Effect of combining boron nitride with fluoroelastomer on the melt fracture of HDPE in extrusion blow molding
Bibliographic record
Abstract
Abstract Boron nitride (BN) is a new polymer processing aid which not only eliminates surface melt fracture in the extrusion of molten polymers, but also postpones the critical shear rate for the onset of gross melt fracture to significantly higher values that depend on resin type and additive concentration. In this work, the influence of BN as a polymer processing additive is first examined in the extrusion blow molding of high‐density polyethylene (HDPE) resins in order to evaluate its usefulness and performance in operations other than continuous extrusion. The equipment used includes both a Battenfeld/Fisher 50‐mm extrusion blow molding machine and a parallel‐plate rheometer. Two types of HDPE, which are blended with boron nitride at various concentration levels, are tested accordingly. It is found that the degree of BN dispersion, characteristics of the HDPE resins, extrusion temperature, and induction time play an important role in eliminating melt fracture. Finally, the influence of combining BN with fluoroelastomer, as an enhanced and potentially better processing aid on the melt fracture of a third HDPE is examined. It is found that such a combination is a superior processing aid that allows extrusion blow molding at very high shear rates.
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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".