Stretchable thin film inductors for wireless sensing in wearable electronic devices
Bibliographic record
Abstract
Abstract The unique soft and elastic nature of stretchable electronics has potential to advance wearable devices as human-machine interfaces. The integration of wireless power and data communication technology into stretchable electronics, which could be realised by inductive coupling and oscillator circuits, is key to achieve continuous monitoring of body signals with minimally invasive devices. As one of the main components for inductive coupling and oscillator circuits, the development of stretchable inductors is therefore compelling. The most common strategy to fabricate stretchable inductors is to add periodic waves to a spiral conductor, which provides mechanical robustness but inevitably increases resistance. In this work, we introduce a method to fabricate stretchable inductors, which relies on creating a wrinkled thin film inductor on a polystyrene substrate, functionalizing the inductor surface with an adhesive layer, and then transferring the structure onto a polydimethylsiloxane (PDMS) elastomer. Contrary to inductors created through the addition of periodic wave patterns, the wrinkled inductor features low resistance while providing high stretchability. The wrinkled inductors fabricated using this approach exhibited 30% decrease in resistance compared to their flat counterparts of the same size and geometry. Resistance and inductance under uniaxial stretching remained unchanged up to 45% strain, revealing exceptional electrical and mechanical stability. The strong chemical bonding between the functionalized wrinkled inductor and the PDMS elastomer contributes to the robustness and long-term stability of the device. This method provides an added advantage of miniaturization of the stretchable inductor, as it is shrunk to 16% of its original size during the wrinkling process. This technology has potential for building high performance stretchable inductors for stretchable wireless electronic devices and can eventually benefit the design of electronics for implants, health care monitoring and wearable communication.
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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 | 0.001 |
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".