The Role of Selectively Located Commercial Graphene Nanoplatelets in the Electrical Properties, Morphology, and Stability of EVA/LLDPE Blends
Bibliographic record
Abstract
Abstract Graphene nanoplatelets (GN) produced on a large scale by mechanochemical exfoliation of graphite are incorporated in a co‐continuous ethylene‐vinyl acetate/linear low‐density polyethylene (EVA/LLDPE) blend. Two different processing routes are chosen to selectively place GN in the EVA phase or force its migration to the EVA/LLDPE interface. The results show a drastic decrease in the electrical percolation threshold when the blends are compared to the respective single‐polymer composites. Even with the presence of agglomerates, GN particles are able to migrate to the blend interface and stabilize the morphology and hence the electrical properties. Annealing the insulating samples at processing temperatures causes a drastic increase in conductivity due to continued GN migration and blend morphology coarsening. Semi‐conductive samples, in which a more robust GN network is already established during processing, present no change in morphology but a slight increase in conductivity during annealing. The mechanical performance of the materials is also evaluated and some of the blends with GN present similar elongation at break as pure EVA, but with increased tensile modulus and tensile strength. The electrical performance at different working temperatures shows that the EVA/LLDPE/GN composites are good candidates to act as a semi‐conductive screen material in power cables or as anti‐static materials in electronic devices.
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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) | 0.000 | 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 | 0.000 |
| 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 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".