Impact of composition and surfactant-templating on mesoporous bioactive glasses structural evolution, bioactivity, and drug delivery property
Bibliographic record
Abstract
This study explores mesoporous bioactive glasses (MBGs) that show promise as advanced therapeutic delivery platforms owing to their tailorable porous properties enabling enhanced drug loading capacity and biomimetic chemistry for localized, sustained release. This work systematically investigates the complex relationship between MBG composition and surfactant templating on structural evolution, in vitro bioactive response, resultant drug loading efficiency and release. A total of 12 samples of sol-gel-derived MBG were synthesized using cationic and non-ionic structure-directing agents (cetyltrimethylammonium bromide, Pluronic F127 and P123) while modulating the SiO 2 /CaO content, generating MBG with surface areas of 60–695 m 2 /g. Electron microscopy and nitrogen desorption studies verified the successful synthesis of the 12 ordered MBG formulations. Assessment of hydroxyapatite conversion kinetics via FTIR spectroscopy and SEM demonstrated accelerated deposition for 70–80% SiO 2 formulations, independent of the surfactant used. However, the templating agent had an impact on drug loading as observed in this study where MBG synthesized by the templating agent Pluronic P123 had higher drug loading compared to the other surfactants. To determine the drug release mechanisms, the in vitro kinetic profiles were fitted to various mathematical models including ze-ro. Most compositions exhibited release properties closest to zero-order, indicating a concentration-independent drug elution rate. These results in this study explain the relationship between tailored hierarchical architecture and intrinsic ion release rates to enable advanced functionality.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".