Gelation Dynamics, Formation Mechanism, Functionalization, and 3D Bioprinting of Silk Fibroin Hydrogel Materials for Biomedical Applications
Bibliographic record
Abstract
), is a natural protein polymer known for its biocompatibility, biodegradability, and sustainability. The protein can be processed into various material formats suitable for a range of applications. Among these, SF hydrogels are useful in the biomedical field, such as tissue engineering, due to the tailorable structures and properties achievable through tuning the gelation process. Therefore, the focus of this contribution is to comprehensively review and understand the formation, gelation mechanism, dynamic control, and functionalization of SF hydrogels. Unlike previous reviews, this work delves into understanding the strategies and mechanisms for tuning the gelation dynamics of SF from molecular assembly and crystallization points of view. Further, this review presents functionalization pathways and practical examples, such as for the 3D printing of SF hydrogels, to illustrate how these strategies, mechanisms, and pathways can be implemented in a specific application scenario. With these insights, researchers can gain a deeper understanding of how to manipulate or control the gelation process and the types of functionalization to achieve specific properties and features. This knowledge would further facilitate the development and application of SF hydrogel materials in various fields.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".