Rational Assembly of Liquid Metal/Elastomer Lattice Conductors for High‐Performance and Strain‐Invariant Stretchable Electronics
Bibliographic record
Abstract
Abstract Highly stretchable and conductive composites have gained tremendous research interests due to the imperative demands in the fields of stretchable electronics and soft robotics. However, it is challenging to maintain the original performance of the composites under complex external deformations. Here, a one‐step dual‐material 3D printing technique is developed to rationally assemble liquid metal (LM) into an elastomer lattice. The 3D interconnected and deformable liquid conductive network is supported by a highly ordered and robust polydimethylsiloxane lattice skeleton, yielding the resultant composites high electrical conductivity (1.98 × 10 6 S m −1 ), stretchability (180%), and electromagnetic interference (EMI) shielding effectiveness (72 dB). Unlike those composites with dispersed fillers, the LM/elastomer lattice composites deliver negligible electromechanical coupling, showing a negative resistance change of only − 2% at a large tensile strain of 100%. The composites also exhibit strain‐invariant EMI shielding performance in a strain range of 0–100%, and present exceptional stability over 1000 rigorous cycles of stretching and releasing. The applications of the composites in flexible display circuits, microwave shielding layer, and EMI shields in wireless power transmission systems are demonstrated. The current findings suggest an effective strategy for fabricating LM‐based composites with precisely controlled and unprecedented multi‐functionality.
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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".