Lignin-based multilayer electrospun mats for absorption-dominant electromagnetic interference shielding
Why this work is in the frame
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Bibliographic record
Abstract
Electromagnetic interference (EMI) has become a growing concern with the proliferation of electronic devices, necessitating sustainable and efficient shielding solutions that surpass conventional reflection-based materials. In this work, we present a bio-derived and novel multilayer nanocomposite EMI shielding system engineered from electrospun lignin/poly(ethylene oxide) (PEO) nanofibers and electrosprayed ethylene glycol-doped poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) coatings with tunable conductivity. The incorporation of Fe 3 O 4 nanoparticles and the introduction of strategic conductivity gradients enabled absorption-dominant shielding by enhancing magnetic and Ohmic losses, impedance matching, and internal scattering mechanisms. A comprehensive rheological study revealed that increasing the PEO content induced a transition from viscous to solid-like behavior, with relaxation times exceeding 50 s, a crucial factor for stable jet formation and uniform fiber morphology. At a lignin-to-PEO ratio of 60/40, the electrospun mats exhibited superior mechanical properties (tensile strength: 4.0 ± 0.8 MPa; tensile modulus: 90.1 ± 8.1 MPa), making them ideal for multilayer fabrication. The final shield, only ∼ 300 µm thick, achieved a remarkable shielding effectiveness of 70 dB and a specific shielding effectiveness (SSE/t) of 5185 dB cm 2 g −1 in the X-band (8.2–12.4 GHz), with an absorbance of 0.66, confirming its absorption-dominant character. This work demonstrates the synergistic use of sustainable materials, nanostructural engineering, and rheological optimization to create lightweight, flexible, and eco-friendly EMI shielding systems. The proposed multilayer design holds significant potential for future applications in aerospace, wearable electronics, and next-generation communication technologies.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 it