Enhancing the Dielectric Properties of Recycled Polyolefin Streams Through Blending
Bibliographic record
Abstract
The extensive use of polyolefins, such as polyethylene (PE) and polypropylene (PP), has led to a substantial accumulation of plastic waste, raising growing concerns about environmental impact and sustainability. In this study, the dielectric, thermal, and chemical properties of recycled materials were investigated, and blending with virgin polyethylene was examined as a sustainable strategy to enhance their electrical performance and promote material reuse. Dielectric analysis demonstrated that blending recycled materials with virgin polyethylene effectively reduced dielectric losses. With the addition of only 15% virgin HDPE, the dielectric loss was significantly lowered by 40% for recycled HDPE (rHDPE) and 30% for the recycled PE-PP blend (r(PE-PP))—compared to their unblended forms. Although the original recycled materials exhibited much higher dielectric losses than virgin HDPE—24 and 28 times greater for rHDPE and r(PE-PP), respectively, at 60 Hz—the blending approach clearly improved their electrical behavior. Overall, the results highlight blending as a practical and sustainable strategy to improve the dielectric performance of recycled polyolefins, enabling their reuse in applications such as electrical cable insulation while contributing to plastic waste reduction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".