Effect of secondary filler properties and geometry on the electrical, dielectric, and electromagnetic interference shielding properties of carbon nanotubes/polyvinylidene fluoride nanocomposites
Bibliographic record
Abstract
Abstract Hybrid polymer nanocomposites based on polyvinylidene fluoride (PVDF) as the matrix, carbon nanotubes (CNTs) as the primary conductive filler, and metal nanoparticles as secondary fillers were fabricated by melt mixing. Secondary nanofillers with different geometry and properties (nickel nanowire (NiNW), silver nanowire (AgNW), nickel nanoparticle (NiNP), and silver nanoparticle (AgNP) were selected to investigate the effect of geometry and properties of secondary filler on the hybrid polymer nanocomposites' properties. Electrical conductivity, electromagnetic interference (EMI) shielding effectiveness, and complex microwave properties of the fabricated hybrid nanocomposites were studied in X‐band frequency (8.2–12.4 GHz). The hybrid nanocomposites containing CNT/AgNW demonstrated superior conductivity and EMI shielding compared to individual fillers such as CNT, AgNW, NiNW, AgNP, NiNP, or hybrid system such as CNT/NiNW. The novelty of the present study lies in the unique synergy arising from the combination of nanofillers with similar geometry and high electrical conductivity, which resulted EMI shielding effectiveness as high as 27 dB for a shield with only 1.1 mm thickness. The EMI shielding mechanisms, including negative permittivity were studied and explained in details in the manuscript.
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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".