Simultaneous Improvement of Oxygen Barrier and Stiffness in High-Density Polyethylene via Effective Integration of Interface Engineering with in Situ Ethylene–Vinyl Alcohol Copolymer Nanofibrillation
Bibliographic record
Abstract
In this study, we proposed a novel technique to simultaneously enhance the oxygen barrier properties and stiffness of high-density polyethylene (HDPE) while preserving its ductility. By utilizing in situ nanofibrillation, fiber-in-fiber composites of an HDPE matrix and ethylene–vinyl alcohol (EVOH) nanofibers were fabricated. Due to the high interfacial tension between HDPE and EVOH, stemming from their differences in chemical structure and polarity, styrene/ethylene-butylene/styrene copolymer grafted with maleic anhydride (SEBS- g -MA) was used as a compatibilizer to improve the affinity between the two polymers. SEM images revealed that the presence of the compatibilizer resulted in smaller fiber sizes (reduced to 65 ± 27 nm from 147 ± 54 nm for 6 wt % compatibilized EVOH compared to noncompatibilized samples), higher aspect ratios, and better distribution. Increasing the aspect ratio and improving nanofiber distribution reduced HDPE’s oxygen permeability by 61% after incorporating 10 wt % compatibilized EVOH nanofibers. Additionally, the nonisothermal and isothermal crystallization indicated that EVOH nanofibers reduced the amount of crystallinity and slowed crystallization kinetics. The alteration in HDPE crystalline structure and its effect on permeability properties were also addressed. Finally, tensile test results indicated that the incorporation of 10 wt % EVOH nanofibers, regardless of the presence of the compatibilizer, increased HDPE Young’s modulus by around 50%. However, without the compatibilizer, there was a significant reduction in HDPE elongation at the break. The incorporation of the compatibilizer allowed for increased stiffness while preserving HDPE ductility. These promising findings underscore potential applications across rigid and soft packaging.
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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) | 0.000 | 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".