A Biomimetic “Salting Out—Alignment—Locking” Tactic to Design Strong and Tough Hydrogel
Bibliographic record
Abstract
Abstract Recently, hydrogel‐based soft materials have demonstrated huge potential in soft robotics, flexible electronics as well as artificial skins. Although various methods are developed to prepare tough and strong hydrogels, it is still challenging to simultaneously enhance the strength and toughness of hydrogels, especially for protein‐based hydrogels. Herein, a biomimetic “salting out—alignment—locking” tactic (SALT) is introduced for enhancing mechanical properties through the synergy of alignment and the salting out effect. As a typical example, tensile strength and modulus of initially brittle gelatin hydrogels increase 940 folds to 10.12 ± 0.50 MPa and 2830 folds to 34.26 ± 3.94 MPa, respectively, and the toughness increases up to 1785 folds to 14.28 ± 3.13 MJ m −3 . The obtained strength and toughness hold records for the previously reported gelatin‐based hydrogel and are close to the tendons. It is further elucidated that the salting out effect engenders hydrophobic domains, while prestretching facilitates chain alignment, both synergistically contributing to the outstanding mechanical properties. It is noteworthy that the SALT demonstrates remarkable versatility across different salt types and polymer systems, thus opening up new avenues for engineering strong, tough, and stiff 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".