Waste Rubber Recycling: A Review on the Evolution and Properties of Thermoplastic Elastomers
Bibliographic record
Abstract
Currently, plastics and rubbers are broadly being used to produce a wide range of products for several applications like automotive, building and construction, material handling, packaging, toys, etc. However, their waste (materials after their end of life) do not degrade and remain for a long period of time in the environment. The increase of polymeric waste materials' generation (plastics and rubbers) in the world led to the need to develop suitable methods to reuse these waste materials and decrease their negative effects by simple disposal into the environment. Combustion and landfilling as traditional methods of polymer waste elimination have several disadvantages such as the formation of dust, fumes, and toxic gases in the air, as well as pollution of underground water resources. From the point of energy consumption and environmental issues, polymer recycling is the most efficient way to manage these waste materials. In the case of rubber recycling, the waste rubber can go through size reduction, and the resulting powders can be melt blended with thermoplastic resins to produce thermoplastic elastomer (TPE) compounds. TPE are multi-functional polymeric materials combining the processability of thermoplastics and the elasticity of rubbers. However, these materials show poor mechanical performance as a result of the incompatibility and immiscibility of most polymer blends. Therefore, the main problem associated with TPE production from recycled materials via melt blending is the low affinity and interaction between the thermoplastic matrix and the crosslinked rubber. This leads to phase separation and weak adhesion between both phases. In this review, the latest developments related to recycled rubbers in TPE are presented, as well as the different compatibilisation methods used to improve the adhesion between waste rubbers and thermoplastic resins. 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".