(367c) Crystallization in Microfluidic Systems: Modeling in Confined Geometries for Process Development

Crystallization plays a pivotal role in the pharmaceutical, fine chemical, and materials industries, demanding precise control over crystal size distribution, polymorphism, and morphology [1]. Recent advances have underscored the potential of microfluidic systems to achieve this control through rapid mass and heat transfer, reduced reagent consumption, and enhanced reproducibility [2], [3]. However, exploiting microfluidic crystallizers to their fullest requires integrated modeling approaches that can capture both macroscopic flow dynamics and microscopic crystal growth mechanisms. Microfluidic platforms provide unique opportunities to tailor supersaturation, mixing, and residence times due to their high surface-to-volume ratios and laminar flow regimes [4]. These features are particularly beneficial for crystallizing sensitive compounds and for exploring complex crystallization pathways. Nonetheless, designing and scaling up such systems remains challenging. Traditional computational fluid dynamics (CFD) simulations excel at describing fluid flow, concentration profiles, and temperature fields but do not inherently track the discrete events of nucleation, crystal growth, and attrition at the micro scale [5]. By coupling CFD with a kinetic Monte Carlo (kMC) model [6], it becomes possible to simulate the local solute concentration, flow-based transport phenomena, and the time-evolving crystal size distribution (CSD) simultaneously.

We developed an integrated CFD-kMC framework for crystallization in microfluidic devices. Within our approach, CFD first resolves the velocity, temperature, and concentration fields in a representative microfluidic channel or droplet-based geometry. These local properties inform the kMC module, which governs nucleation and growth on a per-crystal basis via probability-driven events. The kMC model updates the evolving crystal population, whose feedback on solution concentration is then transferred back to the continuum-scale CFD. This two-way coupling ensures that even subtle changes in nucleation or growth rates can influence local supersaturation fields, reshaping subsequent crystallization dynamics. As a result, we capture spatial heterogeneities near channel walls, stagnation zones, or high-shear regions, all of which can significantly affect final product quality. Comparisons to experimental data demonstrate that the CFD-kMC framework accurately predicts induction times, crystal yield, and size distributions. Moreover, simulations reveal that design parameters (e.g., channel length, cross-sectional geometry, flow rate ratio, and temperature gradients) can be systematically optimized to avoid clogging, reduce undesired nucleation bursts, and promote uniform crystal growth. Such predictive power is instrumental for process developers, who can now evaluate new microfluidic layouts, operating conditions, and solvent-antisolvent combinations more efficiently than would be feasible by trial-and-error experimentation alone.

References:

[1] J. Jang, W.-S. Kim, T. S. Seo, and B. J. Park, “Over a decade of progress: Crystallization in microfluidic systems,” Chemical Engineering Journal, vol. 495, p. 153657, 2024, doi: https://doi.org/10.1016/j.cej.2024.153657.

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[5] S. Teychené and B. Biscans, “Crystal nucleation in a droplet based microfluidic crystallizer,” Chem Eng Sci, vol. 77, pp. 242–248, 2012, doi: https://doi.org/10.1016/j.ces.2012.01.036.

[6] J. S. Kwon, M. Nayhouse, G. Orkoulas, and P. D. Christofides, “Crystal shape and size control using a plug flow crystallization configuration,” Chem Eng Sci, vol. 119, pp. 30–39, 2014, doi: https://doi.org/10.1016/j.ces.2014.07.058.