Recycling Waste Tires into Ground Tire Rubber (GTR)/Rubber Compounds: A Review
Bibliographic record
Abstract
Recycling and recovery of waste tires is a serious environmental problem since vulcanized rubbers require several years to degrade naturally and remain for long periods of time in the environment. This is associated to a complex three dimensional (3D) crosslinked structure and the presence of a high number of different additives inside a tire formulation. Most end-of-life tires are discarded as waste in landfills taking space or incinerated for energy recovery, especially for highly degraded rubber wastes. All these options are no longer acceptable for the environment and circular economy. However, a great deal of progress has been made on the sustainability of waste tires via recycling as this material has high potential being a source of valuable raw materials. Extensive researches were performed on using these end-of-life tires as fillers in civil engineering applications (concrete and asphalt), as well as blending with polymeric matrices (thermoplastics, thermosets or virgin rubber). Several grinding technologies, such as ambient, wet or cryogenic processes, are widely used for downsizing waste tires and converting them into ground tire rubber (GTR) with a larger specific surface area. Here, a focus is made on the use of GTR as a partial replacement in virgin rubber compounds. The paper also presents a review of the possible physical and chemical surface treatments to improve the GTR adhesion and interaction with different matrices, including rubber regeneration processes such as thermomechanical, microwave, ultrasonic and thermochemical producing regenerated tire rubber (RTR). This review also includes a detailed discussion on the effect of GTR/RTR particle size, concentration and crosslinking level on the curing, rheological, mechanical, aging, thermal, dynamic mechanical and swelling properties of rubber compounds. Finally, a conclusion on the current situation is provided with openings for future works.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".