Stabilization of liquid water-in-oil emulsions by modifying the interfacial interaction of glycerol monooleate with aqueous phase ingredients
Bibliographic record
Abstract
Glycerol monooleate (GMO)-stabilized liquid water-in-vegetable oil (W/VO) emulsions are difficult to stabilize due to the desorption of GMO from the W-VO interface towards the oil phase. This work improved the stability of GMO-stabilized liquid 20 wt% water-in-canola oil (W/CO) emulsion by modifying the dispersed aqueous phase composition with hydrogen bond-forming agents. As a control, 20 wt% water-in-mineral oil (W/MO) emulsion was also utilized. Different concentrations of hydrogen bond-forming agents (citric acid (CA), ascorbic acid (AA), low methoxyl pectin (LMP)) with and without salts (sodium chloride (S) or calcium chloride (Ca)) was added to the aqueous phase before emulsification, which enhanced emulsifier binding to the water-oil interface. The emulsions were characterized by phase separation, stability against accelerated gravitation, microstructure and rheology. W/CO emulsion without any aqueous phase additive destabilized instantly, whereas W/MO emulsion stayed stable. The addition of hydrogen bond-forming agents and salts significantly improved emulsion stability. LMP, with many hydrogen bond-forming groups, was able to provide the highest emulsion stability after 7 days in both oils compared to AA, CA and their mixtures with S. Emulsions with both oils formed weak gels with viscous and elastic characteristics due to the formation of an extensive network of water droplet aggregates. Overall, the hydrogen bond-forming agents interacted with GMO at the interface, thereby improving their presence at the water droplet surface, allowing significantly improved stability of GMO-stabilized liquid W/CO emulsions. The knowledge developed in this research can be useful in applying GMO in stabilizing liquid water-in-oil emulsion without using any crystal network.
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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".