Conversion of Viscous Oil-in-Water Nanoemulsions to Viscoelastic Gels upon Removal of Excess Ionic Emulsifier
Bibliographic record
Abstract
Viscous, flowable nanoemulsions stabilized with ionic emulsifier can be transformed into repulsively jammed elastic gels that do not flow under gravity by reducing the droplet size and increasing the interfacial repulsive shell layer thickness. However, a high concentration of emulsifier required to achieve nanodroplets could remain in the continuous phase and lead to oscillatory structural forces, thereby reducing repulsive interaction and forming flowable liquid systems. It was hypothesized that the removal of excess emulsifier from a nanoemulsion could lead to the formation of repulsive gels. Canola oil-in-water nanoemulsions, containing 40 wt % oil, were prepared with a citric acid ester of monoglyceride (Citrem) using a high-pressure homogenizer. The excess emulsifier in the aqueous phase was removed by multiple ultracentrifugation cycles, and the droplet size, rheology, and stability of the nanoemulsions were investigated as a function of excess Citrem concentration. Nanoemulsions with average droplet sizes of 222 and 150 nm were obtained with 3 and 5 wt % Citrem, respectively. The removal of excess Citrem did not change the droplet size significantly. However, the viscosity, yield stress, and storage moduli increased significantly with the reduction of excess Citrem and the decrease in droplet size, converting the flowable weak gel nanoemulsion to a strong viscoelastic gel. The calculated values of oscillatory structural forces decreased with the removal of excess emulsifier, leading to an increase in repulsive interactions and the thickness of the electric double layer. Such an increase in interdroplet separation led to an increase in the effective oil volume fraction beyond the maximum random jamming of oil droplets and the formation of a viscoelastic nanoemulsion gel.
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How this classification was reachedexpand
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) | 0.001 | 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 teacher head, 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".