Agglomeration of needle-like crystals in suspension: I. Measurements

Abstract

A technique for the detection and measurement of the agglomeration of needle-like particles is presented. A novel image analysis routine, based on a supervised machine learning strategy, is used to identify agglomerates that are subsequently characterized by their volume. Through repeated measurement of a large number of agglomerates, a 1D particle size distribution of agglomerates is reconstructed. Concurrently, established tools are used to characterize needle-like primary crystals, whose shape is described by cylinders and whose population can be described by a separate two-dimensional particle size and shape distribution. The performance of the classifier is evaluated, and the reproducibility of the measurement is demonstrated for the case of β l-glutamic acid. For the same system, the agglomeration behavior is studied for varying operating conditions, and general trends are analyzed.