Improved Spinnability of Metallocene Polyethylenes by Using Processing Aids
Bibliographic record
Abstract
Abstract Melt spinning is a polymer processing technique that is strongly influenced by the extensional flow behaviour of polymer melts. Therefore only a few polymeric materials are usable for this kind of processing with sufficient take-up speeds. When approaching critical conditions of deformation most polymers show either fibre break in the molten state either by a brittle cohesive rupture or a ductile failure. During the melt spinning of pure and modified metallocene poylethylenes additional flow instabilities occur within the spinning die. Namely, wall slip, ‘sharkskin’ and pressure oscillations (gross fracture) may be obtained dependending on the volume flow rate. Pressure oscillations lead to diameter oscillations of the melt extrudate, which create local increase of tensile stress in the spin line. This effect immediately causes fiber break in the spinline. Therefore, melt spinning of polyethylenes was only possible up to a critical molecular weight or its relating melt viscosity. The limitation of the molecular weight restricts the mechanical properties of the melt spun fibres. This paper reports on an attempt to find out appropriate processing aids for suppressing ‘sharkskin’ effects and pressure oscillations in an attempt to overcome the limitation of a critical molecular weight. At first, the critical conditions for the onset of flow instabilities for higher molecular weight polymers were analysed. Further experiments concerned with the use of processing aids for melt spinning of metallocene polyethylenes of higher molecular weights. A combination of boron nitride powder and a fluoroelastomer was found to be an effective processing aid for this process.
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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) | 0.001 | 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".