Ahn Byeongho, Bosetti Luca, Mazzotti Marco
Institute of Energy and Process Engineering, ETH Zurich, 8092 Zurich, Switzerland.
Cryst Growth Des. 2022 Jan 5;22(1):661-672. doi: 10.1021/acs.cgd.1c01193. Epub 2021 Dec 7.
The effect of molecular cluster formation on the estimation of kinetic parameters for primary nucleation and growth in different systems has been studied using computationally generated data and three sets of experimental data in the literature. It is shown that the formation of molecular clusters decreases the concentration of monomers and hence the thermodynamic driving force for crystallization, which consequently affects the crystallization kinetics. For a system exhibiting a strong tendency to form molecular clusters, accounting for cluster formation in a kinetic model is critical to interpret kinetic data accurately, for instance, to estimate the specific surface energy γ from a set of primary nucleation rates. On the contrary, for a system with negligible cluster formation, a consideration of cluster formation does not affect parameter estimation outcomes. Moreover, it is demonstrated that using a growth kinetic model that accounts for cluster formation allows the estimation of γ from typical growth kinetic data (i.e., de-supersaturation profiles of seeded batch crystallization), which is a novel method of estimating γ developed in this work. The applicability of the novel method to different systems is proven by showing that the estimated values of γ are closely comparable to the actual values used for generating the kinetic data or the corresponding estimates reported in the literature.
利用计算生成的数据以及文献中的三组实验数据，研究了分子簇形成对不同体系中初级成核和生长动力学参数估计的影响。结果表明，分子簇的形成降低了单体浓度，从而降低了结晶的热力学驱动力，进而影响结晶动力学。对于表现出强烈形成分子簇倾向的体系，在动力学模型中考虑簇的形成对于准确解释动力学数据至关重要，例如，从一组初级成核速率估计比表面能γ。相反，对于簇形成可忽略不计的体系，考虑簇的形成不会影响参数估计结果。此外，结果表明，使用考虑簇形成的生长动力学模型能够从典型的生长动力学数据（即接种分批结晶的过饱和度曲线）中估计γ，这是本研究中开发的一种估计γ的新方法。通过表明γ的估计值与用于生成动力学数据的实际值或文献中报道的相应估计值密切可比，证明了该新方法对不同体系的适用性。
