Multifunctional UPE Composites Reinforced with Recycled PET/HDPE Hybrids
Bibliographic record
Abstract
This study presents the fabrication and comprehensive evaluation of hybrid polymer composites based on unsaturated polyester resin (UPE) reinforced with recycled polyethylene terephthalate (PET) and high-density polyethylene (HDPE) in a fixed weight ratio of 80:20.Varying filler contents (2.5, 5, 7.5, and 10 wt.%) were incorporated into the UPE matrix using a hand lay-up method to investigate the influence of reinforcement loading on mechanical, thermal, and acoustic properties.Results revealed that compressive strength peaked at 2.5 wt.%, while impact strength and hardness reached their highest values at 7.5 wt.%.Flexural strength declined with increasing filler content due to matrix discontinuities and interfacial stress.Notably, thermal conductivity and acoustic insulation improved progressively with filler loading, attaining maximum values at 10 wt.%, attributed to enhanced phonon transport and internal wave scattering.FTIR analysis confirmed a physically blended system without significant chemical bonding, indicating that performance enhancement was driven by dispersion quality, interfacial compatibility, and hybrid filler morphology.These findings demonstrate the feasibility of tailoring composite behavior through controlled loading of recycled hybrid fillers, offering an eco-friendly solution for multi-functional polymer materials in structural and acoustic 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) | 0.000 | 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".