A Stretchable Tactile Sensor Array Based on Hydrogel Ionic Diodes
Bibliographic record
Abstract
Controlled ion migration, as seen in biological systems, has inspired the development of ionic diodes – a type of iontronics capable of controlling ion flux. However, existing ionic diodes face challenges in adapting to biological systems due to the lack of device-level integration that enables monolithic and stretchable ionic diode arrays. In this work, we propose a stretchable hydrogel diode-based sensor array that overcomes these challenges by integrating multiple ionic diodes into a single monolithic array. By doping oppositely charged polyelectrolytes into the hydrogel matrix, we create a diode-like hydrogel sensor based on ion mobility, capable of producing four distinct signal outputs - resistance, capacitance, open-circuit voltage, and short-circuit current - in response to mechanical stimuli. Through the shared double-network polymers, the hydrogel diodes and insulators are interlocked at the molecular level, enabling the integrated array excellent stretchability (over 100% strain), transparency, and spatial sensing. This design not only achieves seamless integration of multiple ionic diodes, but also offers self-powered, bio-inspired sensing capabilities. Furthermore, we demonstrate its potential in human-machine interactions, showcasing applications for robotics and prosthetics. This work represents the first successful integration of ionic diodes into a stretchable monolithic array, offering a new approach for the development of bio-inspired systems with enhanced adaptability and multi-functional sensing capabilities.
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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 | 0.001 | 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".