Low‐Hysteresis Cellulose‐Based Hydrogels for Strain Detecting
Bibliographic record
Abstract
Hydrogels are promising materials for wearable and flexible electronics, yet combining low mechanical hysteresis with high renewable content remains a key challenge. Here, we report a cellulose-based hydrogel with low hysteresis, enabled by incorporating dialcohol nanocellulose (DANC) into a polyacrylamide (PAAM) network. The flexible, hydroxyl-rich DANC chains form abundant reversible hydrogen bonds with the PAAM matrix, allowing the hydrogel to achieve an unprecedented cellulose content of ∼15 wt.% while maintaining stretchability and mechanical robustness. The PAAM/DANC hydrogels display low mechanical hysteresis and high durability during 1000 cyclic strains, with stable mechanical and sensing performance. In addition, the hydrogels exhibit reliable strain sensitivity with a gauge factor of 1.1 and consistent signal output under varying strains. Finally, we demonstrate their potential in wearable strain sensing by detecting complex human motions. This work presents a sustainable strategy to design high-performance cellulose-based hydrogels, advancing their application in next-generation wearable electronics.
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.
How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".