Direct 3D Printing of Stretchable Circuits via Liquid Metal Co‐Extrusion Within Thermoplastic Filaments
Bibliographic record
Abstract
Liquid‐metal alloys are now extensively used for stretchable electronic applications due to their superior electrical conductivity, non‐toxicity, and mechanical stability in micro‐channels. Needle‐injection and direct‐writing are the most popular techniques for patterning micro‐structured liquid metal alloys. However, embedded channels often require a very high pressure to inject liquid‐metal, and direct writing by dispensing is relatively complex due to the low viscosities and high surface tension of the metal which cause liquid to normally dispense in droplets rather than a stream. In this work, a technique to co‐axially extrude liquid‐metal alloy within an encapsulating cover fluid has been presented to obtain a continuous stable stream of liquid‐metal. Fused deposition modeling (FDM) 3D printing has been adapted to co‐extrude a liquid‐metal core with a shell made from a thermoplastic elastomer. A custom extruder system is used to directly produce conductive micro‐wires (diameter: ≈25 μm) of liquid‐metal having an insulating shell of styrene‐ethylene‐butylene‐styrene which can be stretched up to four times the original length without any noticeable mechanical and electrical loss. The system is capable of printing in‐plane conductive pathways as well as out‐of‐plane functional devices with direct‐stable encapsulation of liquid‐metal wires. This technology has been successfully used to print 2D‐pressure and 3D‐strain sensors.
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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".