Silicone Phase Behavior Resolves the Softness–Surface Functionality Trade-Off in Emerging Stretchable Electronics
Bibliographic record
Abstract
The development of skin-like stretchable electronics is constrained by a persistent trade-off: ultrasoft elastomers such as Ecoflex and Dragon Skin offer mechanical compliance but resist surface modification, whereas stiffer silicones like polydimethylsiloxane (PDMS) enable plasma oxidation, surface modification, and device integration at the expense of stretchability. Here, we show that this trade-off originates from the phase behavior of low-molecular-weight species that soften the elastomer but also migrate to the surface, where they interfere with plasma oxidation and inhibit functionalization. Although removing these additives by solvent extraction typically sacrifices softness for surface functionality, we show that the commercial platinum-cured silicone elastomer Mold Star surprisingly becomes tougher and more extensible upon their removal, with dramatically improved surface reactivity. Mechanical testing, contact angle measurements, and atomic force microscopy demonstrate that native Mold Star contains a dispersed phase of low-molecular-weight species that weakens the network and passivates the surface. Extraction removes this phase, exposes the intrinsic nanoscale surface morphology, and enables robust plasma-induced modification and metal adhesion. These changes fundamentally alter interfacial performance: e-beam gold delaminates and electroless plating fails on native Mold Star, whereas extracted Mold Star supports adherent evaporated films and electroless Ni/Au coatings that remain conductive up to 80% strain. These findings establish that controlling the phase behavior of low-molecular-weight species is a powerful design principle for uniting softness and surface functionality in silicone elastomers, enabling the next generation of electronic skins and bio-integrated 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) | 0.000 | 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 | 0.001 |
| 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".