Processability of LLDPE/LDPE blends: Capillary extrusion studies
Bibliographic record
Abstract
Abstract The processing behavior of a number of linear low‐density polyethylenes/low density polyethylene (LLDPE/ LDPE) blends with emphasis on the effects of long chain branches is presented. A Ziegler‐Natta linear low‐density polyethylene was blended with four low‐density polyethylene LDPE's having distinctly different molecular weights. The weight fractions of the LDPEs used in the blends were 1, 5, 10, 20, 50, and 75 wt%. Capillary extrusion reveals that the onset of sharkskin and gross melt fracture are slightly influenced with the addition of LDPE into LLDPE. However, the amplitude of the oscillations in the stick‐slip flow regime was found to scale well with the weight fraction of LDPE. Amounts as low as 1 wt% LDPE have a significant effect on the amplitude of pressure oscillations. These effects are clearly due to the presence of long chain branching (LCB); furthermore, it was observed that the onset of this flow regime was shifted to higher shear rates with increase of LDPE content. On the other hand, shear rheology is not sensitive to detect addition of small levels of LDPE up to 20 wt%. Extensional rheology can detect levels of LDPE as small as 1 wt% only at high Hencky strain rates (typically greater than 5s −1 ) and only for certain blends, typically those that contain LDPE of high molecular weight. It is suggested that the magnitude of oscillations in the oscillating melt fracture flow regime is a sensitive method capable of detecting low levels of LCB. POLYM. ENG. SCI., 47:1317–1326, 2007. © 2007 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".