Extrusion foaming of multiphasic polyethylene/ethylene-vinyl acetate copolymer/carbon nanotube mixtures: Tailoring foam properties by selective localization of nanoparticles
Bibliographic record
Abstract
Semi-conductive foams based on low-density polyethylene/ethylene-vinyl acetate copolymer (LDPE/EVA) blends in the presence of carbon nanotubes (CNTs) were prepared using a twin-screw extrusion process. The effects of CNTs content and localization state in the binary mixture on the physical and structural properties of LDPE/EVA/CNT foams were investigated. The results confirmed that the void fraction, cell density, bubble size and cell size distribution of foams are optimal against CNT loading. The lightest LDPE/EVA/CNT foam was obtained by the CNT localization in the LDPE matrix. This foam containing 2.5 phr of CNT had smaller cells and more uniform cell size comparing to the pure blend foam. The cell density of this foam was 1.598 × 10 6 cells/cm 3 , which is much larger than that for the blend foam, 8.64 × 10 5 cells/cm 3 . However, the CNT localization state in the dispersed EVA domains resulted in lower void fractions and cell densities comparing with the LDPE/EVA blend foam. The findings clarify the profound impact of the nanofiller localization state on the foam properties of the binary polymeric systems. Light semiconductive LDPE/EVA foams with small cells, uniform cell size and high cell densities were achieved by localizing and dispersing the CNT nanoparticles in the LDPE matrix phase.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".