Muscle‐Inspired Robust Anisotropic Cellulose Conductive Hydrogel for Multidirectional Strain Sensors and Implantable Bioelectronics
Bibliographic record
Abstract
Abstract Integrating superior mechanical performance, anisotropic conductivity, and biocompatibility into conductive hydrogels as all‐in‐one human‐machine interaction device remains challenging. Herein, by mimicking the anisotropic structures of human muscles, a robust anisotropic conductive hydrogel is developed by initially aligning polyvinyl alcohol with polypyrrole decorated cellulose nanofibrils to form an anisotropically oriented polymer networks, followed by post‐crosslinking with tannic acid (TA). Introducing TA into hydrogel network permanently secures its hierarchically anisotropic structure through multiple hydrogen bonds, thus endowing the hydrogel with exceptional mechanical properties (tensile strength of 11.41 MPa, toughness of 12.44 MJ m − 3 ), anisotropic adhesive property, and direction‐dependent conductivity. With these attributes, a hydrogel strain sensor with excellent multidirectional sensitivity is developed, enabling stable monitoring of multi‐degrees of freedom joint movements in the human body and facilitating the control of a multiaxial virtual robot manipulator. Moreover, the in vitro/vivo tests demonstrate exceptional biocompatibility and anti‐biofouling properties of the as‐prepared hydrogel sensor, maintaining stable electronic response signals for over 14 days after successful implantation into the Achilles tendon of mice. Overall, this study presents a promising approach for designing conductive hydrogels with superior mechanical properties and anisotropic functionality for emerging applications in both in vitro and in vivo human‐machine interface materials.
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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) | 0.001 | 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".