Evaluating the performance of processing aids in eliminating melt fracture
Bibliographic record
Abstract
This study presents a comprehensive experimental protocol to evaluate the effectiveness of a polymer processing aid (PPA) in eliminating melt fracture of a metallocene linear low-density polyethylene using both capillary rheometry and single-screw extrusion. The prime effects addressed are those of the die length-to-diameter ratio (L/D), the concentration of the PPA, and the temperature on the pressure transients in startup flow and wall slip, while monitoring extrudate appearance. Results show that increasing the temperature or the PPA concentration helps eliminate melt fracture. We also found that the effect of the L/D ratio on the melt fracture differs depending on whether PPA is present. In detail, using longer dies worsens melt fracture in the absence of PPA, while the opposite is true in the presence of PPA. This is because longer coated dies allow more stress relaxation of the melt before exiting the die. Results obtained from a capillary rheometer are compared with those obtained from a single-screw extruder to relate rheometry with real processing. Excellent agreement is found pointing to the significance of using capillary rheometry to evaluate the performance of PPAs in polymer processing operations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".