Ultrasoft Self-Healing Nanoparticle-Hydrogel Composites with Conductive and Magnetic Properties
Bibliographic record
Abstract
Recently, integration of two or more important properties into a hydrogel has been a challenge in the preparation of the multifunctional hydrogel. Herein, in order to impart conductive and magnetic properties to the self-healing PVA hydrogel at the same time, the nanofibrillated cellulose (NFC) was used as the substrate. The polyaniline was coated on the NFC surface by in situ chemical polymerization, and the MnFe 2 O 4 nanoparticles were synthesized and loaded on the NFC by the chemical co-precipitation method. The multifunctional PVA hydrogel was prepared by incorporating the NFC/PAni/MnFe 2 O 4 nanocomposites with the PVA hydrogel. The magnetic and conductive property tests of the multifunctional PVA hydrogel showed that the maximum saturation magnetization and conductivity were 5.22 emu·g –1 and 8.15 × 10 –3 S·cm –1, respectively. Moreover, the multifunctional PVA hydrogel exhibited excellent self-healing and ultrasoft properties, which could be self-healed completely after the pieces of the hydrogel were put together for several minutes at room temperature. Due to the self-healing ability, conductivity, and magnetism, the novel hydrogel was expected to be used in many practical applications, such as electrochemical display devices, rechargeable batteries, and electromagnetic interference shielding. More importantly, we proved a facile template approach to the preparation of a stable polymer and nanoparticle composites using NFC as substrates that imparted different properties to hydrogels.
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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".