Carbon nanotube/ZnO nanowire/polyvinylidene fluoride hybrid nanocomposites for enhanced electromagnetic interference shielding
Bibliographic record
Abstract
Abstract Lightweight and flexible materials with high conductivity and low thickness are highly desirable for electromagnetic interference (EMI) shielding applications. Developing hybrid nanostructured materials, which combine the properties of their constituents, is an excellent strategy to create highly effective EMI shields. In this study, we report a hybrid polymer nanocomposite composed of carbon nanotube (CNT) and ZnO nanowire (ZnONW) for EMI shielding applications. We found that the combination of a conductive filler (CNT) and a dielectric filler (ZnONW) with a similar geometry is an effective method to fabricate nanocomposites with enhanced EMI shielding. We achieved high average shielding effectiveness of 27.3 dB (with a maximum of 41 dB at 10.2 GHz) for a sample of CNT:ZnONW (5.0:2.5 wt%) with only 1.1 mm thickness, which is among the best‐reported values in the literature for polymer nanocomposites with similar filler loading and thickness. This performance originates from the excellent electrical conductivity and dielectric properties of hybrid nanocomposites, combined with the geometry of ZnO nanowire. A comparison of the shielding properties of the developed hybrid nanocomposites with the literature implies that they are promising functional materials in the world of EMI shielding 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".