Crystallization and Precipitation

Crystallization, or crystallisation, is the process of atoms or molecules arranging into a well-defined, rigid crystal lattice in order to minimize their energetic state. The smallest entity of a crystal lattice is called a unit cell, which can accept atoms or molecules to grow a macroscopic crystal. During crystallization, atoms and unit cells bind together with well-defined angles to form a characteristic crystal shape with smooth surfaces and facets.

Crystallization can occur in nature but also has broad industrial application as a separation and purification step in the pharmaceutical, chemical and food industries.

Crystallization touches every aspect of our lives from the foods we eat and the medicines we take to the fuels we use to power our communities.

• The majority of agrochemical and pharmaceutical products go through many crystallization steps during their development and manufacture.
• In the food industry, ingredients such as lactose and lysine are delivered as crystals to humans and animals for consumption.
• A major safety concern for the petrochemical industry is the unwanted crystallization of gas hydrates in deep sea pipelines.

This is why scientists and engineers in multiple industries around the world are required to understand, optimize and control crystallization processes every day. Effective and efficient crystallization ensures high quality and safe production.

Product Quality. Crystallization is important in product quality because it influences particle size, purity and product yield. For example, in the pharmaceutical industry, the crystallization of an active pharmaceutical ingredient (API) needs to be strictly controlled to meet desired product specifications.

Process Quality. Crystallization also influences process quality, such as drying, flowability and scalability. For example, a wide particle size distribution of the API crystallization process could cause slow filtration and inefficient drying, thus creating a bottleneck in the entire manufacturing process.

While crystals have many important attributes, the crystal size distribution likely has the greatest impact on the quality and effectiveness of the final product (and the process needed to deliver it). Crystal size and shape directly influence key steps downstream of the crystallizer, with filtration and drying performance being particularly susceptible to changes in these important attributes. Similarly, the final crystal size can also directly influence the quality of the final product. In a pharmaceutical compound, bioavailability and efficacy are often related to particle size with smaller particles frequently desired for their enhanced solubility and dissolution characteristics.

Crystal size distribution can be optimized and controlled by carefully choosing the correct crystallization conditions and process parameters. Understanding how process parameters influence key transformations, such as nucleation, growth and breakage, allows scientists to develop and manufacture crystals that will have the desired attributes and be efficient to bring to market.

While the method chosen to crystallize product can vary based on several factors, there are six common steps for crystallization to occur. Scientists use solubility curves to create a framework to develop the desired crystallization process. The solubility curves plot temperature vs. solubility to determine the factors for the crystallization process. One factor is the choice of appropriate solvent to use in Step 1 of the crystallization process.

An appropriate solvent is important because crystallization is generally achieved by reducing the product's solubility in a saturated starting solution. When choosing a solvent, some considerations include:

• Solubility: How much solute can be dissolved?
• Safety and environmental impact: How practical is the solvent to handle?
• Disposal fees: How is the solvent discarded?

In addition to the solvent, the temperature is also an important factor to determine if crystallization will occur. There is a given temperature where the maximum quantity of product can be dissolved in a solvent. When this temperature is reached, the solution is saturated and insoluble impurities may be filtered from the hot solution.

Crystallization generally occurs by reducing the solubility of a solute in a solution by one or a combination of these four methods. When solubility is reduced, the solution will become supersaturated. Supersaturation is the driving force for crystal nucleation and growth. It is a vital crystallization step because it determines crystal product factors such as size distribution and phase. The choice of crystallization method depends on the equipment available for crystallization, the objectives of the crystallization process and the solubility and stability of the solute in the chosen solvent.

• Cooling crystallization: When dissolved at high temperature, a large amount of solute can initiate crystallization when the solution undergoes controlled cooling.
• Antisolvent addition: When antisolvent is added in a controlled fashion to the solution, it reduces the solubility of the solution and increases the supersaturation that is required for crystallization.
• Evaporation: When solubility is neither a function of temperature or solvent composition, evaporative crystallization is required. The highly soluble solution is heated to boiling point, and the solvent is removed through evaporation. This leads to crystallization. The final product properties are determined based on the rate of evaporation.
• Reactive (precipitation): When the product is obtained through a chemical reaction such as acid/base neutralization, crystallization can occur through reaction or precipitation.

As solubility is reduced, a point is reached where crystals will nucleate and then grow. The point where crystal nucleation occurs is called the metastable limit. Supersaturation is the difference between the actual concentration and the solubility concentration at a given temperature. When the product is crystallized, highly pure product crystals form and impurities remain in solution. Process parameters such as incorporating a seeding strategy can be used to control supersaturation, improve batch consistency and optimize the product formed.

Based on solubility, one or more crystallization methods (cooling, antisolvent, evaporative or reactive crystallization) are performed to reach high product yield. In order to design efficient crystallization processes, a control over the degree of supersaturation and an understanding of what particle mechanism crystals go through is needed.

For the majority of crystallization processes, the solid, purified particles are the desired product. The crystals need to be separated from the mother liquor by filtration. In order to obtain the product, the requirements for an efficient filtration process are:

• Crystal suspension for filtering
• Vacuum trap or pressure used as the driving force
• Filtration equipment such as filter paper, a porous plate and a clean flask

Finally, the purified crystal product is dried via atmospheric or vacuum method. The method used will depend on factors such as the solvent type and thermal and mechanical stability of the API.

Particle size and count, as well as chemical composition, are important to characterize effectively for the successful development, transfer and operation of processes in numerous industries.

Traditional offline particle size analyzers are used in the quality control laboratory to measure particle properties with accuracy; however, care must be taken to prepare the sample to allow for a consistent measurement. The time delay and potential for particle changes between sampling and analysis make the traditional particle size analysis approach challenging for process optimization and improvement.

In-process measurement instruments offer an opportunity to track how particle size, count, and composition change directly in the process in real time. By understanding how particles behave from the beginning until the end of a process, and by comparing particle changes to process parameters, scientists can develop a deep understanding of particle systems. This allows for the creation of fit-for-purpose particles and monitoring of processes to be optimized using evidence-based methods and for troubleshooting to be executed during production.

In-process particle measurement complements traditional particle size analysis by providing extra information about how particles actually behave naturally in process. If a quality control lab reports a deviation from specification, in-process particle measurement can be used to perform a root cause analysis. Likewise, in-process particle measurement can predict when a process will move out of specification and can help identify when a sample should be taken from a process for offline analysis and quality verification. By combining in-process particle measurement for understanding, optimizing and troubleshooting processes with traditional particle size analysis for quality control, scientists can develop particle processes with higher quality in less time at a lower total cost.

In this example, the cooling rate at the end of the batch induces secondary nucleation resulting in the formation of many fine particles using particle size analyzers. An increase in cooling rate generates supersaturation faster, which is consumed by nucleation rather than growth. Careful control of cooling rate is critical to ensure the desired crystal size distribution specification can be achieved.

The crystal size distribution of ice plays a vital role in the taste and consistency of ice cream, with crystals smaller than 50 μm being better than crystals larger than 100 μm. For agrochemicals, it is vital to ensure that particles are small enough to be sprayed without blocking nozzles while large enough not to drift into neighboring fields.

While it is often a challenge to control crystal size distribution across scales, an opportunity exists to understand crystallization processes in order to deliver an optimized size, yield and shape distribution that will ensure a cost-effective process with the highest possible quality.

By overlaying process conditions onto in-situ particle analysis, scientists can easily understand how process parameters impact concentration, size, shape and structure to make better decisions, eliminate process risks and solve problems – faster.

Process parameters like temperature, stirring and dosing rate have a direct impact on the product and process quality of particle systems. EasyMax, OptiMax, RC1 and RX-10 ensure precise control and recording of process conditions for true particle engineering.

• All process data is recorded whether you preprogram recipe steps such as temperature or dosing ramps, or make adjustments during the process
• Information from process analytical technology (PAT) tools, such as ReactIR, ReactRaman, EasyViewer and/or ParticleTrack, can be overlaid on process parameter trends for quick and easy understanding of particle mechanisms
• Precise execution allows chemists and engineers to run unattended processes with confidence

Particle size, shape and concentration are critical pieces of information at every stage or scale during a crystallization process and hence make critical quality attributes (CQA). Particle size analyzers quickly visualize and quantify particles and critical particle mechanisms for successful crystallization process development.

• Particle properties and particle mechanisms are recorded for review and analysis at all times, even if scientists cannot be in the lab.
• Interoperability between automated reactors and ParticleTrack and EasyViewer enable scientists to set up feedback control loops for particle size or count controlled cooling, or to set antisolvent dosing rates to minimize undesired particle populations, such as excessive fines.
• The intuitive "Start Experiment Wizard" makes it easy for every scientist to quickly collect high-quality particle data.

Solution concentration, supersaturation and crystal form (polymorph) are often connected and largely determine the success of crystallization process development.

ReactIR and ReactRaman systematically analyze solution and particle composition to achieve the desired process endpoint every time.

• Solution composition and particle unit cell configuration are systematically analyzed, recorded and visualized in real-time.
• The combination of spectroscopic PAT tools, like ReactIR and ReactRaman, with automated reactors enable scientists to make supersaturation a control parameter; crystallization processes run at constant supersaturation levels to achieve more uniform particle size distributions.
• Integrated One Click Analytics automatically find and display meaningful and easy-to-understand chemical and structural information for quick and evidence-based decision making.

Control over crystallizations is critical in achieving important quality attributes, and there are a significant number of interacting factors that influence crystallinity, crystal size, particle size distribution, polymorphism and more. Dynochem modeling aids in unraveling the science behind crystallizations and enables the development of understandable and practical design space for crystallization processes. Dynochem uses data from in situ analytical measurements to model solubility/supersaturation profiles as a factor of key variables, including temperature, seed loading and cooling rate. Variables associated with methods used to induce crystallization, including distillation and antisolvent addition, are quickly modeled to determine, for example, the effect of cooling profiles on the tradeoff between product purity and yield. When it comes to scaling-up crystallizations (or scaling down for troubleshooting purposes), Dynochem is used to understand and optimize physicochemical variables, including mixing, stirring speed and heat transfer, and their effects on crystallization. Dynochem modeling quickly identifies appropriate process conditions to ensure that crystallizations are well controlled and reproducible across scales.    

Discover a selection of crystallization publications below:

• Szilágyi, B., Eren, A., Quon, J. L., Papageorgiou, C. D., & Nagy, Z. K. (2022). Monitoring and digital design of the cooling crystallization of a high-aspect ratio anticancer drug using a two-dimensional population balance model. Chemical Engineering Science, 257, 117700. https://doi.org/10.1016/j.ces.2022.117700
• Salami, H., Harris, P. R., Yu, D. C., Bommarius, A. S., Rousseau, R. W., & Grover, M. A. (2022). Periodic wet milling as a solution to size-based separation of crystal products from biocatalyst for continuous reactive crystallization. Chemical Engineering Research and Design, 177, 473–483. https://doi.org/10.1016/j.cherd.2021.11.007
• Yang, L., Zhang, Y., Liu, P., Wang, C., Qu, Y., Cheng, J., & Yang, C. (2022). Kinetics and population balance modeling of antisolvent crystallization of polymorphic indomethacin. Chemical Engineering Journal, 428, 132591. https://doi.org/10.1016/j.cej.2021.132591
• Salami, H., McDonald, M. A., Bommarius, A. S., Rousseau, R. W., & Grover, M. A. (2021). In Situ Imaging Combined with Deep Learning for Crystallization Process Monitoring: Application to Cephalexin Production. Organic Process Research & Development, 25(7), 1670–1679. https://doi.org/10.1021/acs.oprd.1c00136
• Sato, Y., Liu, J., Kukor, A. J., Culhane, J. C., Tucker, J. L., Kucera, D. J., Cochran, B. M., & Hein, J. E. (2021). Real-Time Monitoring of Solid–Liquid Slurries: Optimized Synthesis of Tetrabenazine. The Journal of Organic Chemistry, 86(20), 14069–14078. https://doi.org/10.1021/acs.joc.1c01098
• Sirota, E., Kwok, T., Varsolona, R. J., Whittaker, A., Andreani, T., Quirie, S., Margelefsky, E., & Lamberto, D. J. (2021). Crystallization Process Development for the Final Step of the Biocatalytic Synthesis of Islatravir: Comprehensive Crystal Engineering for a Low-Dose Drug. Organic Process Research & Development, 25(2), 308–317. https://doi.org/10.1021/acs.oprd.0c00520
• Zhao, X., Webb, N. J., Muehlfeld, M. P., Stottlemyer, A. L., & Russell, M. W. (2021). Application of a Semiautomated Crystallizer to Study Oiling-Out and Agglomeration Events—A Case Study in Industrial Crystallization Optimization. Organic Process Research & Development, 25(3), 564–575. https://doi.org/10.1021/acs.oprd.0c00494
• Ostergaard, I., de Diego, H. L., Qu, H., & Nagy, Z. K. (2020). Risk-Based Operation of a Continuous Mixed-Suspension-Mixed-Product-Removal Antisolvent Crystallization Process for Polymorphic Control. Organic Process Research & Development, 24(12), 2840–2852. https://doi.org/10.1021/acs.oprd.0c00368
• Zhang, D., Liu, L., Xu, S., Du, S., Dong, W., & Gong, J. (2018). Optimization of cooling strategy and seeding by FBRM analysis of batch crystallization. Journal of Crystal Growth, 486, 1–9. https://doi.org/10.1016/j.jcrysgro.2017.12.046
• Jiang, M., Zhu, X., Molaro, M. C., Rasche, M. L., Zhang, H., Chadwick, K., Raimondo, D. M., Kim, K. K. K., Zhou, L., Zhu, Z., Wong, M. H., O’Grady, D., Hebrault, D., Tedesco, J., & Braatz, R. D. (2014). Modification of Crystal Shape through Deep Temperature Cycling. Industrial & Engineering Chemistry Research, 53(13), 5325–5336. https://doi.org/10.1021/ie400859d
• Kitamura, M. (2009). Strategy for control of crystallization of polymorphs. CrystEngComm, 11(6), 949. https://doi.org/10.1039/b809332f
• Chew, J. W., Chow, P. S., & Tan, R. B. H. (2007). Automated In-line Technique Using FBRM to Achieve Consistent Product Quality in Cooling Crystallization. Crystal Growth & Design, 7(8), 1416–1422. https://doi.org/10.1021/cg060822t
• Paul, E. L., Tung, H. H., & Midler, M. (2005). Organic crystallization processes. Powder Technology, 150(2), 133–143. https://doi.org/10.1016/j.powtec.2004.11.040
• Brunsteiner, M., Jones, A. G., Pratola, F., Price, S. L., & Simons, S. J. R. (2004). Toward a Molecular Understanding of Crystal Agglomeration. Crystal Growth & Design, 5(1), 3–16. https://doi.org/10.1021/cg049837m
• Jackson, K. (1984). Crystal growth kinetics. Materials Science and Engineering, 65(1), 7–13. https://doi.org/10.1016/0025-5416(84)90194-0
• Fasoli, U., & Conti, R. (1973). Crystal breakage in a mixed suspension crystallizer. Kristall Und Technik, 8(8), 931–946. https://doi.org/10.1002/crat.19730080806
• Nývlt, J. (1968). Kinetics of nucleation in solutions. Journal of Crystal Growth, 3–4, 377–383. https://doi.org/10.1016/0022-0248(68)90179-6