Synthesis of shape‐controlled silica nanoparticles via dual soft templates: A comparative study between aqueous and microemulsion synthesis for the impregnation of procaine anaesthesia drug
Bibliographic record
Abstract
Abstract Silica nanoparticles (SiNPs) are promising drug delivery nanocarriers due to their tunable size, porous structure, and surface properties. This study compares two synthesis methods: (i) a low‐temperature aqueous sol–gel process yielding SiNPs of 500–700 nm, and (ii) a water‐in‐oil (W/O) microemulsion using either CTAB or TX‐100 surfactants. Surfactant selection significantly affected nanoparticle size, stability, and dispersity. Characterization by dynamic light scattering (DLS), scanning electron microscopy (SEM), X‐ray diffraction (XRD), Brunauer–Emmett–Teller (BET), solid‐state nuclear magnetic resonance spectroscopy (ssNMR), X‐ray photoelectron spectroscopy (XPS), and photoluminescence analysis (PL) confirmed successful synthesis. The TX‐100‐mediated microemulsion method proved particularly effective in achieving highly stable, reproducible, and monodisperse SiNPs, with a size limit of approximately 100 nm, making them ideal candidates for drug encapsulation. Procaine (PRC) incorporation demonstrated the role of reverse micelle dynamics and surfactant‐stabilized interfaces in enhancing encapsulation efficiency. This work highlights the critical role of surfactant and medium selection in SiNPs synthesis, demonstrating their impact on nanoparticle stability, dispersity, and drug loading efficiency. The TX‐100‐mediated microemulsion technique emerges as a superior approach for producing stable, monodisperse SiNPs, advancing the design of nanocarriers for PRC drug delivery applications.
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How this classification was reachedexpand
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 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".