Development of an eco-friendly thermoplastic composite material from waste tires and biopolymer
Bibliographic record
Abstract
The increasing demand for sustainable polymeric materials has driven research into eco-friendly alternatives to petroleum-based polymers. This study investigates the development of a thermoplastic composite by reinforcing bio-based high-density polyethylene (Bio-HDPE), derived from sugarcane ethanol, with functionalized recycled carbon black (FrCB) obtained via vacuum pyrolysis of waste tires. The goal is to evaluate the mechanical and thermal properties of Bio-HDPE/FrCB composites compared to those reinforced with commercial carbon black (cCB) and assess their potential as a sustainable substitute. Composites with 3 wt% and 15 wt% filler were prepared and characterized using differential scanning calorimetry (DSC), scanning electron microscopy (SEM), and mechanical testing (tensile, hardness, and impact strength). Results indicate that FrCB acts as a nucleating agent, increasing crystallinity up to 65.1 % at 15 % FrCB and enhancing tensile modulus by 47 % (1844 MPa) and hardness by 15 % (67.58 Shore D). However, the filler reduced impact strength due to agglomeration and weak interfacial adhesion. Compared to cCB, FrCB showed superior compatibility with Bio-HDPE, yielding higher tensile modulus and hardness at equivalent loadings. These findings demonstrate that Bio-HDPE/FrCB composites offer a viable, eco-friendly alternative to conventional HDPE/cCB composites, with enhanced mechanical performance and reduced carbon footprint.
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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".