Water droplets as fillers: the effect of a dispersed aqueous phase on the rheology and microstructure of fat crystal-stabilized water-in-oil emulsions
Bibliographic record
Abstract
Composite systems are continuous matrices that contain embedded particles, called fillers, that can be used to manipulate the mechanical strength and viscoelastic properties of the resulting material. The use of fillers in composite systems is common practice and largely depends on particle size and shape, surface functional groups and aggregation behaviour. Emulsions are complex food dispersions and, while they have structural similarities in common with composites, they are not widely considered in this light. In this thesis, fat crystal network-stabilized emulsions prepared using various emulsifiers were studied to determine whether varying the structure of the water droplet surface could modulate the viscoelastic behaviour of the resulting composite. The oil phase consisted of a high melting triglyceride in canola oil with an emulsifier that promoted a specific droplet surface, namely polyglycerol polyricinoleate (PGPR) which did not promote interfacial crystallization, or one of two monoglycerides [glycerol monooleate (GMO), or glycerol monostearate (GMS)], which both enhanced interfacial crystallization of fats. Interfacial fat crystallization imparted structural rigidity by surrounding the dispersed aqueous droplets within a solid shell that interacted with adjacent network crystal aggregates and other droplets. The work here shows that the presence of interfacial fat causes the droplet to promote an increase in viscosity and in G′, thus reinforcing the crystal network. These effects were enhanced by decreasing the droplet size and increasing the volume fraction of the dispersed aqueous phase. As well, the crystalline shell that provided imparted active filler qualities to the droplets also protected the droplet from shear degradation, protecting encapsulated contents from being released. PGPR, a branched, polymeric emulsifier, minimized the presence of interfacial fat crystallization such that interactions of the dispersed droplets interacted only weakly with nearby droplets and fat crystals. As a result, the dispersed phase did not have a pronounced effect on emulsion reinforcement or viscosity and displayed predominantly viscous behaviour irrespective of volume fraction. These aqueous droplets were also less effective as encapsulation vehicles as they were more prone to shear-mediated release of encapsulated material. These results show that the droplet interface is determinant in fat crystal network rheology and shear-stability by establishing the dispersed phase droplets of such emulsions as either active or inactive fillers. Mechanically strong droplets (i.e., those with interfacial fat crystal shells) which interacted strongly with their surroundings more effectively reinforced/added rigidity to the emulsion - behaviour typical of active fillers. Conversely, the presence of weakly interacting droplets (i.e., liquid interface) did not impart changes to emulsion rheology. As with solid filler particles, droplet characteristics, such as size and volume fraction can then be used to predictably modulate viscoelastic behaviour and encapsulation efficiency. Filler “activity” can also be used as a tool to control encapsulation or release of compounds during shear. This thesis experimentally demonstrates that embedded droplets with interfaces tailored by the use of surfactants can be considered a functional and tunable component of fat crystal network-stabilized emulsions.
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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) | 0.001 | 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".