Flexible high dielectric polystyrene/ethylene‐α‐octene copolymer/graphene nanocomposites: Tuning the morphology and dielectric properties by graphene's surface polarity
Bibliographic record
Abstract
Abstract The current research focuses on suggesting a new potential application of polymeric materials for capacitor applications by focusing on polystyrene (PS)/ethylene‐α‐octene copolymer (EOC) blends. Polymeric materials have high dielectric strength (≈ 200 for PS), good processability, and flexibility. However, their low dielectric constants make them not suitable for capacitor applications. Therefore, suggesting a method for enhancing their dielectric constant accompanied by low tan δ proposes an excellent substitute for commonly used ceramic dielectrics. The current article investigates the control of the dielectric properties of PS/EOC blends by manipulating the graphene nanoplatelets (GNPs) surface polarity. Results confirm that the localization site of the GNPs is mainly controlled by their surface polarities; this matter leads to the change of the morphology from sea/island for the pure blend to co‐continuous for the blend containing the most polar GNPs resulting in the best dielectric properties. This manipulation of the morphology by low concentration of GNPs (1.5 wt%) and therefore, dielectric properties result in high dielectric constant ≈ 55.8 at 10 MHz and low tan δ = 0.007, accompanied by high flexibility, which is the main drawback of ceramic capacitors, makes it a fantastic option for capacitor applications.
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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".