A Self-Assembled, Low-Cost, Microstructured Layer for Extremely Stretchable Gold Films
Bibliographic record
Abstract
We demonstrate a simple, low-cost, and green approach to deposit a microstructured coating on the silicone elastomer polydimethylsiloxane (PDMS) that can be coated with gold to produce highly stretchable and conductive films. The microstructured coating is fabricated using an aqueous emulsion of poly(vinyl acetate) (PVAc): common, commercially available white glue. The aqueous glue emulsion self-assembles on the PDMS surface to generate clustered PVAc globules, which can be conformally coated with gold. The microstructured surface provides numerous defect sites that localize strain when the structure is stretched, resulting in the initiation of numerous microcracks. As the structure is further elongated, the microcracks interact with one another, preventing long-range crack propagation and thus preserving the conduction pathway. The resistance of PDMS/glue/gold structures remains remarkably low (23 times the initial resistance) up to 65% elongation, making these structure useful as stretchable interconnects. Decreasing the concentration of the PVAc aqueous emulsion reduces the density of defect sites of the microstructure, which increases the change in resistance of the gold films with stretching. In this way, we can tune the resistance changes of the PDMS/glue/gold structures and increase their sensitivity to strain. We demonstrate the use of these structures as wearable, soft 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 | 0.001 | 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".