Recent development and biomedical applications of self-healing hydrogels
Bibliographic record
Abstract
INTRODUCTION: Hydrogels are of special importance, owing to their high-water content and various applications in biomedical and bio-engineering research. Self-healing properties is a common phenomenon in living organisms. Their endowed property of being able to self-repair after physical/chemical/mechanical damage to fully or partially its original properties demonstrates their prospective therapeutic applications. Due to complicated preparation and selection of suitable materials, the application of many host-guest supramolecular polymeric hydrogels are so limited. Thus, the design and construction of self-repairing material are highly desirable for effectively increase in the lifetime of a functional material. However, recent advances in the field of materials science and bioengineering and nanotechnology have led to the design of biologically relevant self-healing hydrogels for therapeutic applications. This review focuses on the recent development of self-healing hydrogels for biomedical application. AREAS COVERED: The strategies of making self-healing hydrogels and their healing mechanisms are discussed. The significance of self-healing hydrogel for biomedical application is also highlighted in areas such as 3D/4D printing, cell/drug delivery, as well as soft actuators. EXPERT OPINION: Materials that have the ability to self-repair damage and regain the desired mechanical properties, have been found to be excellent candidate materials for a range of biomedical uses especially if their unique characteristics are similar to that of soft-tissues. Self-healing hydrogels have been synthesized and shown to exhibit similar characteristics as human tissues, however, significant improvement is required in the fabrication process from inexpensive and nontoxic/non-hazardous materials and techniques, and, in addition, further fine-tuning of the self-healing properties are needed for specific biomedical uses.
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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 | 0.001 | 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) | 0.002 | 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".