Elongational rheology of LLDPE / LDPE blends
Bibliographic record
Abstract
Abstract The objective of this study is to investigate the effect of low density polyethylene (LDPE) content in linear low density polyethylene (LLDPE) on the crystallinity and strain hardening of LDPE / LLDPE blends. Three different linear low density polyethylenes (LL‐1, LL‐2 and LL‐3) and low density polyethylenes (LD‐1, LD‐2 and LD‐3) were investigated. Eight blends of LL‐1 with 10, 20, 30 and 70 wt % of LD‐1 and LD‐3, respectively, were prepared using a single screw extruder. The elongational behavior of the blends and their constituents were measured at 150°C using an RME rheometer. For the blends of LL‐1 with LD‐1, the low shear rate viscosity indicated a synergistic effect over the whole range of concentrations, whereas for the blends of LL‐1 with LD‐3, a different behavior was observed. For the elongational viscosity behavior, no significant differences were observed for the strain hardening of the 10–30% LDPE blends. Thermal analysis indicated that at concentrations up to 20%, LDPE does not significantly affect the melting and crystallization temperatures of LLDPE blends. In conclusion, the crystallinity and rheological results indicate that 10–20% LDPE is sufficient to provide improved strain hardening in LLDPE. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 88: 3070–3077, 2003
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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".