Enhanced electromagnetic interference shielding effectiveness of hybrid fillers by segregated structure
Bibliographic record
Abstract
Polymer nanocomposites seem to be promising candidate for the electromagnetic interference (EMI) shielding material because of its loss cost, lightweight, flexibility, and ease of production. However, to achieve high shielding, there are concerns about the nanofiller concentration and dispersion of nanofillers in the polymer matrix. To overcome this issue, implementing the effective filler dispersion technique by segregated structure were investigated. In this study using miscible mixing and precipitation method (MSMP), polystyrene (PS) matrix was fabricated by incorporation of multiwall carbon nanotubes (CNT) as primary filler and nickel nanowires (NiNW) or zinc oxide nanowires (ZnONW) as secondary filler. Preparing a hybrid structure of the dielectric/magnetic nanofiller along with CNT led to significant increase in EMI shielding effectiveness (SE) when compared to the single filler systems. For instance, PS/2.0vol% CNT nanocomposites showed EMI SE of about 16.6dB (in X-band frequency) for 1.1 mm thick shield. By adding 0.5vol% of the secondary filler either NiNW or ZnONW, it enhanced the EMI SE to 23.2dB and 24.0dB respectively. This excellent EMI SE of the hybrid nanocomposites was attributed to both selectively distributed conductive nanofillers in a segregated structure and excellent magnetic/dielectric properties of the synthesized NiNW/ZnONW, respectively. Details of the dielectric loss mechanisms for the architectures were studied. Besides achieving reliable dielectric properties, CNT/NiNW or CNT/ZnONW nanocomposite shield can be prepared with low thickness and low filler content, making them useful for various shielding applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".