The wide applications of oil-in-water emulsions range from pharmaceuticals to beverages, from paints to specialty chemicals, and more. In its simplest form, an emulsion is a binary system of immiscible liquids forming droplets. One of the main features to be monitored is stability, which can be affected by external factors as storage methods, time, temperature, osmolarity, pH, surfactant, shear stress. Weak stability primarily affects the droplets size (eg. due to coalescence), with important consequences on eg. the organoleptic properties of a soft drink or the efficacy and safety of a pharmaceutical product. Particle analysis plays thus a fundamental role in the design, formulation, and QC of oil-in-water emulsions, enabling the observation of the growth process and the stability of the particles. Classizer™ ONE fits the need of a value-added application in the characterization of oil emulsions. SPES/SPES² data provide physical and statistical information, as PSD, oversize, effective refractive index, an estimate of the behavior and stability. Each characteristic can be crucial to improve the knowledge and the quality of an oil-in-water formulate.
- Characterize the particle size, payload, stability, and concentration of emulsion / capsules / liposomes
- Measure size, concentration, and fate of particles in real heterogeneous fluids
- Estimate the shell thickness and payload of capsules
- Reduce formulation time and costs required to bring new products to market
- Optimize efficacy and improve stability of formulations
(in figure) EOS CLOUDS of a silicone oil-in-water emulsion. EOS software automatically estimates a RI of 1.40 in agreement with the expected value.
(in figure) Oversize study of the oil-in-water emulsion.
(in figure) EOS CLOUDS of a mineral oil-in-water emulsion. EOS software automatically estimates a RI of 1.47 in agreement with the expected value.
(in figure) Oversize study of the oil-in-water emulsion.
Example of application: mixture of two pre-emulsified oils 
A more complex case is the presence of multiple oils with different refractive indexes in the same liquid. The case considered here is the mixture of two pre-emulsified oils .
(in figure) EOS CLOUDS of a mix of two oil emulsions with different refractive indexes (1.40, 1.47). Two narrow data clouds are observed and are related to the two separated population of oil emulsions dispersed in the water.
Example of application: emulsions of a blend of oils 
Another complex case is the presence of multiple oils inside the single droplest in suspensions. The case considered here is the emulsions of oil blends at different concentrations. If the oils are mixed before emulsifying, both are present in the droplets suspended in the liquid. The resulting optical property, namely the RI, of the emulsion is expected to be the weight average of the RIs of the two oils. Accordingly, the Classizer™ ONE provides the user with a single population which RI is the average between the original bulk values of the oils. By changing the volume fraction of the two oils in the blend before the emulsification, it is possible to create emulsions with intermediate RI values. It is not possible to distinguish the blends based on the sole PSD, but nevertheless the slightly different positions on the EOS CLOUDS for the different blends provides an estimate of the original concentrations.
(in figure) EOS CLOUDS of a blend of two oils with different refractive indexes (1.40, 1.47) and emulsified in water. A single data cloud is observed. The software retrieves an effective average RI of 1.44.
(in figure) Measured refractive index of the emulsions based on the percentage of mix between the two starting oils (RI of 1.40 and 1.47, respectively).