Recent advances in designing conductive hydrogels for flexible electronics
Bibliographic record
Abstract
Abstract Flexible electronics have emerged as an exciting research area in recent years, serving as ideal interfaces bridging biological systems and conventional electronic devices. Flexible electronics can not only collect physiological signals for human health monitoring but also enrich our daily life with multifunctional smart materials and devices. Conductive hydrogels (CHs) have become promising candidates for the fabrication of flexible electronics owing to their biocompatibility, adjustable mechanical flexibility, good conductivity, and multiple stimuli‐responsive properties. To achieve on‐demand mechanical properties such as stretchability, compressibility, and elasticity, the rational design of polymer networks via modulating chemical and physical intermolecular interactions is required. Moreover, the type of conductive components (eg, electron‐conductive materials, ions) and the incorporation method also play an important role in the conductivity of CHs. Electron‐CHs usually possess excellent conductivity, while ion‐CHs are generally transparent and can generate ion gradients within the hydrogel matrices. This mini review focuses on the recent advances in the design of CHs, introducing various design strategies for electron‐CHs and ion‐CHs employed in flexible electronics and highlighting their versatile applications such as biosensors, batteries, supercapacitors, nanogenerators, actuators, touch panels, and displays. image
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".