Surface-active solid lipid nanoparticles for emulsion stabilization
Bibliographic record
Abstract
Solid lipid nanoparticles (SLNs) consisting of the food grade surfactant lipid glyceryl stearate citrate (GSC) were prepared using hot melt emulsification and high-pressure homogenization. Flash-cooling of the resultant oil-in-water nanoemulsions led to the formation of GSC SLNs. The effective mean particle size of the SLNs was ~180 nm by dynamic light scattering and the volume-weighted mean particle size was ~152 nm by laser diffraction, with a zeta (ζ) potential of ~-49 mV. Effective mean particle size and ζ potential were stable for 24 wk. An equilibrium contact angle of ~108.7⁰ measured through oil phase was obtained, suggesting a wettable lipid surface. The melting curve of the SLNs obtained by differential scanning calorimetry showed a significantly lower melting point and broader peak as compared to the bulk GSC, which was attributed to the nano-scale particle size. Unusually, the cooling curve showed an identical crystallization temperature as that for the bulk GSC, which suggested surface heterogeneous crystallization of the SLNs. Subsequent heating-cooling cycles confirmed the existence of nanosized particles in the sample during thermal analysis. Transmission electron (TEM) and atomic force microscopy (AFM) both revealed anisometric, flattened SLNs with circular or elliptical shapes. SLNs were studied for their effectiveness as colloidal emulsifiers in oil-in-water (o/w) emulsions. The generated o/w emulsions had a volume-weighted mean droplet size of ~459 nm by laser diffraction and ζ potential of ~-43 mV. The emulsions were stable for up to 12 wk. as observed for macroscopic changes by inverted light microscopy. TEM images pointed to the presence of a Pickering-type network stabilizing the emulsions. With time, desorption of the SLNs from the oil droplet surface into the continuous aqueous environment, coupled with Ostwald ripening, resulted in destabilization of the emulsions. Overall, these results demonstrated that stabilization of o/w emulsions was achievable using SLNs as the sole emulsifiers.
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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".