Analysis of network formation and long-term stability in silica nanoparticle stabilized emulsions
Bibliographic record
Abstract
Emulsions are widely used in industrial applications, including in food sciences, cosmetics, and enhanced oil recovery. For these industries, an in depth understanding of the stability and rheological properties of emulsions under both static and dynamic conditions is vital to their successful application. Presented here is a thorough assessment of a model nanoparticle (NP) stabilized dodecane-in-water emulsion as a route to improved understanding of the relationship between NP properties, microstructure and droplet-droplet interactions on the stability and rheological properties of emulsions. Emulsions are obtained here with low NP loadings without the need for added electrolyte through the use of an optimized silica NP (SNP) surface modification procedure. The prepared emulsions were characterized via optical microscopy, cryo-scanning electron microscopy (cryo-SEM), zeta potential analysis and laser scanning confocal microscopy (LSCM), enabling quantification of the emulsion droplet size, SNP interfacial coverage/morphology and surface charge. The correlation of these properties with the rheology of the emulsions is investigated through small amplitude oscillatory shear experiments which provide significant insight into the origins of the emulsions' rheological behavior and their stability. In addition, long-term stability, droplet-droplet network formation and microstructural evolution are found to be readily detectable shortly after preparation through measured progression of the emulsion's rheological properties.
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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 | 0.001 | 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".