Influence of processing aids on the uniaxial extensional behavior of metallocene polyethylenes
Bibliographic record
Abstract
Abstract It has been reported in the literature that boron nitride powders with hexagonal crystal structure in combination with fluoroelastomers at small concentrations (typically 500–1000 ppm) can be used as suitable processing aids in melt extrusion processes to overcome flow instabilities such as sharkskin and gross melt fracture. They essentially act as energy dissipaters and suppress the unbound increase of extensional stresses, which are responsible for triggering gross melt fracture. This paper reports on the mechanism by which the presence of a small amount of boron nitride changes the behavior of metallocene polyethylenes in uniaxial elongation. Samples modified with different processing aids were tested in solid and molten state using uniaxial elongation and other rheological tests. The stress–strain behavior is described by the BDEW (Ball–Doi–Edwards–Warner; Ball et al., Polymer, 22, 1010, ()) network theory. This theory predicts a reduction of the entanglement density (number of “slip‐links”) in the presence of boron nitride particles that explains the decrease in extensional stresses. Thus, it can be argued that the presence of boron nitride particles dissipates the release of elastic energy at lower extensional stress levels due to the decrease in the entanglement density, thus preventing catastrophic failures such as gross melt fracture. POLYM. ENG. SCI., 46:735–742, 2006. © 2006 Society of Plastics Engineers
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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".