Preparation of Hemicellulose Nanoparticle-Containing Ionic Hydrogels with High Strength, Self-Healing, and UV Resistance and Their Applications as Strain Sensors and Asymmetric Pressure Sensors
Bibliographic record
Abstract
Smart functional fillers can significantly enhance the comprehensive properties of ionic hydrogels, such as their mechanical properties, which are key features of hydrogels in wearable sensor applications. As a plant-derived natural polymer, hemicellulose can serve as smart functional fillers. In this study, tannic acid-modified hemicellulose nanoparticles (TA@HC) and Fe3+ were used in the preparation of PAA/TA@HC/Fe3+ hydrogels. The addition of TA@HC and Fe3+ in the sodium persulfate (SPS) and acrylic acid (AA) polymerization system resulted in a fast gelation process that was completed within a short time (as short as 30 s) at room temperature. The catechol-rich TA and Fe3+ system allows for quick activation of SPS to produce free radicals, generating abundant hydroxyl groups in a short period of time, which was responsible for the fast gelation. Furthermore, due to the TA@HC effect and the dynamic catechol (TA)-Fe3+ redox system, the PAA/TA@HC/Fe3+ hydrogel exhibited excellent mechanical properties with an exceptionally high strain (as high as 5600%), adhesiveness, rapid and efficient self-healing ability, and reproducible self-adhesion onto various substrates. More importantly, asymmetric adhesive PAA/TA@HC/Fe3+ hydrogels were prepared by selective Fe3+ coating of the upper hydrogel surface to render the top surface nonadhesive so that the same hydrogel with different adhesiveness between the upper and bottom surfaces was obtained. The asymmetric adhesive hydrogel design permits the adhesive side to fit comfortably to the skin and the nonadhesive side showing anti-interference against various different pollutant materials, accurately serving as a pressure sensor.
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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 | 0.000 | 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) | 0.000 | 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".