Filler-Free Conducting Polymers as a New Class of Transparent Electromagnetic Interference Shields
Bibliographic record
Abstract
Transparent electromagnetic interference (EMI) shields are increasingly in demand for medical, military, wireless networks, aerospace electronics, and navigation control systems. To date, researchers have mixed pristine and/or doped conductive polymers with carbon allotropes and metallic fillers to increase the total shielding effectiveness, compromising the transparency, amount of the materials used, and weight of the shields. Obtaining cost-effective and transparent EMI shields without the need to incorporate fillers is extremely desirable. Herein, we implement a design strategy for fabricating a gigahertz (GHz) highly transparent shield made of poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS). The total EMI shielding effectiveness of 15 dB is achieved in the X-band frequency range for a 50 nm ultrathin film with a high transparency of 97.1%. The fabricated filler-free EMI shield holds a record thickness-specific shielding figure-of-merit of 300 dB μm –1 —far exceeding the best values for micron-thick silver-, carbon-, and MXene-based composite material shields—with even a higher transparency. The feasibility of the developed filler-free shield for large-scale applications is validated by its integration into a cell phone display glass, as a prototype, in which the EMI shielding effectiveness elevates to 18.3 dB.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
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".