Bibliographic record
Abstract
ABSTRACT Vulcanized rubber, due to unique characteristics, has seen main uses in automobiles, mostly as tires. Even with the latest shifts in the industry toward electrical drives, vehicles still ride on tires. Today, tires reported in the public domain consist of about 19% natural rubber and 24% synthetic rubbers, while plastics, metal, fillers, and additives make up the rest. Globally, the rubber industry claims to produce over 1.6 billion tires annually, and waste managers report collecting a billion waste tires after usage; the rest remains with the users, breaks down in service, or illegally piles in dumpsters. Tires of extensive designs and complex manufacturing withstand the harshness of service life. Consequently, their disposal creates monumental technical and industrial challenges. Current disposal strategies to retiring tires—consisting of incineration, crumb rubber generation, and landfilling—show clear shortcomings. Waste tire rubber recovery and regeneration are preferred for rubber sustainability and rubber product circular economy. Multiple devulcanization processes introduced selective cleavages of the crosslinks of the vulcanizates while retaining polymeric structure. This paper reviews devulcanization methods explored, such as chemical, mechanical, biological, and their combinations. It presents additional steps necessary to turn postconsumer goods based on rubbers (like end-of-life tires) into engineering materials and products. In this paper we offer a new perspective on sustainable waste rubber recovery and reuse. In a follow-up paper, we will discuss the steps to put postindustrial rubbers and rubber products back into production, toward zero waste rubber and rubber product manufacturing.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".