Ultraflexible Self-Healing Guar Gum-Glycerol Hydrogel with Injectable, Antifreeze, and Strain-Sensitive Properties
Bibliographic record
Abstract
Recently, flexible, injectable, and strain-sensitive hydrogels have attracted great research interest for application as electronic skin and wearable strain sensors. The synergistic integration of high flexibility, rapid self-healing, and antifreezing properties makes injectable, strain-sensitive, and self-healing guar gum hydrogels still a great challenge. Here, inspired by the strong hydrogen bonding of glycerol and water, the chelation cross-linking between glycerol and borax, we constructed a compact three-dimensional dynamic cross-linked net formed of glycerol-water-borax. Under stress, dynamic interactions of glycerol-water-borax net act as sacrificial bond energy for effective dissipation, which enables the hydrogel to achieve high flexibility, stretchability, and injectability. More importantly,because of the presence of glycerol, the antifreeze and moisturizing properties of the gel are improved. The hydrogel also exhibited an ultrafast self-healing ability of only 15 s. In addition, the results show that the hydrogel has self-adhesive properties and strain sensitivity. The hydrogels have the potential to be used to make flexible, wearable, and 3D-printable electronic skin and strain-sensitive sensors.
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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) | 0.000 | 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 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".