One-Pot Preparation of Autonomously Self-Healable Elastomeric Hydrogel from Boric Acid and Random Copolymer Bearing Hydroxyl Groups
Bibliographic record
Abstract
Self-healable hydrogels based on the dynamically reversible boronate ester or borate ester bonds are usually prepared by reacting boronic acid or boric acid with diol compounds or polymer-like poly(vinyl alcohol) bearing a hydroxyl group in each monomer unit. Herein, we report a finding that not only facilitates the preparation but also extends the range of self-healable hydrogels of this kind. By simply copolymerizing commercially available N, N -dimethylacrylamide and 2-hydroxyethyl acrylate (8:2 weight ratio) in the presence of boric acid in a one-pot fashion, the resulting random copolymer can gel in aqueous solution at pH = 9, giving rise to a solid hydrogel (tensile strength >0.5 MPa at water content of 30%) that, on the one hand, can autonomously self-heal (near 100% fracture stress recovery within 48 h in air at room temperature) and, on the other hand, shows the characteristics of elastomer (little stress relaxation under loading and small residual deformation after unloading upon repeated 300% elongation cycles). The results reveal that it can be sufficient to have a random copolymer with comonomer units bearing hydroxyl groups for reacting with boric acid to generate dynamically reversible borate ester bonds. This finding thus points out a general, facile, and cost-effective method to obtain and explore new borate ester bond-based self-healable 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".