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Protein-stabilized emulsions are used in many applications but their stabilization mechanisms, particularly for plant-based proteins, are only partially understood.  This dissertation investigates the structural and rheological properties of emulsions stabilized with pea proteins, focused on the role of the excess present in the continuous phase.  The main finding is that pea proteins contribute to the stability in two ways, both as a classical interfacial material that adsorbs to the oil-water interface, as well as being dispersed in the continuous phase where they form a hydrated fractal-like network.  This network increases viscosity and induces gelation, which provides great emulsion stability across a wide range of pH, compositions and temperatures.  

Systematic mapping of stable compositions revealed a previously unexplored stability region at intermediate oil concentrations (~ 10-60% v/v) and high protein concentrations (~ 5-15% w/v).  Structural characterization including confocal microscopy, and X-ray and neutron scattering, revealed that most of the protein is present in the continuous phase as hydrated aggregates forming networks that extend to micrometre length scales.  These networks are important for the resulting droplet size and rheological stability.  The emulsions exhibit shear thinning and thixotropic behaviour, typical of colloidal systems, as well as a yield stress that restricts droplet motion and contributes to stability.   The viscosity increases with protein concentration according to the Krieger Dougherty relationship when a large effective volume fraction of hydrated proteins is considered.  The droplet size decreases with increases of protein concentration, oil concentration, pH and applied shear, while changes of temperature have limited effect.  

Comparison with other emulsions formed with plant-based materials indicates that similar stabilization mechanisms may occur in those systems with sufficient excess biopolymer in the continuous phase.  This demonstrates the broader relevance of this work, where the formation of a viscoelastic network can significantly improve emulsion stability.  By introducing a new way of representing scattering data, rapid visual comparison between complex samples is simplified, which could improve efficiency in the handling of large data sets and aid automated interpretation with artificial intelligence.