Preparation and Evaluation Recycling Waste Polymers Composites
Bibliographic record
Abstract
In this work, polymeric waste materials such as tires and PVC waste have been exploited in the manufacturing of industrial speed bumps.Tires were processed into granules of approximately 0.185 mm, and PVC waste was reduced to granules with a size of about 0.275 mm.These prepared granules were combined with silicone rubber and a hardening agent to form a cohesive binder.Different prototypes were then crafted using varied proportions of tire to PVC waste specifically, 10%, 20%, 30%, and 40%.These prototypes underwent rigorous testing to evaluate their thermal conductivity coefficient, Shore A hardness, and compressive strength.It was observed that the prototype with a mix of 40% tire waste and 20% PVC waste exhibited the highest thermal conductivity.For hardness, the combination of 40% tire waste and 40% PVC waste achieved the highest Shore A value.However, the greatest compressive strength was exhibited by the prototype with a lower ratio of 10% tire waste to 10% PVC waste.Based on these results, the optimal compressive strengths of 2.25 MPa for the tire component and 2.12 MPa for the PVC.This composition was determined to be the most effective for the application envisioned in this work.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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".