High‐Throughput Object Recognition and Sizing in Disperse Systems

Abstract

Size and shape of dispersed objects defines properties of suspensions, emulsions, and foams, such as stability, texture, and flow. Accordingly, a rational product design requires reliable size distribution analysis. This is particularly challenging in dense foams. An endoscopic setup was optimized for bubble imaging minimizing light reflections, uneven illumination, and foam distortion. A software tool was developed detecting large quantities of foam bubbles at dispersed phase fractions up to 93 % from images with spatially varying contrast within minutes based on the template matching algorithm. Reliability of the method is also illustrated for a bimodal glass bead mixture, anisotropic nanocrystals, and emulsion droplets during freezing.

Shear modulus and yield stress of foams: contribution of interfacial elasticity

“…The bubbles size distribution was automatically determined from the images without further processing using a template matching based bubble detection tool as described elsewhere. 22 The images were screened for similarities with circular templates differing in radius and the Sauter bubble radius R 32 was calculated from the ensemble of detected bubbles. At specific foam ages corresponding to desired gas volume fractions between 0.85 and 0.95, foams were transferred into the gap of a plate-plate geometry and sheared in oscillation using a rotational rheometer (RheoScope 1, Thermo Fisher Scientific) at 1 Hz frequency and varying stress amplitude between 0.1 and 100 Pa as described in ref.…”