Cellulose Nanofibrils Enhanced, Strong, Stretchable, Freezing‐Tolerant Ionic Conductive Organohydrogel for Multi‐Functional Sensors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it