Cellulose Nanofibrils Enhanced, Strong, Stretchable, Freezing‐Tolerant Ionic Conductive Organohydrogel for Multi‐Functional Sensors
Bibliographic record
Abstract
Abstract To date, ionic conducting hydrogel attracts tremendous attention as an alternative to the conventional rigid metallic conductors in fabricating flexible devices, owing to their intrinsic characteristics. However, simultaneous realization of high stiffness, toughness, ionic conductivity, and freezing tolerance through a simple approach is still a challenge. Here, a novel highly stretchable (up to 660%), strong (up to 2.1 MPa), tough (5.25 MJ m −3 ), and transparent (up to 90%) ionic conductive (3.2 S m −1 ) organohydrogel is facilely fabricated, through sol–gel transition of polyvinyl alcohol and cellulose nanofibrils (CNFs) in dimethyl sulfoxide‐water solvent system. The ionic conductive organohydrogel presents superior freezing tolerance, remaining flexible and conductive (1.1 S m −1 ) even at −70 °C, as compared to the other reported anti‐freezing ionic conductive (organo)hydrogel. Notably, this material design demonstrates synergistic effect of CNFs in boosting both mechanical properties and ionic conductivity, tackling a long‐standing dilemma among strength, toughness, and ionic conductivity for the ionic conducting hydrogel. In addition, the organohydrogel displays high sensitivity toward both tensile and compressive deformation and based on which multi‐functional sensors are assembled to detect human body movement with high sensitivity, stability, and durability. This novel organohydrogel is envisioned to function as a versatile platform for multi‐functional sensors in the future.
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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".