Surface-active solid lipid nanoparticles as Pickering stabilizers for oil-in-water emulsions
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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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) | 0.000 | 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 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".