A novel image analysis technique for 2D characterization of overlapping needle-like crystals

Selective anomer crystallization from aqueous solution: Monitoring lactose recovery under mutarotation limitation via attenuated total reflection Fourier-transform spectroscopy and theoretical rate analysis

Lactose is typically produced via cooling crystallization either from whey or whey permeate (edible grade) or from aqueous solution (pharmaceutical grade). While in solution, lactose is present in 2 anomeric forms, α- and β-lactose. During cooling crystallization under standard process conditions, only α-lactose crystallizes, depleting the solution of α-anomer. In practice, mutarotation kinetics are often assumed to be much faster than crystallization. However, some literature reports limitation of crystallization by mutarotation. In the present research, we investigate the influence of operating conditions on mutarotation in lactose crystallization and explore the existence of an operation regimen where mutarotation can be disregarded in the crystallization process. Therefore, we study crystallization from aqueous lactose solutions by inline monitoring of concentrations of α- and β-lactose via attenuated total reflection Fourier-transform spectroscopy. By implementing a linear cooling profile of 9 K/h to a minimum temperature of 10°C, we measured a remarkable increase in β/α ratio, reaching a maximum of 2.19. This ratio exceeds the equilibrium level by 36%. However, when the same cooling profile was applied to a minimum temperature of 25°C, the deviation was significantly lower, with a maximum β/α ratio of 1.72, representing only an 8% deviation from equilibrium. We also performed a theoretical assessment of the influence of process parameters on crystallization kinetics. We conclude that mutarotation needs to be taken into consideration for efficient crystallization control if the crystal surface area and supersaturation are sufficiently high.

Do metastable polymorphs always grow faster? Measuring and comparing growth kinetics of three polymorphs of tolfenamic acid

The phenomenon of molecular crystal polymorphism is of central importance for all those industries that rely on crystallisation for the manufacturing of their products. Computational methods for the evaluation of thermodynamic properties of polymorphs have become incredibly accurate and a priori prediction of crystal structures is becoming routine. The computational study and prediction of the kinetics of crystallisation impacting polymorphism, however, have received considerably less attention despite their crucial role in directing crystallisation outcomes. This is mainly due to the lack of available experimental data, as nucleation and growth kinetics of polymorphs are generally difficult to measure. On the one hand, the determination of overall nucleation and growth kinetics through batch experiments suffers from unwanted polymorphic transformations or the absence of experimental conditions under which several polymorphs can be nucleated. On the other hand, growth rates of polymorphs obtained from measurements of single crystals are often only recorded along a few specific crystal dimensions, thus lacking information about overall growth and rendering an incomplete picture of the problem. In this work, we measure the crystal growth kinetics of three polymorphs (I, II and IX) of tolfenamic acid (TFA) in isopropanol solutions, with the intention of providing a meaningful comparison of their growth rates. First, we analyse the relation between the measured growth rates and the crystal structures of the TFA polymorphs. We then explore ways to compare their relative growth rates and discuss their significance when trying to determine which polymorph grows faster. Using approximations for describing the volume of TFA crystals, we show that while crystals of the metastable TFA-II grow the fastest at all solution concentrations, crystals of the metastable TFA-IX become kinetically competitive as the driving force for crystallisation increases. Overall, both metastable forms TFA-II and TFA-IX grow faster than the stable TFA-I.

Online 3D Characterization of Micrometer-Sized Cuboidal Particles in Suspension

Characterization of particle size and shape is central to the study of particulate matter in its broadest sense. Whilst 1D characterization defines the state of the art, the development of 2D and 3D characterization methods has attracted increasing attention, due to a common need to measure particle shape alongside size. Herein, ensembles of micrometer-sized cuboidal particles are studied, for which reliable sizing techniques are currently missing. Such particles must be characterized using three orthogonal dimensions to completely describe their size and shape. To this end, the utility of an online and in-flow multiprojection imaging tool coupled with machine learning is experimentally assessed. Central to this activity, a methodology is outlined to produce micrometer-sized, non-spherical analytical standards. Such analytical standards are fabricated using photolithography, and consist of monodisperse micro-cuboidal particles of user-defined size and shape. The aforementioned activities are addressed through an experimental framework that fabricates analytical standards and subsequently uses them to validate the performance of our multiprojection imaging tool. Significantly, it is shown that the same set of data collected for particle sizing can also be used to estimate particle orientation in flow, thus defining a rapid and robust protocol to investigate the behavior of dilute particle-laden flows.

Complex Growth of Benzamide Form I: Effect of Additives, Solution Flow, and Surface Rugosity

Understanding crystal growth kinetics is of great importance for the development and manufacturing of crystalline molecular materials. In this work, the impact of additives on the growth kinetics of benzamide form I (BZM-I) crystals has been studied. Using our newly developed crystal growth setup for the measurement of facet-specific crystal growth rates under flow, BZM-I growth rates were measured in the presence of various additives previously reported to induce morphological changes. The additives did not have a significant impact on the growth rates of BZM-I at low concentrations. By comparison to other systems, these additives could not be described as "effective" since BZM-I showed a high tolerance of the additives' presence during growth, which may be a consequence of the type of growth mechanisms at play. Growth of pure BZM-I was found to be extremely defected, and perhaps those defects allow the accommodation of impurities. An alternative explanation is that at low additive concentrations, solid solutions are formed, which was indeed confirmed for a few of the additives. Additionally, the growth of BZM-I was found to be significantly affected by solution dynamics. Changes in some facet growth rates were observed with changes in the orientation of the BZM-I single crystals relative to the solution flow. Of the two sets of facets involved in the growth of the width and length of the crystal, the {10l̅} facets were found to be greatly affected by the solution flow while the {011} facets were not affected at all. Computational fluid dynamics simulations showed that solute concentration has higher gradients at the edges of the leading edge {10l̅} facets, which can explain the appearance of satellite crystals. {10l̅} facets were found to show significant structural rugosity at the molecular level, which may play a role in their mechanism of growth. The work highlights the complexities of measuring crystal growth data of even simple systems such as BZM-I, specifically addressing the effect of additives and fluid dynamics.

Potential of Deep Learning Methods for Deep Level Particle Characterization in Crystallization

Crystalline particle properties, which are defined throughout the crystallization process chain, are strongly tied to the quality of the final product bringing along the need of detailed particle characterization. The most important characteristics are the size, shape and purity, which are influenced by agglomeration. Therefore, a pure size determination is often insufficient and a deep level evaluation regarding agglomerates and primary crystals bound in agglomerates is desirable as basis to increase the quality of crystalline products. We present a promising deep learning approach for particle characterization in crystallization. In an end-to-end fashion, the interactions and processing steps are minimized. Based on instance segmentation, all crystals containing single crystals, agglomerates and primary crystals in agglomerates are detected and classified with pixel-level accuracy. The deep learning approach shows superior performance to previous image analysis methods and reaches a new level of detail. In experimental studies, L-alanine is crystallized from aqueous solution. A detailed description of size and number of all particles including primary crystals is provided and characteristic measures for the level of agglomeration are given. This can lead to a better process understanding and has the potential to serve as cornerstone for kinetic studies.