Stretchable electronics for wearable and high-current applications
Bibliographic record
Abstract
Advances in the development of novel materials and fabrication processes are resulting in an increased number of flexible and stretchable electronics applications. This evolving technology enables new devices that are not readily fabricated using traditional silicon processes, and has the potential to transform many industries, including personalized healthcare, consumer electronics, and communication. Fabrication of stretchable devices is typically achieved through the use of stretchable polymer-based conductors, or more rigid conductors, such as metals, with patterned geometries that can accommodate stretching. Although the application space for stretchable electronics is extensive, the practicality of these devices can be severely limited by power consumption and cost. Moreover, strict process flows can impede innovation that would otherwise enable new applications. In an effort to overcome these impediments, we present two modified approaches and applications based on a newly developed process for stretchable and flexible electronics fabrication. This includes the development of a metallization pattern stamping process allowing for 1) stretchable interconnects to be directly integrated with stretchable/wearable fabrics, and 2) a process variation enabling aligned multi-layer devices with integrated ferromagnetic nanocomposite polymer components enabling a fully-flexible electromagnetic microactuator for large-magnitude magnetic field generation. The wearable interconnects are measured, showing high conductivity, and can accommodate over 20% strain before experiencing conductive failure. The electromagnetic actuators have been fabricated and initial measurements show well-aligned, highly conductive, isolated metal layers. These two applications demonstrate the versatility of the newly developed process and suggest potential for its furthered use in stretchable electronics and MEMS applications.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".