Lightweight Polypropylene/Stainless-Steel Fiber Composite Foams with Low Percolation for Efficient Electromagnetic Interference Shielding
Bibliographic record
Abstract
Lightweight polypropylene/stainless-steel fiber (PP-SSF) composites with 15-35% density reduction were fabricated using foam injection molding. The electrical percolation threshold, through-plane electrical conductivity, and electromagnetic interference (EMI) shielding effectiveness (SE) of the PP-SSF composite foams were characterized and compared against the solid counterparts. With 3 wt % CO2 dissolved in PP as a temporary plasticizer and lubricant, the fiber breakage was significantly decreased during injection molding, and well-dispersed fibers with unprecedentedly large aspect ratios of over 100 were achieved. The percolation threshold was dramatically decreased from 0.85 to 0.21 vol %, accounting for 75% reduction, which is highly superior, compared to 28% reduction of the previous PP-carbon fiber composite foam.1 Unlike the case of carbon fiber,1 SSFs were much longer than the cell size, and the percolation threshold reduction of PP-SSF composite foams was thus primarily governed by the decreased fiber breakage instead of fiber orientation. The specific EMI SE was also significantly enhanced. A maximum specific EMI SE of 75 dB·g(-1)·cm(3) was achieved in PP-1.1 vol % SSF composite foams, which was much higher than that of the solid counterpart. Also, the relationships between the microstructure and properties were discussed. The mechanism of EMI shielding enhancement was also studied.
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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) | 0.001 | 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".