Electrospinning for electromagnetic interference shielding: Principles, challenges, and future directions
Bibliographic record
Abstract
Electrospinning is an electrohydrodynamic process in which a liquid droplet is electrified to generate a charged jet that undergoes stretching and elongation to form fibers. This technique is widely recognized for fabricating nonwoven wearable textiles, with promising applications in electromagnetic interference (EMI) shielding for healthcare and military systems. Effective EMI shields depend largely on electrical conductivity; however, electrospinning faces significant challenges when processing conductive materials due to excessive charge dissipation, jet instability, and unintended electrospraying instead of fiber formation. Here, we critically examine these challenges to elucidate the relationship between electrical conductivity and electrospinnability, identifying key bottlenecks in the field. Additionally, the recent progress in transitioning from reflection-based electrospun EMI shields to absorption-dominant ones is discussed in detail. Finally, we outline future directions that include strategies for absorption-dominant shielding, highlight the synergistic potential of electrospinning and electrospraying for scalable production, and advocate for the integration of machine learning tools to accelerate the design of next-generation EMI shielding materials. This review aims to bridge the gap between fundamental research and real-world applications, addressing critical challenges and paving the way toward high-performance, wearable EMI shielding 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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 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".