Ultrasound Cavitation Enables Rapid, Initiator‐Free Fabrication of Tough Anti‐Freezing Hydrogels
Bibliographic record
Abstract
Abstract Hydrogels are often synthesized with thermal or photo‐initiated gelation, leaving alternative energy sources less explored. While ultrasound has been used for polymer synthesis and mechanochemistry, its application through cavitation for hydrogel synthesis as a constructive force is rare, and the underlying sonochemical mechanisms are poorly understood. Here, the application and mechanism of ultrasound cavitation for rapid, initiator‐free, and oxygen‐tolerant fabrication of tough anti‐freezing hydrogels is reported. By incorporating polyol solvents and interpenetrating polymers into the gelling solution, radical generation is amplified and network formation is enhanced. Using tough polyacrylamide‐alginate hydrogels as a model system, rapid gelation (as fast as 2 minutes) and high fracture toughness (up to 600 J m − 2 ) is demonstrated. By varying ultrasound intensity, crosslinker‐to‐monomer ratio, and glycerol concentration, the synthesis‐structure‐property relation is established for the resulting sonogels and the underlying mechanism is uncovered using combined molecular, optical, and mechanical testing techniques. The coupling of gelation and convection under ultrasound results in sonogels with unique structural and mechanical properties. Additionally, the fabrication of hydrogel constructs is demonstrated using both non‐focused and high‐intensity focused ultrasound. This work establishes a foundation for ultrasound‐driven sono‐fabrication and highlights new avenues in soft materials, advanced manufacturing, bioadhesives, and tissue engineering.
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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".