Direct Ink Writing of Flexible Conductive Polymer Aerogels with Exceptional Electromagnetic Shielding Efficiency
Bibliographic record
Abstract
In this work, a strategy for synthesizing highly conductive poly(3,4-ethylenedioxythiophene) polystyrenesulfonate (PEDOT:PSS) aerogel films through additive manufacturing, specifically using the direct ink writing (DIW) method, is demonstrated. This approach marks the first application of pure PEDOT:PSS aerogels in electromagnetic interference (EMI) shielding, employing PEDOT:PSS as the sole matrix material without additives. The synthesis enabled by DIW achieves aerogels with over 90% porosity and an electrical conductivity surpassing 10 S cm –1 . The films exhibit outstanding EMI shielding effectiveness in the X and Ku bands, achieving shielding effectiveness above 100 dB and specific shielding effectiveness approaching 15,000 dB cm 2 g –1 . The aerogels also show remarkable environmental resilience, maintaining their conductive properties after extensive exposure to high humidity, multiple water soak and dry cycles, and temperatures up to 200 °C without degradation in shielding efficiency. Additionally, by modifying the hydrogel precursor formulations, the aerogels’ mechanical flexibility is enhanced with minimal impact on their EMI shielding performance. This research not only underscores the capabilities of PEDOT:PSS aerogels as a viable material for EMI shielding but also highlights the critical role of additive manufacturing via DIW in developing adaptable and robust shielding solutions for diverse environmental conditions.
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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".