Direct-Write Printed Wearable Metasurfaces
Bibliographic record
Abstract
Direct-write printing was utilized in fabricating a wearable metasurface for frequency filtering at 6 GHz. Using radio frequency (RF) performance and sheet resistance, Rs as performance metrics, the printed metasurfaces were subjected to wash cycle and longevity testing. The metasurface was printed on two woven nylon industrial fabrics (70D (EX) and 200D (BW) thread masses) featuring a polyurethane (PU) coating on one side. Two conductive inks, CM127-48 (Creative Materials) and PE876 (Dupont) were printed on the fabrics and tested for Rs , RF performance, wash cycle stability, and longevity. Rs values for CM127-48 on EX and BW were similar (0.083±0.013 and 0.088±0.031 ohm/sq, respectively), while PE876 had higher Rs , on BW than EX (0.080±0.015 and 0.35±0.06 ohm/s). RF performance revealed that S21 magnitudes were at least −30 dB for all four ink–fabric combinations, in agreement with simulations. Wash cycle testing of PE876 on EX and BW revealed a comparable decrease in S21 magnitude for both fabrics. However, BW samples exhibited minimal shifts in peak frequency. Longevity tests on the four ink–fabric combinations demonstrated that S21 magnitudes were at least −29.5 dB, with the smallest change in magnitude for the CM127-48–EX sample. Materials selection may be tailored for various design goals in wearable electronics.
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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) | 0.002 | 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".