Monolithic Dual‐Material 3D Printing of Ionic Skins with Long‐Term Performance Stability
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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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 | 0.001 |
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".