Properties and Stability of Solid Lipid Particle Dispersions Based on Canola Stearin and Poloxamer 188
Bibliographic record
Abstract
Abstract Solid lipid particles (SLP) are one strategy for encapsulating lipophilic molecules, including for controlled release and enhanced bioavailability applications. SLP based on fully hydrogenated canola stearin (CaSt, 5, 10, 20, and 30 wt%) and the non‐ionic surfactant Poloxamer 188 (P188, 0.0, 0.5, 1.0, 2.5, 5.0 and 10.0 wt%) were produced by high pressure melt homogenization using a microfluidizer. Spherical particles in the region of 140 nm were formed, depending on compostion and processing parameters. Surfactant concentration and pressure had a significant influence on particle diameter (P < 0.05), although number of homogenization cycles did not (P > 0.05). A maximum surfactant surface load of approximately 4 mg m−2 was observed and, at or above 2.5% P188, excess surfactant was present in the continuous phase after production. P188 had the effect of decreasing particle size and facilitating transitions from the α to the β polymorph (P < 0.05) both through surface nucleation and size reduction effects. A stability study of the 10% CaSt SLP with 0.0, 1.0, or 5.0% P188 revealed particle growth for the 0.0 and 1.0% P188 SLP, especially at 20 versus 4 °C, but no changes in the 5.0% P188 SLP, which were exclusively in the β form, at both temperatures for up to 240 days.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".