Enhancing Mechanical Performance of High-Density Polyethylene at Different Environmental Conditions with Outstanding Foamability through In-Situ Rubber Nanofibrillation: Exploring the Impact of Interface Modification
Bibliographic record
Abstract
In this study, we utilized in situ nanofibrillation of thermoplastic polyester ether elastomer (TPEE) within a high-density polyethylene (HDPE) matrix to enhance the rheological properties, foamability, and mechanical characteristics of the HDPE nanocomposite at both room and subzero temperatures. Due to the inherent polarity differences between these two components, TPEE is thermodynamically incompatible with the nonpolar HDPE. To address this compatibility issue, we employed a compatibilizer, styrene/ethylene-butylene/styrene copolymer-grafted maleic anhydride (SEBS- g -MA), to reduce the interfacial tension between the two blend components. In the initial step, we prepared a 10% masterbatch of HDPE/TPEE with and without the compatibilizer using a twin-screw extruder. Subsequently, we processed the 10% masterbatch further through spun bonding to create fiber-in-fiber composites. Scanning electron microscopy (SEM) analysis revealed a significant reduction in the spherical size of HDPE/TPEE particles following the inclusion of SEBS- g -MA, as well as a much smaller TPEE nanofiber size (approximately 60–70 nm for 5% TPEE). Moreover, extensional rheological testing revealed a notable enhancement in extensional rheological properties, with strain-hardening behavior being more pronounced in the compatibilized nanofibrillar composites compared to the noncompatibilized ones. SEM images of the foam structures depicted substantial improvement in the foamability of HDPE in terms of the cell size and density following the nanofibrillation process and the use of the compatibilizer. Ultimately, the in situ rubber fibrillation and enhancement of HDPE and TPEE interface using a compatibilizer led to increasing the HDPE ductility at room and subzero temperatures while maintaining its stiffness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".