacial instability phenomena. The resins used were chosen carefully and included materials of high and low viscosity as well as broad and narrow molecular weight distributions (MWD). The experiments involved the coextrusion of either the same material in both layers or various combinations of the four materials and the focus of the work was to elucidate the effects of flow rates, molecular weight (MW) and MWD on interfacial instability. The effect of the geometry at the point where the materials merged was also investigated. It was concluded that there are essentially two types of interfacial instabilities and that the MW had the strongest effect on the occurrence of the “zig‐zag” instability due to high interfacial stress while the breadth of the MWD had a strong effect on the appearance of the “wave” instability. Broad MWD materials had a greater tendency to exhibit interfacial instability, which is more due to layer ratio than processing conditions or die geometries. The results suggest that the origin of the “wave” type of interfacial instability is due to an extreme extensional deformation of the minor layer at the merge point and that the viscoelastic properties of adjacent layers determine the instability development.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".