Monolithic Dual‐Material 3D Printing of Ionic Skins with Long‐Term Performance Stability
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Artificial “ionic skin” is of great interest for mimicking the functionality of human skin, such as subtle pressure sensing. However, the development of ionic skin is hindered by the strict requirements of device integration and the need for devices with satisfactory performance. Here, a dual‐material printing strategy for ionic skin fabrication to eliminate signal drift and performance degradation during long‐term use is proposed, while endowing the ionic skins with high sensitivity by 3D printing of ionic hydrogel electrodes with microstructures. The ionic skins are fabricated by alternative digital light processing 3D printing of two photocurable precursors: hydrogel and water‐dilutable polyurethane acrylate (WPUA), in which the ionically conductive hydrogel layers serve as soft, transparent electrodes and the electrically insulated WPUA as flexible, transparent dielectric layers. This novel dual‐material printing strategy enables strong chemical bonding between the hydrogel and the WPUA, endowing the device with designed characteristics. The resulting device has high sensitivity, minimal hysteresis, a response time in the millisecond range, and excellent repetition durability for pressure sensing. The results demonstrate the potential of the dual‐material 3D printing strategy as a pathway to realize highly stable and high‐performance ionic skin fabrication to monitor human physiological signals and human–machine interactions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it