Aerogel‐Based Biomaterials for Biomedical Applications: From Fabrication Methods to Disease‐Targeting Applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 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