3D‐Printed Stacked Ionic Assemblies for Iontronic Touch Sensors
Bibliographic record
Abstract
Abstract Sensing is the process of detecting and monitoring any physico‐chemical environmental parameters. Herein, new self‐powered iontronic sensors, which utilize touch‐induced ionic charge separation in ionically conductive hydrogels, are introduced for potential use in object mapping, recognition, and localization. This is accomplished using high‐resolution stereolithography (SLA) 3D printing of stacked ionic assemblies consisting of discrete compartments having different ion transport properties. The latter assemblies readily allow programming the output voltage magnitude and polarity by means of variations in ion type, charge density, and cross‐linking density within the iontronic device. Voltages of up to 70 mV are generated on application of compressive strains of as much as 50% (≈22.5 kPa), with the magnitude directly proportional to stress, and the polarity dependent on the sign of the mobile ion. As a proof‐of‐concept demonstration, the resulting touch sensors are integrated on the fingertip to enable the tactile feedback, mimicking the tactile perception of objects for recognition applications. In addition, it is proposed that streaming potential is the underlying mechanism behind the iontronic touch sensors. The electromechanical response is therein consistent with a streaming potential model.
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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".