Waste to Value-Added Product: Developing Electrically Conductive Nanocomposites Using a Non-Recyclable Plastic Waste Containing Vulcanized Rubber
Bibliographic record
Abstract
This study intends to show the potential application of a non-recyclable plastic waste towards the development of electrically conductive nanocomposites. Herein, the conductive nanofiller and binding matrix are carbon nanotubes (CNT) and polystyrene (PS), respectively, and the waste material is a plastic foam consisting of mainly vulcanized nitrile butadiene rubber and polyvinyl chloride (PVC). Two nanocomposite systems, i.e., PS/Waste/CNT and PS/CNT, with different compositions were melt-blended in a mixer and characterized for electrical properties. Higher electrical conduction and improved electromagnetic interference shielding performance in PS/Waste/CNT system indicated better conductive network of CNTs. For instance, at 1.0 wt.% CNT loading, the PS/Waste/CNT nanocomposites with the plastic waste content of 30 and 50 wt.% conducted electricity 3 and 4 orders of magnitude higher than the PS/CNT nanocomposite, respectively. More importantly, incorporation of the plastic waste (50 wt.%) reduced the electrical percolation threshold by 30% in comparison with the PS/CNT nanocomposite. The enhanced network of CNTs in PS/Waste/CNT samples was attributed to double percolation morphology, evidenced by optical images and rheological tests, caused by the excluded volume effect of the plastic waste. Indeed, due to its high content of vulcanized rubber, the plastic waste did not melt during the blending process. As a result, CNTs concentrated in the PS phase, forming a denser interconnected network in PS/Waste/CNT samples.
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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 | 0.001 |
| 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 | 0.000 |
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".