The analysis of TEM micrographs of the samples showed a monodispersed population of nearly spherical particles (see Figure ). Especially particles that are designed as reference material candidates require the measurement of a very large number of particles to achieve a high statistical significance. Therefore, and for the sake of demonstrating how bottle necks can be removed or at least mitigated when using characterization techniques that cannot be fully automated or used inline, we investigated the use of automatic image segmentation with the help of an artificial intelligence model. This allows to measure > 1000 particles within a couple of minutes, speeding up this task by at least 1-2 orders of magnitude compared to manual measurement while generating similar results (see Table and Figure ). The particle size distributions were very similar across batches (Figure ) with both automatic (Figure ) segmentation and manual measurement (Figure ) of particles resulting in similar mean particle sizes, namely 16.2 ± 0.4 nm from automatic segmentation and 16.5 ± 0.4 nm from manual measurement. Thus, considering all the size distributions from DLS and TEM (Figure ), the variation between batches was below 5%.