Rheological study of the miscibility of LLDPE/LDPE blends and the influence of <i>T</i><sub>mix</sub>
Bibliographic record
Abstract
Abstract The miscibility of LLDPE and LDPE blends and the influence of mixing temperature (Tmix) are discussed. Adequate amounts of antioxidants were added during melt blending. Dynamic and steady shear measurments were carried out at 190°C in a Rheometrics Mechanical Spectrometer 800.The dependence of rheological properties on blend composition indicated that blends of LLDEPE (butene) and LDPE mixed at 190°C and 220°C are only partially miscible; immiscibilty is likely to occur around the 50/50 composition and in the LDPE‐rich blends. Blends at 190°C are likely miscible in the LLDPE‐rich range. Tncreaasing Tmix did improve the miscibility of LLDPE/LDPE blends at 190°C or influence the dynamic sher properties (η′ or G′) of the “pure” resins of blends. Suggested that the molecular order [see Hussein and Williams, J. Non‐Newt. Fluid Mech., 86 105 (1999); Hussein and Williams, Macromol. Rapid Commun., 19, 323 (1998)] and mismatch of the molecular conformations of different polyethylene structures provide an explanation for the immiscibility of polyethylenes. Agreement was observed between themeasured dynamic properities and theortical predictions of Palierne and Bousmina‐Karner emulsion models.
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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".