Enhancing the mechanical, morphological, and rheological behavior of polyethylene/polypropylene blends with maleic anhydride‐grafted polyethylene
Bibliographic record
Abstract
Abstract This is the first study to showcase the use of maleic anhydride‐grafted polyethylene (MAPE) to compatibilize polyethylene (PE)‐rich blends, where polypropylene (PP) represents the minor phase. By first mixing PP with MAPE, and then adding PE, MAPE was assumed to be localized at the PE/PP interface. Microscopy analysis confirmed that MAPE led to a remarkably fine PE/PP/MAPE morphology, with PP being uniformly dispersed into PE and having an average diameter 267% smaller than that in the PE/PP blend. According to mechanical and rheological tests, this translated into a 14%, 20%, and 14% enhancement of tensile strength, tensile modulus, and tensile toughness, respectively, as well as a 10% and 20% drop in PE/PP viscosity mismatch and interfacial tension, respectively. Finally, PE/PP/MAPE tensile toughness and elongation at break were greater than those of virgin PP, while PE/PP/MAPE strength and stiffness were similar to the ones of neat PP. Therefore, this study provides industries with the possibility to utilize products rich in PE instead of those made of more expensive PP, while still keeping the level of performance high; hence, creating a paradigm shift in the development of advanced lightweight polyolefin materials with tuned functionalities.
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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".