Nanoparticles exhibiting unique opto-electronic properties have received special interest in the past years. One such example are Quantum Dots (QD), i.e. small semiconductor nanocrystals that exhibit quantum confinement, due to their unique size-dependent opto-electronic properties. QDs are applied in diverse fields including photovoltaics and displays. However, reproducible production of well-defined materials with high quality is still a clear challenge. Automated hot injection synthesis of CdSe nanocrystals using a Chemspeed Technologies platform was selected as it is a benchmark process of high complexity. Inline temperature monitoring and rapid sampling to 96 well micro titer plates (MTPs) showed outstanding reproducibility of the robotic system complimented by automated cleaning. We show high reproducibility in combination with early stage sampling and controlled mixing allowed us to systematically analyze the influence of stirring as a process parameter on focusing (narrowing) and defocusing (widening) of particle size distributions (PSDs), that was expressed in terms of the evolution of the relative standard deviation (RSD). Consequently, a deeper investigation on the process-structure relationship was conducted for the influence of mixing using high throughput methodologies such as Design of Experiments (DoE) and Machine Learning (ML) for process optimization and scale-up and structure-property using automated quantum yield (QY) determination.
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