Bibliographic record
Abstract
The purpose of this research was to investigate the effect of surfactant type and presence of solid fat on the stability and release characteristics of water-in-oil (W/O) emulsions subjected to simulated gastrointestinal conditions. Emulsions consisting of a 20 wt% aqueous phase dispersed in canola oil were stabilized in one of four different ways: core-shell stabilization with glycerol monostearate (GMS), network stabilization using polyglycerol polyricinoleate and solid fat added to the continuous phase (PGPR-F), combined core-shell and network stabilization using glycerol monooleate and a continuous phase fat crystal network (GMO-F) and finally, a PGPR-based liquid emulsion with no added fat. The dispersed aqueous phase of all emulsions contained 1mM methylene blue (MB), which was used as a marker to quantify emulsion breakdown and release of aqueous phase cargo. Quiescent storage at 25 °C for 30 days revealed no phase separation for the GMS, GMO-F, and PGPR-F emulsions whereas the PGPR emulsion began to phase-separate 16 h following preparation. When subjected to gastric conditions, the PGPR-F emulsion showed the lowest MB release after 60 min (0.3 % of initial load) with the other emulsions showing ~ 12 % release. In duodenal conditions, the PGPRF and GMS emulsions showed the lowest MB release after 120 min of exposure (~ 0.5 %) followed by the PGPR (9.4 %) and GMO-F (14.6 %) emulsions, respectively. Emulsion photomicrographs taken prior to, and after, contact with simulated gastric and intestinal fluids showed that emulsion microstructure was an important contributor to emulsion stability. Overall, the PGPR-F emulsion was the most stable in both gastric and intestinal fluids. These results have shown that fat phase structuring is an important contributor to W/O emulsion breakdown behaviour in simulated gastrointestinal conditions.
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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".