Ionic Crosslinking Improves the Stiffness and Toughness of Protein Hydrogels
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Protein-based hydrogels are promising materials for biomedical and materials science applications. However, engineering hydrogels with both high stiffness and high toughness, a key requirement for many applications, remains challenging. Recently, by using the denatured crosslinking method, we developed highly stiff and tough protein hydrogels based on the polyprotein (FL) 8 via introducing chain entanglements into the hydrogel network, which allow for stiffening the hydrogel without sacrificing toughness. These hydrogels exhibited a Young’s modulus of ∼0.7 MPa and breaking strain of ∼100% in tensile tests. To further enhance their stretchability and toughness, here we report the engineering of a protein/alginate hybrid hydrogel, in which the protein and alginate networks are covalently joined. Alginate was chemically modified with tyramine to introduce phenol groups, allowing the modified alginate to be photochemically crosslinked together with the polyprotein (FL) 8 to form a hybrid network hydrogel. Using calcium-mediated ionic crosslinking, we demonstrated the feasibility to tune the Young’s modulus and breaking strain of these hydrogels by controlling the degree of tyramine modification of alginate. Our results showed that incorporating noncovalent ionic crosslinking into the hydrogel network increased the hydrogel’s stretchability from ∼100% to over 200% without compromising stiffness, significantly improving the hydrogel’s toughness. This work expands the mechanical tunability of protein hydrogels and the repertoire of strategies for engineering hydrogels with a broad range of mechanical properties.
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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) | 0.001 | 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) | 0.002 | 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".