Recycled Tire Fibers Used as Reinforcement for Recycled Polyethylene Composites
Bibliographic record
Abstract
This study proposes a simple approach to separate most rubber particles from recycled tire fibers (RTFs) and to determine their rubber content using thermogravimetric analysis (TGA)/calcination. Furthermore, scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDAX), and Fourier transform infrared spectroscopy (FTIR) analyses are used to investigate the separation process and materials compositions. Afterwards, a series of composites based on recycled post-consumer low-density polyethylene (rLDPE) with clean fiber (CF) and residual ground rubber particles (GR) is prepared at different filler concentrations (0–30%) via extrusion compounding before using compression molding and injection molding for comparison. In all cases, injection molding leads to higher strength and modulus but lower elongation at break. The results show that incorporating 30 wt.% of CF into rLDPE yields a remarkable improvement in tensile strength (15%), tensile modulus (192%) and flexural modulus (142%). On the other hand, the incorporation of up to 30 wt.% of GR results in a reduction in both tensile strength and flexural modulus by 15%, confirming the critical role of the cleaning process for RTF in achieving the best results.
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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".