Intrinsically Conductive Liquid Metal‐Elastomer Composites for Stretchable and Flexible Electronics
Bibliographic record
Abstract
Abstract Liquid metal‐embedded elastomers (LMEEs) are a class of deformable composites made of particles of liquid metal dispersed in an elastomeric matrix. Although these composites possess high thermal conductivity, they are not intrinsically electrically conductive unless a stimulus is applied to join the liquid metal inclusions into a conductive pathway. LMEEs with intrinsic conductivity, especially with a conductive surface, have great potential uses in flexible and stretchable electronics as soft, nondamaging contacts for device characterization, stretchable interconnects for deformable circuits, and as a “soft solder” to electrically connect devices to flexible and stretchable substrates. Here, a simple process is introduced to fabricate intrinsically conductive LMEEs (iLMEEs) with conductive surfaces through the sedimentation of microparticles of eutectic gallium‐indium alloy (EGaIn) in the elastomer poly(dimethylsiloxane). During this sedimentation process, an EGaIn‐rich 3D percolation network forms at the bottom surface. The resulting iLMEE possesses a conductive surface comprising a mosaic of EGaIn particles embedded in PDMS, with a low sheet resistance of 0.63 ± 0.04 Ω sq –1 . iLMEE is soft, stretchable, and exhibits stable conductivity to 100% strain. We demonstrate the use of iLMEE as nondamaging, reusable soft electrical contact probes and as mechanically robust electrical connections between light‐emitting devices and flexible plastic substrates.
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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 | 0.000 |
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".