Temperature-regulated flexibility of polymer chains in rapidly self-healing hydrogels
Bibliographic record
Abstract
Abstract Without the introduction of new functional groups, altering the properties of a substance, such as by changing from a non-self-healing to a rapidly self-healing material, is often difficult. In this work, we report that the properties of 2-hydroxyethyl methacrylate and acrylamide (HEMA/AAm) hydrogels can be easily altered from non-self-healing to rapidly self-healing by simply tuning the reaction temperature. Notably, the hydrogels that are prepared at room temperature do not exhibit self-healing behavior, while those treated at an elevated temperature show automatic self-healing performance within ~15 s. Interestingly, in contrast with the previous self-healing HEMA-based polymeric hydrogels, which function only above their glass transition temperatures (Tg), the hydrogels prepared herein exhibit rapid self-healing properties at room temperature, which is below their Tg. In addition, the stretching capabilities of the hydrogels can be greatly enhanced by up to 30-fold. The hydrogels also exhibit good adhesive performance and can adhere strongly onto various substrates, such as wood, glass, fabric, paper, leather, porcelain, and steel. For example, a 10 kg weight could be suspended from a wooden substrate with the aid of these hydrogels. These results may provide valuable insight regarding the design of self-healing hydrogels and their large-scale production.
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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".