Flexible, Ultrathin, and High-Efficiency Electromagnetic Shielding Properties of Poly(Vinylidene Fluoride)/Carbon Composite Films
Bibliographic record
Abstract
In this study, we fabricated conductive poly(vinylidene fluoride) (PVDF)/carbon composites simply by dispersing multiwalled carbon nanotubes (MWCNTs) and graphene nanoplatelets into a PVDF solution. The electrical conductivity and the electromagnetic interference (EMI) shielding of the PVDF/carbon composites were increased by increasing the conductive carbon filler amounts. Moreover, we also found that the EMI shielding properties of the PVDF/CNT/graphene composites were higher than those of PVDF/CNT and PVDF/graphene composites. The mean EMI shielding values of PVDF/5 wt %-CNT, PVDF/10 wt %-graphene, and PVDF/CNT/graphene composite films with a thickness of 0.1 mm were 22.41, 18.70, and 27.58 dB, respectively. An analysis of the shielding mechanism showed that the main contribution to the EMI shielding came from the absorption mechanism, and that the EMI shielding could be tuned by controlling the films’ thickness. The total shielding of the PVDF/CNT/graphene films increased from 21.90 to 36.46 dB as the thickness was increased from 0.06 mm to 0.25 mm. In particular, the PVDF/carbon composite films, with a thickness of 0.1 mm, achieved the highest specific shielding values of 1 310 dB cm 2 /g for the PVDF/5 wt %-CNT composite and 1 557 dB cm 2 /g for the PVDF/CNT/graphene composite, respectively. This was due to the ultrathin thickness. Our study provides the groundwork for an effective way to design flexible, ultrathin conductive polymer composite film for application in miniaturized 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) | 0.000 | 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 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".