Understanding Stochastic Processes Through Accurate Detection Time Measurements with the Crystal16

 

This work is provided by Elena Cândida dos Santos, PhD student at ETH Zurich under the supervision of Prof. Marco Mazzotti. Her research focuses on developing high-throughput droplet-based microfluidic platforms for elucidating the stochastic behavior of systems in crystallization processes.

 

Nucleation is a crucial step during the crystallization process; its characteristics have relevant consequences for process outcomes such as particle size distribution and polymorphic forms, presence of fines, yields, among others. The Separation Processes Laboratory from ETH Zürich has a broad scientific program that aims at understanding fundamental physical and chemical aspects of crystallization processes in order to enable comprehensive process design. Our research focuses on investigating the mechanisms of crystal nucleation by assessing the role of its intrinsic stochasticity in systems of various sizes, which is of great importance for efficient process scale-up and scale-down. In recent years, new technologies became available which allow multiple simultaneous repetitions of the same experiment. Crystal16 Temperature Measurement System (Crystal16-TMS) offers such capabilities. Small crystallization volumes provided by Crystal16-TMS enable generating large datasets with a modest amount of substances. More importantly, many experiments can be performed at the same time, thus shortening the time needed to obtain statistically relevant amounts of data. During our studies, repetition of the experiments were hence obtained by running temperature cycles in 16 vial-like crystallizers, simultaneously.





Crystal16 with in-vial Temperature Measurement System (Crystal16-TMS) allowing detection of temperature changes during crystallization. 




The Crystal16-TMS enables measuring the temperature inside each crystallizer, which allowed us to calculate important properties of the system such as supersaturations. Nucleation rates can be retrieved based on mathematical models [1], which, on the one hand, help clarifying the physical origin of stochasticity, and on the other hand, allow correlating the stochastic formation of primary nuclei with detection times acquired with the Crystal16-TMS. As a result, average properties of the products needed for correct design of crystallization processes can be determined.

 

[1]. Maggioni, G.M.; Mazzotti, M. Faraday Discuss. 2015, 179, 291-307