Efficient Multi-Material Structured Thin Film Transfer to Elastomers for Stretchable Electronic Devices
Bibliographic record
Abstract
Stretchable electronic devices must conform to curved surfaces and display highly reproducible and predictable performance over a range of mechanical deformations. Mechanical resilience in stretchable devices arises from the inherent robustness and stretchability of each component, as well as from good adhesive contact between functional and structural components. In this work, we combine bench-top thin film structuring with solvent assisted lift-off transfer to produce flexible and stretchable multi-material thin film devices. Patterned wrinkled thin films made of gold (Au), silicon dioxide (SiO2), or indium tin oxide (ITO) were produced through thermal shrinking of pre-stressed polystyrene (PS) substrates. The wrinkled films were then transferred from the PS to poly(dimethylsiloxane) (PDMS) substrates through covalent bonding and solvent-assisted dissolution of the PS. Using this approach, different materials and hybrid structures could be lifted off simultaneously from the PS, simplifying the fabrication of multi-material stretchable thin film devices. As proof-of-concept, we used this structuring and transfer method to fabricate flexible and stretchable thin film heaters. Their characterization at a variety of applied voltages and under cyclic tensile strain showed highly reproducible heating performance. We anticipate this fabrication method can aid in the development of flexible and stretchable electronic devices.
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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) | 0.001 | 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 | 0.001 |
| 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".