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Bijels (bicontinuous interfacially jammed emulsion gels) have the potential to be useful in many different applications due to their internal connectivity and the possibility of efficient mass transport through the channels. Recently, new methods of making the bijel have been proposed, which simplify the fabrication process, making commercial application more realistic. Here, we study the flow properties of bijels prepared by mixing alone using oscillatory rheology combined with confocal microscopy and also squeezing flow experiments. We found that the bijel undergoes a two-step yielding process where the first step corresponds to the fluidizing of the interface, allowing the motion of the structure, and the second step corresponds to the breaking of the structure. In the squeeze flow experiments, the yield stress of the bijel is observed to show a power law dependence on squeezing speed. However, when stress in excess of yield stress is plotted against shear rate, all the different squeeze flow data show a superposition.

While studies carried out in a Langmuir trough have rigorously demonstrated that, at high surface pressure, ellipsoidal particles do flip and spherocylinders (rods) can flip, much less is known about the practical situation on the surface of a droplet or bubble. We present emulsification studies using colloidal rods and find that the droplets are bridged by the rods independent of shear rate and particle concentration and are only weakly dependent on the pH of the continuous phase. In a trough, it is the low aspect ratio rods which flip and the high aspect ratio rods which form bilayers; on the surface of a droplet we found that the high aspect ratio rods always bridge whereas the shorter rods show random bridging behavior. Hence, the behavior of anisotropic particles "in action" is essentially opposite to expectations from trough studies.

Bicontinuous interfacially jammed emulsion gels (bijels) are novel composite materials that can be challenging to manufacture. As a step towards automating production, we have developed a machine learning tool to classify fabrication attempts. We use training and testing data in the form of confocal images from both successful and unsuccessful attempts at bijel fabrication. We then apply machine learning techniques to this data in order to classify whether an image is a bijel or a non-bijel. Our principal approach is to process the images to find their autocorrelation function and structure factor, and from these functions we identify variables that can be used for training a supervised machine learning model to identify a bijel image. We are able to categorise images with reasonable accuracies of 85.4% and 87.5% for two different approaches. We findthat using both the liquid and particle channels helps to achieve optimal performance and that successful classification relies on the bijel samples sharing a characteristic length scale. Our second approach is to classify the shapes of the liquid domains directly; the shape descriptors arethen used to classify fabrication attempts via a decision tree. We have used an adaptive design approach to find an image pre-processing step that yields the optimal classification results. Again, we find that the characteristic length scale of the images is crucial in performing the classification.