Digital image analysis of droplet patterns in polymer systems: Point pattern

New techniques of digital image analysis which are especially suitable for analyzing particle‐distribution patterns are presented. Statistical mathematical methods are applied to the quantitative analysis of a spatial distribution of points in polymer systems. The techniques are useful for studying the spatial patterns of particles or droplets which are commonly observed in nucleation‐growth‐type phase separation and late‐stage spinodal decomposition in polymer mixtures, crystallization process, microphase separation in block copolymers, incompatible polymer alloys, and composite materials, etc. We can divide point patterns into three typical point patterns: the Poisson pattern, the clustered pattern, and the regular pattern. The kind and strength of interaction or force between points can be determined from spatial point‐distribution patterns. The point‐pattern analysis has been applied to phase‐separated structures of a polymer mixture, and it has been revealed that the pattern belongs to the regular pattern. The appearance of the regular pattern is probably a result of the Brownian coalescence mechanism for the droplet growth and it might also be due to a long‐range interaction among droplets through diffusion field.