Crystal Growth, Dissolution, and Agglomeration Kinetics of Sodium Chlorate

Chiral Flipping in Viedma Deracemization

Understanding deracemization is crucial for progress in chiral chemistry, especially for improving separation techniques. Here, we first report the phenomenon of chiral flipping (or reverse deracemization) in a chiral material (i.e., sodium chlorate crystals) during Viedma deracemization, employing a small-volume reactor system for precise analysis. We observe considerable chiral flipping, influenced by the initial imbalance in the numbers of L- and D-form particles. We developed a simple probabilistic model to further elucidate this behavior. We find that the fluctuation in the populations of chiral crystal particles resulting from their random dissolution and regeneration is the key factor behind chiral flipping. This study not only brings to light this intriguing observation of chiral flipping but also contributes to the enhancement of deracemization techniques.

Exact and Ubiquitous Condition for Solid-State Deracemization in Vitro and in Nature

Solid-state deracemization is the amplification of an enantiomeric excess in suspensions of conglomerate-forming chiral compounds. Although numerous chemical and biochemical compounds deracemize, its governing mechanism has remained elusive. We introduce a novel formulation of the classical population-based model of deracemization through temperature cycles to prove that suspensions deracemize whenever a simple and ubiquitous condition is met: crystal dissolution must be faster than crystal growth. Such asymmetry is a known principle of crystallization, hence explaining the generality of deracemization. Through both experiments and a theoretical analysis, we demonstrate that this condition applies even for very small temperature cycles and for random temperature fluctuations. These findings establish solid-state deracemization as an attractive route to the manufacture of enantiopure products and as a plausible pathway toward the emergence of homochirality in nature.

Size-Independent Nucleation and Growth Model of Potassium Sulfate from Supersaturated Solution Produced by Stirred Crystallization

This paper explores the kinetics of the crystallization of potassium sulfate in a stirred bed crystallizer through experimental investigation. Employing classical nucleation theory, the homogeneous and heterogeneous nucleation mechanisms of potassium sulfate were investigated. The induction time and critical nucleation parameters, including the surface tension (γ), critical nucleation radius (r*), critical nucleation free energy (ΔG*), and critical nucleation molecule number (i*), were meticulously determined under varying temperatures and supersaturation ratios. The experimental findings revealed that as the temperature and supersaturation ratio increased, the induction time, critical nucleation free energy, critical nucleation radius, and critical molecule number decreased whereas the nucleation rate increased. The crystalline shape remains relatively unaltered with respect to temperature and supersaturation ratio, yet the particle size (D10, D50, D90) increases as the supersaturation and temperature increase. The variations in the measured nucleation parameters align well with the predictions of classical nucleation theory. Furthermore, the kinetic equations of crystal nucleation and the growth rate in a stirred crystallization system were fitted using population balance equations. The results demonstrate that the growth rate increases with increasing supersaturation and stirring rates. Additionally, the effects of the parameters in the nucleation rate equation suggested that the suspension density exerted the greatest influence, followed by the supersaturation ratio and stirring rate. This extensive research provides invaluable theoretical guidance for optimizing the crystallization process and designing industrial crystallizers.

A Novel Simulated Moving Plug Flow Crystallizer (SM-PFC) for Addressing the Encrustation Problem: Simulation-Based Studies on Cooling Crystallization

The plug flow crystallizer (PFC) is a promising candidate in the move toward adoption of continuous manufacturing in the pharmaceutical industry. However, a major concern for the smooth running of PFCs is the encrustation or fouling which can result in blockage of the crystallizer or unplanned shutdown of the process. In order to address this problem, simulation studies are carried out to explore the feasibility of a novel simulated-moving PFC (SM-PFC) configuration that can run uninterrupted in the presence of heavy fouling without compromising the desired critical quality attributes of the product crystals. The key concept of the SM-PFC lies in the arrangement of the crystallizer segments where a fouled segment is isolated, while a clean segment is simultaneously brought online avoiding fouling-related issues and maintaining uninterrupted operation. The inlet and outlet ports are also changed appropriately so that the whole operation mimics the movement of the PFC. The simulation results suggest that the proposed PFC configuration could be a potential mitigating approach for the encrustation problem enabling continuous operation of the crystallizer in the presence of heavy fouling while maintaining the product specifications.

Applications of Artificial Intelligence and Machine Learning Algorithms to Crystallization

Artificial intelligence and specifically machine learning applications are nowadays used in a variety of scientific applications and cutting-edge technologies, where they have a transformative impact. Such an assembly of statistical and linear algebra methods making use of large data sets is becoming more and more integrated into chemistry and crystallization research workflows. This review aims to present, for the first time, a holistic overview of machine learning and cheminformatics applications as a novel, powerful means to accelerate the discovery of new crystal structures, predict key properties of organic crystalline materials, simulate, understand, and control the dynamics of complex crystallization process systems, as well as contribute to high throughput automation of chemical process development involving crystalline materials. We critically review the advances in these new, rapidly emerging research areas, raising awareness in issues such as the bridging of machine learning models with first-principles mechanistic models, data set size, structure, and quality, as well as the selection of appropriate descriptors. At the same time, we propose future research at the interface of applied mathematics, chemistry, and crystallography. Overall, this review aims to increase the adoption of such methods and tools by chemists and scientists across industry and academia.