Lignin derived hydrogel with highly adhesive for flexible strain sensors
Bibliographic record
Abstract
The application of natural polymers to hydrogel materials with stretchable and compressible properties has attracted more and more attention. However, hydrogel materials made of pure natural polymers are not only poor in mechanical properties, but also lack in stability and sensitivity in strain sensors. Herein, the ionic conductive lignin hydrogels with highly stretchable (tensile strain ∼525.1%) and compressible (compression strain ∼95%) performance were formulated by a simple solution blending method. The lignin-based hydrogel with ultra-self-adhesive properties was able to adhere to various hydrophobic or hydrophilic surfaces. The adhesion measured on stainless steel, plexiglass, and paper reached 307 kPa, 301 kPa, and 174 kPa, respectively. Moreover, lignin-based hydrogels can be used as reliable and stable strain sensors to respond to environmental stimuli. Good adhesion can make hydrogels closer to the skin, so as to more accurately detect human signals, and excellent ion conduction ability can meet the needs of monitoring wrist bending activities. Significantly, the various properties of lignin-based hydrogel can be controlled through rationally adjusting the chemical composition of the hydrogel. It was proved that lignin-based hydrogel with natural-based formulation, high mechanical properties, and adhesion performance has great application potential in flexible equipment.
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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".