Recycled Carbon Black/High-Density Polyethylene Composite from Waste Tires: Manufacturing, Testing, and Aging Characterization
Bibliographic record
Abstract
This study addresses the global issue of recycling used vehicle tires, typically burned out or trimmed to be reused in playground floors or road banks. In this study, we explore a novel environmentally responsive approach to decomposing and recovering the carbon black particles contained in tires (25–30 wt.%) by vacuum pyrolysis. Given that carbon black is well known for its UV protection in plastics, the objective of this research is to provide an ecological alternative to commercial carbon black of fossil origin by recycling the carbon black (rCB) from used tires. In our research, we create a composite material using rCB and high-density polyethylene (HDPE). In this article, we present the environmental aging studies carried out on this composite material. The topographic evolution of the samples with aging and the oxidation kinetics of the surface and through the thickness were studied. The Beer–Lambert law is used to relate the oxidative index to the characteristic depth of the samples. The UV photons are observed to penetrate up to 54% less with the addition of 6 wt.% of rCB compared to virgin HDPE. In this work, the addition of rCB as filler for HDPE used for outdoor applications has demonstrated to be an antioxidant for UV protection and a good substitute for commercial carbon black for industrial goods.
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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".