Lignin-based multilayer electrospun mats for absorption-dominant electromagnetic interference shielding
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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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) | 0.000 | 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".