Photocurable Methacrylated Silk Fibroin/Hyaluronic Acid Dual Macrocrosslinker System Generating Extracellular Matrix‐Inspired Tough and Stretchable Hydrogels
Bibliographic record
Abstract
Abstract Extracellular matrix (ECM) containing interconnected proteins and glycosaminoglycans (GAGs) is a vital component of a tissue. Its gel‐like physicochemical architecture is always a model for scientists studying in the fields of material science. Here, inspired from the ECM, soft hydrogels possessing an interconnected protein/GAG network are fabricated. This network comprises silk fibroin (SF) and hyaluronic acid (HA) as a protein and a GAG component, respectively. The interconnection of the SF and HA is performed by using both methacrylated SF (meth‐SF) and HA (meth‐HA), which behave as macrocroslinkers for a monomer forming a flexible polymer network between the meth‐SF and meth‐HA. Meth‐HA/meth‐SF hydrogels can be compressed and stretched up to 95% and 300%, respectively, with fracture stresses varying between kPa to MPa ranges. Furthermore, they have highly frequency‐dependent viscoelastic properties above a particular frequency, likewise seen in many cells and tissues. Mechanical and viscoelastic properties of the hydrogels can be easily tuned by changing the methacrylation degree of the HA, and the concentration and the type of the monomer. It is believed that the meth‐HA/meth‐SF hydrogels prepared within the scope of this study will be good candidates for tissue engineering applications.
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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".