Engineering the Cracking Patterns in Stretchable Copper Films Using Acid-Oxidized Poly(dimethylsiloxane) Substrates
Bibliographic record
Abstract
Stretchable conductors are a fundamental part of stretchable and wearable electronic devices. Although the high conductivity of metals makes them the materials of choice, metal films deposited on poly(dimethylsiloxane) (PDMS) elastomers develop destructive channel cracks with strain due to the mechanical mismatch between the metal and elastomer. Engineering how cracks form in metal films under strain is a promising way to control the resistance increase with strain and expand the utility of stretchable metal films to include strain sensors along with electrodes and interconnects. The topography of the PDMS surface dramatically influences the cracking and resistance response in the metal film. However, there is not yet a full understanding of the topography–cracking–resistance relationship to enable researchers to “dial in” a specific resistance response for a particular application. This study presents a fully solution-based approach to crack engineering to provide more insight into this relationship. Oxidizing the surface of a PDMS substrate in a mixture of sulfuric and nitric acids induces the formation of a hierarchical topography, which can be adjusted simply by changing the composition of the acid mixture. The topography influences crack formation with strain in an overlying copper film deposited by electroless metallization. This study reveals that the topography–cracking–resistance relationship is complex and microscale cracking patterns outperform nanoscale cracking patterns to retain the conductivity. The optimal topography is one that generates cracks with microscale interconnections that preserve the conductivity.
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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".