Agglomeration of Needle-like Crystals in Suspension: I. Measurements

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.