Soft and Mechanically Robust Electromagnetic Interference Shielding Material in Polypropylene Reinforced by Aligned Steel Fibers and Crystalline Structures
Bibliographic record
Abstract
With growing demands arising from technology development for highly durable and easily moldable materials in flexible electronic devices, electromagnetic interference (EMI) materials are gradually shifting from rigid metals to soft polymer composites. Current flexible EMI shielded materials with high electrical conductivity have limited mechanical robustness owing to unstable filler–polymer structures and filler agglomerations. This work prepares highly conductive EMI shielding materials from steel fibers (SFs) and polypropylene (PP) composites using extrusion preprocessing and postprocessing methods, such as compression molding and injection molding. The extrusion process provides highly crystalline SSF/PP composites with mechanical strength at break of up to 30 MPa and a high elastic modulus of up to 2.5 GPa. Extrusion followed by injection processing forms an aligned structure of SSF and creates β crystalline phases inside the PP matrix. The SSF/PP composites prepared by the extrusion-injection process demonstrated flexibility and bending to 180° with full recoverability and strain to failure of 250% for 0.2–1 vol % and 80% for 2 vol % SSF. 2 vol % SSF/PP composites exhibit a high out-of-plane electrical conductivity of 0.25 S cm –1, an in-plane electrical conductivity of 12 S cm –1, an EMI shielding effectiveness of 25 dB, and a high impact strength of 4.7 kJ m –2 . The study provides a facile, scalable, and solvent-free strategy for high-performance, soft, and durable EMI shielding materials with great potential for flexible and portable electronics.
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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".