CFD-Based Computational Studies of Quantum Dot Size Control in Slug Flow Crystallizers: Handling Slug-to-Slug Variation

Recently, slug-flow crystallizers (SFCs) have been proposed for continuous manufacturing of colloidal quantum dots (QDs). Despite the intriguing advantages of SFCs for controlled manufacturing of QDs, it has been difficult to account for the wide crystal size distribution (CSD) caused by slug-to-slug (S2S) variation, and the absence of a modeling and control framework made it challenging to fine-tune the QD size distribution. In response, we developed a computational fluid dynamics (CFD) model to simulate the S2S variation in SFCs. The results from the CFD model were integrated with a slug crystallizer model, which can describe the effect of S2S heterogeneity on crystallization of QDs in SFCs. Specifically, the slug crystallizer model was constructed by combining a continuum model with a kinetic Monte Carlo model. Based on the proposed CFD-based multiscale model, an optimal operation problem was formulated to ensure a good set-point (QD size) tracking performance and a narrow CSD.

Automated summary

The study introduced slug-flow crystallizers for the manufacturing of colloidal quantum dots, but faced challenges with wide crystal size distribution. By integrating a computational fluid dynamics model with a slug crystallizer model, they developed an optimal operation problem to track QD size and achieve a narrow CSD distribution.