Binary blends of metallocene polyethylene with conventional polyolefins: Rheological and morphological properties
Bibliographic record
Abstract
Abstract The rheology and morphology of four sets of binary blends of polyethylene synthesized with metallocene catalysis (metallocene polyethylene: MCPE) with polyolefins prepared using Ziegler‐Natta catalysts have been investigated. The blend systems are MCPE with high density polyethylene (MCPE‐HDPE), polypropylene (MCPE‐PP), poly(propylene‐co‐ethylene) (MCPE‐CoPP), and poly(propylene‐co‐ethylene‐co‐1‐butylene) (MCPE‐TerPP). Cole‐Cole plots [storage melt viscosity (η′) versus loss melt viscosity (η″)], plots of the dynamic storage modulus (G′) versus the dynamic loss modulus (G″), and plots of the log melt viscosity (η*, η′, and η″) versus blend compositions were constructed. The morphology of the blends after microtoming and etching was studied. The phase morphology of MCPE‐HDPE appeared homogeneous, whereas the other three blends were heterogeneous. Rheological and morphological investigations indicated that the MCPE‐HDPE blend was miscible, but the other three blends were immiscible in the melt as well as in the solid state. These observations can be rationalized in terms of the similarity of the chemical structures of the polyolefins.
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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 | 0.001 | 0.001 |
| 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".