Highly Flexible, Stretchable, and Compressible Lignin‐Based Hydrogel Sensors with Frost Resistance for Advanced Bionic Hand Control
Bibliographic record
Abstract
Abstract Bio‐based hydrogels, valued for their flexibility, tunable mechanical properties, and biocompatibility, are promising materials for wearable skins and sensing devices in bionic hand control systems. Lignin, a biopolymer rich in functional groups, can be modified into UV‐curable monomers, enabling the development of 3D‐printed hydrogels via photopolymerization. However, the inherent rigidity of lignin's aromatic rings, coupled with covalent cross‐linking between lignin and other monomers, often limits the hydrogel's stretchability (poor strain) and compressibility. Additional challenges, including poor moisture retention and freeze resistance, further hinder their wider application. In this study, a lignin‐based hydrogel is developed with high flexibility, tensile strain (≥350%), compressive strain (≈95%), and fatigue resistance (up to 10 000 cycles under 50% strain, and 200–800 cycles under 95% compressive strain), which is achieved by incorporating glycerol and lithium chloride to facilitate dynamic hydrogen and lithium ion bonds, while accordingly reducing covalent cross‐linking sites between monomers. The enhanced moisture retention and freeze resistance of hydrogels allow effective sensing performance at −40±1 °C. Afterward, using 3D printing technology, wearable tensile strain sensors and ripple‐shaped 3 × 3 Matrix hydrogel pressure sensors are fabricated, which demonstrated uniform stress distribution and improved performance in controlling complex bionic hand movements, underscoring their application in advancing human–machine interfaces.
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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".