Effects of tire‐derived pyrolytic carbon black and pyrolytic heavy oil on the curing and mechanical properties of styrene‐butadiene rubber composites
Bibliographic record
Abstract
Abstract To investigate the potential effect of the pyrolytic carbon black (CBp) and pyrolytic heavy oil (HO), we incorporated them into styrene‐butadiene rubber (SBR) as reinforcing filler and plasticizing agent, respectively. Since the industrial N330 CB's specific surface area and other specifications adhere to the CBp standard, it has been selected as a benchmark. To test HO's applicability, conventional (AO) was substituted with it. We experimented with the N330 and CBp in the 1/1 variant. We examined the impacts on the cure behavior, physical characteristics such as density and hardness, mechanical properties, abrasion resistance, dynamic mechanical performance, and thermal properties of the filled SBR composites. According to field emission (FESEM) and transmission electron microscopy (TEM) micrographs, the increased tensile and tear strength is due to N330 black's superior dispersion over CBp in the rubber matrix. Better dispersion in N330 type CB is also justified by lower tan δ max from the dynamic mechanical analysis (DMA) study. Utilizing HO changes the curing conditions since it comprises more sulfur, which accelerates the formation of sulfur–sulfur cross‐links. Increased tensile and tear strengths, as well as improved dispersion in the rubber matrix, have been seen with the combined use of N330 and CBp. Meanwhile, HO could function well in place of commercially available AOs. Highlights The impact of scrap tires on human health and the environment is explored. Recovered carbon black (CB) from waste tire mixed with styrene‐butadiene rubber in various ratios. Pyrolytic oil is also incorporated to check its reusability in tire formulation. Similar cure characteristics and mechanical strength were found in the composites. Replacement of the conventional CB is proposed with recovered black.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".