Conductive Bio‐based Hydrogel for Wearable Electrodes via Direct Ink Writing on Skin
Bibliographic record
Abstract
Abstract Non‐invasive electrodes for recording and delivering electric signals to the human body are crucial for health monitoring and rehabilitation applications. However, high‐fidelity signal recording or delivery with epidermal electrodes remains a challenge due to the need for shape customization, to account for the variance of body morphology among individuals, and the need for conformal contact, to accommodate creviced skin surfaces, intricate curves, and moving bodies. In this study, a conductive and self‐adhesive hydrogel for direct ink writing of wearable electrodes on the skin is presented, utilizing physical cross‐linking mechanisms between bio‐based polymers. With a fast gelation time and a facile fabrication method, the printed hydrogel achieves a 0.40 mm resolution via handheld 3D printers. Compared with silver/silver chloride (Ag/AgCl) coated gel electrode standards, the hydrogel electrode formed in situ achieves a higher signal‐to‐noise ratio by 88%, for the monitoring of forearm muscle biopotential and decreases the required current from 3.5 to 2.25 mA, for the functional electrical stimulation for eye closure. The lowered contact impedance of the hydrogel electrode is attributed to its sol–gel transition in situ on the skin, demonstrating its potential to enable future healthcare applications with improved personalization, efficiency, and comfort.
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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) | 0.002 | 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".