Aerogel‐Based Biomaterials for Biomedical Applications: From Fabrication Methods to Disease‐Targeting Applications
Bibliographic record
Abstract
Aerogel-based biomaterials are increasingly being considered for biomedical applications due to their unique properties such as high porosity, hierarchical porous network, and large specific pore surface area. Depending on the pore size of the aerogel, biological effects such as cell adhesion, fluid absorption, oxygen permeability, and metabolite exchange can be altered. Based on the diverse potential of aerogels in biomedical applications, this paper provides a comprehensive review of fabrication processes including sol-gel, aging, drying, and self-assembly along with the materials that can be used to form aerogels. In addition to the technology utilizing aerogel itself, it also provides insight into the applicability of aerogel based on additive manufacturing technology. To this end, how microfluidic-based technologies and 3D printing can be combined with aerogel-based materials for biomedical applications is discussed. Furthermore, previously reported examples of aerogels for regenerative medicine and biomedical applications are thoroughly reviewed. A wide range of applications with aerogels including wound healing, drug delivery, tissue engineering, and diagnostics are demonstrated. Finally, the prospects for aerogel-based biomedical applications are presented. The understanding of the fabrication, modification, and applicability of aerogels through this study is expected to shed light on the biomedical utilization of aerogels.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".