Comparison of Pickering and Network Stabilization in Water-in-Oil Emulsions
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Bibliographic record
Abstract
We compared the efficacy of Pickering crystals, a continuous phase crystal network, and a combination thereof against sedimentation and dispersed phase coalescence in water-in-oil (W/O) emulsions. Using 20 wt % water-in-canola oil emulsions as our model, glycerol monostearate (GMS) permitted Pickering-type stabilization, whereas simultaneous usage of hydrogenated canola oil (HCO) and glycerol monooleate (GMO) primarily led to network-stabilized emulsions. A minimum of 4 wt % GMS or 10 wt % HCO was required for long-term sedimentation stability. Although there were no significant differences between the two in mean droplet size with time, the free water content of the network-stabilized emulsions was higher than Pickering-stabilized emulsions, suggesting higher instability. Microscopy revealed the presence of crystal shells around the dispersed phase in the GMS-stabilized emulsions, whereas in the HCO-stabilized emulsion, spherulitic growth in the continuous phase and on the droplet surface occurred. The displacement energy (E(disp)) to detach crystals from the oil-water interface was ∼10(4) kT, and was highest for GMS crystals. Thermal cycling to induce dispersed phase coalescence of the emulsions resulted in desorption of both GMS and GMO from the interface, which we ascribed to solute-solvent hydrogen bonding between the emulsifier molecules and the solvent oil, based on IR spectra. Overall, Pickering crystals were more effective than network crystals for emulsion stabilization. However, the thermal stability of all emulsions was hampered by the diffusion of the molten emulsifiers from the interface.
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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 | 0.000 |
| 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 it