Visually Imperceptible Liquid‐Metal Circuits for Transparent, Stretchable Electronics with Direct Laser Writing
Bibliographic record
Abstract
Ω m) that are soft, elastic (strain limit >100%), and optically transparent. The films are composed of a grid-like array of visually imperceptible liquid-metal (LM) lines on a clear elastomer. Unlike previous efforts in transparent LM circuitry, the current approach enables fully imperceptible electronics that have not only high optical transmittance (>85% at 550 nm) but are also invisible under typical lighting conditions and reading distances. This unique combination of properties is enabled with a laser writing technique that results in LM grid patterns with a line width and pitch as small as 4.5 and 100 µm, respectively-yielding grid-like wiring that has adequate conductivity for digital functionality but is also well below the threshold for visual perception. The electrical, mechanical, electromechanical, and optomechanical properties of the films are characterized and it is found that high conductivity and transparency are preserved at tensile strains of ≈100%. To demonstrate their effectiveness for emerging applications in transparent displays and sensing electronics, the material architecture is incorporated into a couple of illustrative use cases related to chemical hazard warning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 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".