Phase morphology, mechanical, and thermal properties of fiber-reinforced thermoplastic elastomer: Effects of blend composition and compatibilization
Bibliographic record
Abstract
In this work, recycled high density polyethylene (rHDPE) was compounded with regenerated tire rubber (RR) (35-80 wt.%) and reinforced with recycled tire textile fiber (RTF) (20 wt.%) as a first step. The materials were compounded by melt extrusion, injection molded, and characterized in terms of morphological, mechanical, physical, and thermal properties. Although, replacement of the rubber phase with RTF compensated for tensile/flexural moduli losses of rHDPE/RR/RTF blends because of the more rigid nature of fibers increasing the composites stiffness, the impact strength substantially decreased. So, a new approach is proposed for impact modification by adding a blend of maleic anhydride grafted polyethylene (MAPE)/RR (70/30) into a fiber-reinforced rubberized composite. As in this case, a more homogeneous distribution of the fillers was observed due to better compatibility between MAPE, rHDPE, and RR. The tensile properties were improved as the elongation at break increased up to 173% because of better interfacial adhesion. Impact modification of the resulting thermoplastic elastomer (TPE) composites based on rHDPE/(RR/MAPE)/RTF was successfully performed (improved toughness by 60%) via encapsulation of the rubber phase by MAPE forming a thick/soft interphase decreasing interfacial stress concentration slowing down fracture. Finally, the thermal stability of rubberized fiber-reinforced TPE also revealed the positive effect of MAPE addition on molecular entanglements and strong bonding yielding lower weight loss, while the microstructure and crystallinity degree did not significantly change up to 60 wt.% RR/MAPE (70/30).
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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".