Effect of Ground Tire Rubber (GTR) Particle Size and Content on the Morphological and Mechanical Properties of Recycled High-Density Polyethylene (rHDPE)/GTR Blends
Bibliographic record
Abstract
This work investigates the effect of ground rubber tire (GRT) particle size and their concentration on the morphological, mechanical, physical, and thermal properties of thermoplastic elastomer (TPE) blends based on recycled high-density polyethylene (rHDPE). In our methodology, samples are prepared via melt blending (twin-screw extrusion followed by compression molding) to prepare different series of blends using GTR with three different particle sizes (0–250 μm, 250–500 μm, and 500–850 μm) for different GTR concentrations (0, 20, 35, 50, and 65 wt.%). The thermal properties are characterized by differential scanning calorimeter (DSC), and the morphology of the blends is studied by scanning electron microscopy (SEM). The mechanical and physical properties of the blends are investigated by quasi-static tensile and flexural tests, combined with impact strength and dynamic mechanical analysis (DMA). The SEM observations indicate some incompatibility and inhomogeneity in the blends, due to low interfacial adhesion between rHDPE and GTR (especially for GTR > 50 wt.%). Increasing the GTR content up to 65 wt.% leads to poor interphase (high interfacial tension) and agglomeration, resulting in the formation of voids around GTR particles and increasing defects/cracks in the matrix. However, introducing fine GTR particles (0–250 μm) with higher specific surface area leads to a more homogenous structure and uniform particle dispersion, due to improved physical/interfacial interactions. The results also show that for a fixed composition, smaller GTR particles (0–250 μm) gives lower melt flow index (MFI), but higher tensile strength/modulus/elongation at break and toughness compared to larger GTR particles (250–500 μm and 500–850 μm).
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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".