Multi-layered rubber-based nanocomposites for absorption-dominant EMI shielding and adaptive infrared camouflage
Bibliographic record
Abstract
Designing cutting-edge flexible electronics that seamlessly integrate into the infrared (IR) spectrum while providing absorption-dominated EMI shielding remains a formidable challenge. This requires a delicate balance between impedance matching for high electromagnetic wave (EMW) absorption, optimal electrical conductivity for efficient EMI shielding, and precise IR reflection control for effective camouflage, a synergy barely achieved in a single nanocomposite. This study introduces a novel approach, pioneering a step-by-step engineered distribution of carbon nanotubes (CNTs) and pyrolyzed magnetic metal-organic frameworks (MMOFs) within a multi-layered styrene-butadiene rubber (SBR) system. By increasing the CNT content gradient while reducing the MMOF content gradient across the layers, this approach unlocks a unique balance among impedance matching, electrical conductivity, and IR emissivity. The optimized 1-mm-thick nanocomposite achieved a shielding effectiveness of 54 dB with an absorption coefficient of 0.70, demonstrating the successful development of an absorption-dominant EMI shield capable of blocking 99.9996 % of incoming EMWs. It's remarkable that Joule heating capability also allows it to rapidly reach a steady-state temperature of 125 °C within 84 s under a 10 V driving voltage. This electro-thermal energy conversion ability complements IR camouflage, as the optimized nanocomposite can dynamically modulate IR radiation in response to fluctuating environmental temperatures, ensuring adaptive IR camouflage under varying conditions. Mechanically, the developed nanocomposite offers exceptional flexibility and durability, with an elongation at break of 134 % and a maximum tensile stress of 11.5 MPa. This multifunctional multi-layered nanocomposite integrates high EMW absorption, efficient EMI shielding, and IR camouflage, making it ideal for next-generation flexible electronics. • The asymmetric nanocomposite shows 54 dB EMI shielding with 70 % absorption. • EMI shielding and absorption are retained after 500 mechanical deformation cycles. • Asymmetric nanocomposite shows adaptive IR camouflage via Joule heating. • Flexible EMI shield exhibits 134 % elongation and a tensile strength of 11.5 MPa.
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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) | 0.001 | 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".