Fruit Peel-Inspired Super-Stable Ionic Organohydrogel Electronics with Dense Hydrophobic Skin
Bibliographic record
Abstract
Hydrogels are easily dehydrated during use and storage, making it difficult to maintain structural and performance stability, which greatly reduces their application value as wearable devices. Inspired by the structure of fruit, an ionic organohydrogel core containing Ca 2+ and glycerol was first fabricated, and then, stearic acid (STA) was rivetted on the organohydrogel surface through (3-aminopropyl) triethoxysilane to obtain a dense hydrophobic coating (100 μm). The organohydrogel with hydrophobic skin presented better water spreading resistance and mechanical strength. In addition to the moisturizing effect of Ca 2+ and glycerol, the skin also further blocks the contact between the organohydrogel and the air, thereby significantly enhancing the anti-dryness of the organohydrogel. Therefore, this organohydrogel has extremely outstanding structural, strength, and electrical conductivity stability. The developed strain sensor based on this organohydrogel can realize human motion monitoring capability at ultra-low temperature. This design idea, which combines multiple anti-drying mechanisms, has great expansion value for other gels suitable for various application scenarios.
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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".