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 crystal lattice is called a unit cell, which can accept atoms or molecules to grow a macroscopic crystal. During crystallization, atoms and molecules bind together with well-defined angles to form a characteristic crystal shape with smooth surfaces and facets. Although crystallization can occur in nature, crystallization also has a broad industrial application as a separation and purification step in the pharmaceutical and chemical industries.

The choice of operating conditions during a crystallization process directly influences important product attributes such as crystal size, crystal shape and purity. By understanding the crystallization process and choosing the right process parameters, it is possible to repeatedly produce crystals of the correct size, shape, and purity while minimizing downstream processing issues, such as long filtration times or inadequate drying.

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. Key food ingredients, such as lactose and lysine, are manufactured using crystallization and the unwanted crystallization of gas hydrates in deep sea pipelines is a major safety concern for the petrochemical industry.

Crystallization
Crystallization is a process whereby solid crystals are formed from another phase, typically a liquid solution or melt.

Crystal
Crystal is a solid particle in which the constituent molecules, atoms, or ions are arranged in some fixed and rigid, repeating three-dimensional pattern or lattice.

Precipitation
Precipitation is the process of generating solid from the solution caused by supersaturation when the concentration of the solute is higher than its solubility. This term is usually interchangeable with "crystallization", but differs in that it can also indicate the formation of amorphous (non-crystalline) solid.

Solubility
Solubility is a measure of the amount of solute that can be dissolved in a given solvent at a given temperature

Saturated Solution
At a given temperature, there is a maximum amount of solute that can be dissolved in the solvent. At this point the solution is saturated. The quantity of solute dissolved at this point is the solubility.

Supersaturation
Supersaturation can mean the status that the actual solute concentration is higher than the equilibrium solute concentration (solubility), or the degree of how much the concentration is higher than the solubility.

Recrystallization
Recrystallization is a process in which an initially solidified crystalline material is redissolved and crystallized again to produce final product crystals of desired size, shape, purity and yield.

Protein Crystallization
Protein crystallization is a method of creating structured, ordered lattices for often-complex macromolecules. 

Crystallization occurs when the solubility of a solute in solution is reduced by some means. Common methods to reduce solubility include:

a. Cooling

b. Antisolvent Addition

c. Evaporation of Solvent

d. Precipitation Through Chemical Reaction

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.

1. Choose an appropriate solvent. Common considerations included how much solute can be dissolved (solubility) and how practical the solvent is to handle (safety)
2. Dissolve the product in the solvent by increasing the temperature until all solids of the product are dissolved. At this moment, insoluble impurities may be filtered from the hot solution
3. Reduce solubility via cooling, anti-solvent addition, evaporation or reaction. The solution will become supersaturated.
4. Crystallize the product. As solubility is reduced, a point is reached where crystals will nucleate and then grow. Highly pure product crystals should form and impurities should remain in solution.
5. Allow the system to reach equilibrium to maximize the yield of product solid.
6. Filter and dry the purified product.

Crystallization proceeds through a series of interdependent mechanisms that are each uniquely influenced by the choice of process parameters:

• Nucleation
• Growth
• Oiling Out in Crystallization
• Agglomeration
• Breakage
• Polymorphism Chemistry

These mechanisms, which are often hidden form scientists, play a dominant role in defining the outcome of a crystallization process.

While crystals have many important attributes, the crystal size distribution probably 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 from 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 often 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 the market.

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 to deliver an optimized size and shape distribution that will ensure a cost effective process with the highest possible quality.

A crystallization workstation allows scientists to obtain maximum scientific information from a single experiment in a centralized software suite. Process Analytical Technology (PAT) provides analytical information, including:

• Automated Reactors: EasyMax, OptiMax, RC1 reaction calorimeters and RX-10 provide 24/7 precise control and recording of process parameters, including the capture of crystallization enthalpies so that scientists can confidently identify Critical Process Parameters (CPPs)
  
  
• EasyViewer: A probe-based imaging tool with image analysis captures high-resolution images and calculate crystal, particle and droplet size distributions as they naturally exist in process
  
  
• ParticleTrack: Statistically, highly robust particle mechanism finger printing through particle size and count expedites successful scale-up from lab to fully ATEX rate plant scale
  
  
• ReactRaman: In-situ Raman spectroscopy provides molecular and structural information to help scientists understand complex polymorphic landscapes and select process parameter to always achieve the desired crystal form
  
  
• ReactIR: Real-time FTIR spectroscopy provides key information for concentration and supersaturation levels, metastable zone width, desupersaturation kinetics and crystallization endpoint so that crystallization processes can run repeatedly to a desired target endpoint everytime
  
  
• iC Software: Enables interoperability between all PAT tools so that all probes and reactors can communicate with each other and all analytical information (size, form, supersaturation, etc.) becomes a process control parameter

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 pre-program 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 antisolvent dosing rates to minimize undesired particle populations, such as excessive fines
  
  
• The intuitive "Start Experiment Wizard" makes it easy for every scientists 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 finds and displays meaningful and easy-to-understand chemical and structural information for quick and evidence-based decision making

Control over crystallizations is critical in achieving critical quality attributes and there are a significant number of interacting factors that influence crystallinity, affecting crystal size, particle size distribution, polymorphism, etc. 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 as are, 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.    

The design of a crystallization process that will deliver pure crystals with an optimized yield and size, involves considering a number of important elements:

• Choose an appropriate solvent
• Screen for stability and unwanted polymorphs
• Determine growth and nucleation kinetics
• Define a seeding strategy
• Optimize cooling and anti-solvent profiles
• Understand the impact of mixing and scale

Discover a selection of crystallization publications below:

• 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
• 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
• 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
• 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
• 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
• 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. ASAP. https://doi.org/10.1021/acs.joc.1c01098
• 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. ASAP. https://doi.org/10.1021/acs.oprd.1c00136
• 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
• Nonoyama, N., Hanaki, K., & Yabuki, Y. (2006). Constant Supersaturation Control of Antisolvent-Addition Batch Crystallization. Organic Process Research & Development, 10(4), 727–732. https://doi.org/10.1021/op0600052Nývlt, 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
• Jackson, K. (1984). Crystal growth kinetics. Materials Science and Engineering, 65(1), 7–13. https://doi.org/10.1016/0025-5416(84)90194-0
• Brunsteiner, M., Jones, A. G., Pratola, F., Price, S. L., & Simons, S. J. R. (2005). Toward a Molecular Understanding of Crystal Agglomeration. Crystal Growth & Design, 5(1), 3–16. https://doi.org/10.1021/cg049837m
• 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
• 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
• Kitamura, M. (2009). Strategy for control of crystallization of polymorphs. CrystEngComm, 11(6), 949. https://doi.org/10.1039/b809332f