Surface-active solid lipid nanoparticles as Pickering stabilizers for oil-in-water emulsions
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Bibliographic record
Abstract
Oil-in-water (O/W) emulsions solely stabilized by surface-active solid lipid nanoparticles (SLNs) were developed. The SLNs were generated by quench-cooling hot O/W nanoemulsions consisting of 7.5% glyceryl stearyl citrate (GSC) dispersed in water. Their initial volume-weighted mean particle diameter (∼152 nm) and zeta potential (ca.-49 mV) remained unchanged for 24 weeks. O/W emulsions (oil phase volume fraction: 0.2) containing 7.5% (w/w) GSC SLNs in the aqueous phase were kinetically-stable for 12 weeks and did not visually phase-separate over 24 weeks. The O/W emulsions generated with solid-state GSC SLNs had a volume-weighted mean oil droplet diameter of ∼459 nm and a zeta potential of ca.-43 mV. Emulsion microstructure evaluated with TEM revealed dispersed oil droplets sparsely covered with adsorbed Pickering-type SLNs as well aggregated SLNs present in the continuous phase. Gradual emulsion destabilization resulted from GSC SLN dissolution during the experimental timeframe. Overall, surface-active SLNs developed via nanoemulsions effectively kinetically stabilized O/W emulsions.
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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 | 0.000 | 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 | 0.001 |
| 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 it