Morphological and Mechanical Properties of Thermoplastic Elastomers Based on Recycled High Density Polyethylene and Recycled Natural Rubber
Bibliographic record
Abstract
Abstract In this work, thermoplastic elastomers (TPE) are produced by melt mixing of recycled high-density polyethylene (RHD) with two types of off-the-road (OTR) ground tire rubber (GTR). Non-regenerated (NR) and regenerated (RR) rubbers are used to investigate the effect of GTR concentration and regeneration on the blends properties containing up to 90 wt.% GTR. The blend morphology is studied by scanning electron microscopy (SEM) to show incompatibility and low interfacial adhesion between RHD and GTR (NR and RR) especially above 40 wt.% RR. This behavior is attributed to the GTR regeneration process and agglomeration of RR particles with lower surface area and affinity toward RHD compared with NR. In all the compounds, the mechanical properties in tension of RR blends were lower than for NR blends, which is attributed to the degradation of the GTR backbone chains lowering the molecular weight (MW) during the regeneration process. Also, NR has a more important effect on impact strength improvement due to its higher crosslinked structure making the particles more deformable/elastic to absorb the mechanical energy before crack initiation. The experimental results also show that 80 wt.% GTR is the optimum concentration for the production of low cost and eco-friendly TPE based on recycled materials.
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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".