Hyaluronic-Acid-Based Organic-Inorganic Composites for Biomedical Applications
Bibliographic record
Abstract
Applications of natural hyaluronic acid (HYH) for the fabrication of organic-inorganic composites for biomedical applications are described. Such composites combine unique functional properties of HYH with functional properties of hydroxyapatite, various bioceramics, bioglass, biocements, metal nanoparticles, and quantum dots. Functional properties of advanced composite gels, scaffold materials, cements, particles, films, and coatings are described. Benefiting from the synergy of properties of HYH and inorganic components, advanced composites provide a platform for the development of new drug delivery materials. Many advanced properties of composites are attributed to the ability of HYH to promote biomineralization. Properties of HYH are a key factor for the development of colloidal and electrochemical methods for the fabrication of films and protective coatings for surface modification of biomedical implants and the development of advanced biosensors. Overcoming limitations of traditional materials, HYH is used as a biocompatible capping, dispersing, and structure-directing agent for the synthesis of functional inorganic materials and composites. Gel-forming properties of HYH enable a facile and straightforward approach to the fabrication of antimicrobial materials in different forms. Of particular interest are applications of HYH for the fabrication of biosensors. This review summarizes manufacturing strategies and mechanisms and outlines future trends in the development of functional biocomposites.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".