Recent development and biomedical applications of self-healing hydrogels
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it