The effect of surface energy of boron nitride on polymer processability
Bibliographic record
Abstract
Abstract Flow instabilities manifest themselves as distortions on the extrudate surface (melt fracture). They are usually observed at high production rates in many polymer processing operations. Certain fluoropolymers/fluoroelastomers have long been used as processing aids for surface melt fracture elimination. Recent developments have shown that a small amount of boron nitride (BN) powder may successfully eliminate surface melt fracture and also delay the onset of gross melt fracture to higher rates. It has also been reported that a combination of BN and fluoropolymer/fluoroelastomer enhances the effectiveness of the polymer processing even further. The main objective of the present work was to measure the surface properties of a number BN powders, mainly surface energy, in order to gain a better understanding of its performance as a processing aid. Based on this study, it can be concluded that surface energy plays an important role in deciding the possible interactions between the processing aid, polymer melt and the extruding surface. It is observed that the lubricious nature of BN along with an optimum balance of its polar (non‐dispersive) and non‐polar (dispersive) components of surface energy renders BN a successful processing aid in eliminating both sharkskin and gross melt fracture phenomena. Polym. Eng. Sci. 44:1543–1550, 2004. © 2004 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".