Review on modification strategies of polyethylene/polypropylene immiscible thermoplastic polymer blends for enhancing their mechanical behavior
Bibliographic record
Abstract
Blends of polyethylene (PE) and polypropylene (PP) have always been the subject of intense reasearch for encouraging polymer waste recycling while producing new materials for specific applications in a sustainable way. However, being thermodynamically immiscible, these polyolefins form a binary system usually exhibiting lower performances compared with those of the homopolymers. Many studies have been carried out to better understand the PE/PP blend compatibilization for developing a high-performance and cost-effective product. Both nonreactive and reactive compatibilization promote the brittle to ductile transition for a PE/PP blend. However, the final product usually does not meet the requirements for high demanding commercial applications. Therefore, further PE/PP modification with a reinforcing filler, being either synthetic or natural, proved to be a good method for manufacturing high-performance reinforcend polymer blend composites, with superior and tailored properties. This review summarizes the recent progress in compatibilization techniques applied for enhancing the interfacial adhesion between PE and PP. Moreover, future perspectives on better understanding the influence of themodynamics on PE/PP synergy are discussed to introduce more effective compatibilization strategies, which will allow this blend to be used for innovative industrial applications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".