Mussel-Inspired Nanocomposite Hydrogel-Based Electrodes with Reusable and Injectable Properties for Human Electrophysiological Signals Detection
Bibliographic record
Abstract
Recently, ion-conducting hydrogels have received much attention in the preparation of noninvasive electronics. However, there have been few studies on the simultaneous integration of multiproperties of hydrogels to meet the actual needs of flexible electrodes. Herein, we prepared mussel-inspired proanthocyanins (PC)-coated cellulose nanofibrils (CNF) nanocomposites, which were dispersed in the guar gum (GG) and glycerol solution to prepare a PC-CNF-GG-glycerol hydrogel. This hydrogel exhibits great adhesion (7.9 KPa) and UV-blocking ability (82%). Moreover, the borax solution used as a cross-linker also imparts ion-conducting property to the hydrogel, and the strain sensor fabricated by the hydrogel exhibits low-weight detection ability (200 mg) and fast response speed (33 ms). The adhesive, conductive, and injectable PC-CNF-GG-glycerol hydrogel can also be used for preparing wearable, portable, and editable electrodes. The new electrode can accurately detect human electrophysiological signals. Interestingly, the hydrogel electrode also has advantages of reusability. In summary, this ion-conducting hydrogel has the potential to become a new generation of portable and biofriendly bioelectrodes, sensors, and dressings.
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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) | 0.000 | 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".