Enhancing the rectification effect of hydrogel-based stretchable ionic diodes through incorporating cations with high valence
Bibliographic record
Abstract
The controlled migration of ions in biological systems has inspired the development of ion-based electronics. Ionic diodes, leveraging ions as charge carriers, offer selective control over ion flux, mimicking ion-selective behavior observed in biological systems. Conventional ionic diodes containing fluids encounter challenges in adapting to biological systems due to their limited stretchability and stability. Recent advancements in solid-state ionic diodes based on stretchable gels enable tissue-like stretchability while maintaining diode-like performance. However, their relatively low rectification ratio hinders their electrical performance, necessitating effective strategies to enhance the rectification effect of stretchable ionic diodes. Here, we propose a method to enhance the rectification effect of hydrogel-based stretchable ionic diodes by incorporating high-valence cations into the P-type hydrogel layer. Through neutralization reactions, cations with valences of 1, 2, and 3 were introduced to replace original hydrogen ions in the hydrogel, resulting in a substantial increase in the rectification ratio from 3 to over 70, with an elevated rectification ratio (140) under 100% strain. The enhanced rectification effect enables applications in iontronics, such as ionic rectifiers and bipolar junction transistors (BJTs). This study, for the first time, highlights the potential of improving electrical performances of iontronics through the manipulation of different ion properties.
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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 | 0.000 |
| 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".