Layered Foam/Film Polymer Nanocomposites with Highly Efficient EMI Shielding Properties and Ultralow Reflection
Bibliographic record
Abstract
Abstract Lightweight, high-efficiency and low reflection electromagnetic interference (EMI) shielding polymer composites are greatly desired for addressing the challenge of ever-increasing electromagnetic pollution. Lightweight layered foam/film PVDF nanocomposites with efficient EMI shielding effectiveness and ultralow reflection power were fabricated by physical foaming. The unique layered foam/film structure was composed of PVDF/SiCnw/MXene (Ti 3 C 2 T x ) composite foam as absorption layer and highly conductive PVDF/MWCNT/GnPs composite film as a reflection layer. The foam layer with numerous heterogeneous interfaces developed between the SiC nanowires (SiCnw) and 2D MXene nanosheets imparted superior EM wave attenuation capability. Furthermore, the microcellular structure effectively tuned the impedance matching and prolonged the wave propagating path by internal scattering and multiple reflections. Meanwhile, the highly conductive PVDF/MWCNT/GnPs composite (~ 220 S m −1 ) exhibited superior reflectivity (R) of 0.95. The tailored structure in the layered foam/film PVDF nanocomposite exhibited an EMI SE of 32.6 dB and a low reflection bandwidth of 4 GHz (R < 0.1) over the Ku-band (12.4 − 18.0 GHz) at a thickness of 1.95 mm. A peak SE R of 3.1 × 10 –4 dB was obtained which corresponds to only 0.0022% reflection efficiency. In consequence, this study introduces a feasible approach to develop lightweight, high-efficiency EMI shielding materials with ultralow reflection for emerging 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".