Experiment and simulation of flexible CNT/SA/PDMS electromagnetic shielding composite
Bibliographic record
Abstract
Flexible electromagnetic shielding composites have a great potential for wide range applications. In this study, two flexible composites were produced by plating Ni nanoparticles on carbon nanotubes (CNTs) or infiltrating carbon nanofibers/polydimethylsiloxane (CNF/PDMS) polymer into CNT/sodium alginate (CNT/SA) sponge skeleton (CNT/SA/CNF/PDMS composites). The composites are tested under the X band in the frequency range of 8.2 - 12.4 GHz, the electromagnetic interference shielding effectiveness (EMI-SE) values of the above two composites are almost as twice as that of CNT/SA/PDMS composite at a same CNT loading. Introducing nano-sized Ni particles on CNT improved the microwave absorption capacity of the composite, while adding CNF on the PDMS matrix enhanced the conductivity of these composites. Under 10% strain, both flexible composites show stable conductivity. Simulation and calculation results shown that increasing the cladding rate of Ni nanoparticles on the surface of CNT, reducing the average size of Ni particles, and increasing the loading of CNF in PDMS matrix can significantly improve conductivity and then EMI performance of the materials. All of these could benefit for the design of flexible electromagnetic shielding composites.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".