The Intersection of Computational Design and Wearable‐Optimized Electrospun Structural Nanohybrids for Electromagnetic Absorption
Bibliographic record
Abstract
Abstract By leveraging the principles of electromagnetic theory and materials science, the characteristics of dielectric polymer composites can be optimized, eliminating repetitive trial‐and‐error in their application as electromagnetic absorbers (EMAs). Herein, a systematic framework for optimizing the thickness and composition of double‐layer EMAs is proposed, using a combination of transmission line, Debye relaxation, and Maxwell–Garnett theories. Following theoretical optimization, a double‐layered electrospun EMA is fabricated, which comprises a ≈1.17 mm thick matrix of styrene–butadiene–styrene (SBS) decorated with MXene on its fibrous structure. The second SBS layer, with a thickness of ≈0.52 mm, incorporates a hybrid of MXene and graphene nanoribbons (GNR) as conductive additives. The EMA exhibits durable electrical performance after 2000 tensile cycles, owing to the surface chemistry engineering and the novel in situ assembly technique. It is capable of shielding 99.9% of the incident wave and >80% absorptivity ( A ) over almost the entire K u ‐band. The EMA also exhibits desirable mechanical characteristics, such as >300% stretchability and full twist and wrinkle recoveries, making it an excellent choice for protective attire applications. Additionally, the introduced approach provides solutions for the advancement of tailorable polymer composite EMAs, with respect to specific criteria of the target wave frequency, effective absorption bandwidth, and absorption levels.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".