Achieving Ultra‐High Electromagnetic Wave Absorption of Lightweight and Flexible Polyamide Composites via Customizing Microcellular Architecture
Bibliographic record
Abstract
Abstract Porous conductive polymer composites (CPCs) have been proven to be potential electromagnetic wave (EMW) absorbers. However, challenges persist regarding the inferior absorption capacity and limited EMW attenuation mechanisms. Here, an eco‐friendly, scalable, and versatile route to fabricate lightweight and flexible microcellular foamed polyamide 6 (PA6)/carbon nanotube (CNT) nanocomposites with customized cellular structure and ultra‐high EMW absorption capacity via supercritical CO 2 foaming is proposed. The unique porous structure is verified to endow composite absorbents with good impedance matching and strong loss capacity simultaneously owning to their tunable dielectric properties and abundant interfaces. Moreover, the effects of CNT content and tailored microcellular architecture (i.e. varied void fraction under similar cell size, and varied cell size under similar void fraction) on the EMW absorbing performance are systematically investigated. Benefiting from the structural merits, the composite foam with void fraction of 44.1% and cell size of 21.7 µm delivers the ultra‐low reflection loss ( RL ) of −71.8 dB at a small thickness of 4.0 mm, demonstrating superior EMW absorption performance compared with vast majority of foamed CPCs. Subsequently, the Computer Simulation Technology (CST) simulation is performed to visualize the structural advantages of absorbers with varied cell size from the micro and macro perspective, and reveal the EMW attenuation evolutionary mechanism. The composite foam also possesses excellent mechanical and hydrophobic properties. By manipulating the microcellular architecture, this work paves a novel path toward developing lightweight, waterproofing, and high‐performance CPCs‐based absorbers.
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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 | 0.000 | 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 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".