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Crystals
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Crystallization Process and Simulation Calculation, Third Edition
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Crystallization Process and Simulation Calculation, Third Edition

A special issue of Crystals (ISSN 2073-4352). This special issue belongs to the section "Industrial Crystallization".

Deadline for manuscript submissions: 30 May 2025 | Viewed by 7510

Special Issue Editors

Dr. Mingyang Chen

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Guest Editor
State Key Laboratory of Chemical Engineering, Tianjin University, School of Chemical Engineering and Technology, Tianjin 300072, China
Interests: crystallization process; spherical crystallization; nucleation; crystal growth; crystal agglomeration; simulation; particle engineering
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jinbo Ouyang

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Guest Editor
State Key Laboratory of Nuclear Resources and Environment, East China Institute of Technology, Fuzhou, China
Interests: drug crystal design; isolation adsorbent material design; wastewater treatment
Special Issues, Collections and Topics in MDPI journals
Dr. Kangli Li

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Guest Editor
Institute of Shaoxing, Tianjin University, Shaoxing 312300, China
Interests: polymorphism; nucleation; crystal growth; industrial crystallization; crystal engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Mengmeng Sun

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Guest Editor
School of Chemical Engineering and Technology, Hebei University of Technology, Tianjin 300401, China
Interests: particle engineering; oiling-out crystallization; spherical crystallization; nucleation; crystal growth; molecular dynamic simulation
Dr. Yu Liu

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Guest Editor
School of Chemical Engineering, Shenyang University of Chemical Technology, Shenyang 110142, China
Interests: nucleation mechanism; polymorph nucleation; crystal growth; crystal engineering

Special Issue Information

Dear Colleagues,

Following the remarkable success of the first edition and the second edition of this topic, entitled “Crystallization Process and Simulation Calculation” (https://www.mdpi.com/journal/crystals/special_issues/crystallization_process2; https://www.mdpi.com/journal/crystals/special_issues/9LVS3K8K6Q ), we are pleased to announce this third edition.

Crystallization is a crucial unit operation where nucleation, growth, agglomeration, and breakage are regulated to produce high-quality crystals and achieve efficient separation and purification. In recent years, there have been notable advancements in crystallization processes. Process intensification techniques such as ultrasound and wet grinding have been employed to improve the nucleation and breakage processes, thereby preparing ultrafine powders and nanoparticles with different morphologies. Co-crystallization, as a means of crystal engineering, is also widely used to modify crystal structure and morphology, aiming to enhance the physicochemical properties and powder performance of crystal products. Spherical crystallization technology is utilized to generate spherical crystalline particles through crystal growth or agglomeration processes. Continuous crystallization has also gained increasing interest due to its high productivity and consistency in product quality. These studies offer innovative strategies and methods to design processes and control crystallization, ensuring the desired quality attributes and predictable performance of the product. Given the complex nature of crystallization processes, characterized by strong coupling, nonlinearity and large lagging, the challenges that remain include the design of a robust and well-characterized process for efficient crystallization and the production of high-quality crystalline products. The development of process analytical technology that can provide rapid and precise inline or online measurements is critical for designing and controlling crystallization processes. Simulation technology, such as molecular dynamics simulation and hydrodynamics simulation technology, provide insights into the process at multiple scales over time or location. These experimental and simulation tools can greatly enhance the understanding and optimization of crystallization processes.

This Special Issue, “Crystallization Processes and Simulation Calculations, Third Edition”, serves to provide a platform for researchers to report results and findings regarding crystallization process technologies, simulation and process analytical technologies, and relevant crystallization studies.

Dr. Mingyang Chen
Prof. Dr. Jinbo Ouyang
Dr. Kangli Li
Dr. Mengmeng Sun
Dr. Yu Liu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Crystals is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2100 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • nucleation and growth
  • agglomeration and breakage
  • process analytical technology
  • process intensification
  • continuous crystallization
  • spherical crystallization
  • co-crystallization
  • molecular dynamics simulation
  • hydrodynamics simulation

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Related Special Issues

Published Papers (8 papers)

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Research

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20 pages, 3502 KiB  
Article
Neural Network-Based Kinetic Model for Antisolvent Crystallization of Benzophenone: Construction, Validation, and Mechanistic Interpretation
by Yafei Dong, Shutian Xuanyuan, Chuang Xie, Ying Sun, Xiaomeng Zhou and Yuanhang Wang
Crystals 2025, 15(5), 464; https://doi.org/10.3390/cryst15050464 - 15 May 2025
Viewed by 98
Abstract
The emergence of artificial neural networks and the widespread application of process analytical technology (PAT) have founded a robust foundation for neural network applications in crystallization research. This study investigated benzophenone antisolvent crystallization kinetics through online monitoring of the crystallization process via PAT [...] Read more.
The emergence of artificial neural networks and the widespread application of process analytical technology (PAT) have founded a robust foundation for neural network applications in crystallization research. This study investigated benzophenone antisolvent crystallization kinetics through online monitoring of the crystallization process via PAT tools and kinetics fitting via neural networks. The antisolvent crystallization process was simulated by integrating network-based kinetics with population balance. The findings suggest that benzophenone exhibits size-independent growth in water–methanol systems. The neural network-based model demonstrates improved performance (a consistent 50 ± 5% enhancement in prediction accuracy (R2) over empirical kinetic models) in predicting crystallization kinetics. Furthermore, the network-based process model achieved remarkable agreement with the experimental crystal size distribution, showing smaller deviation (1.1%), less than that of traditional empirical models (5.29%). This work proposed a robust crystallization process model combining PAT tools and artificial neural networks, enabling rapid crystallization kinetics determination and accurate process simulations. Full article
(This article belongs to the Special Issue Crystallization Process and Simulation Calculation, Third Edition)
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15 pages, 7828 KiB  
Article
The Effect of Carbon on the Crystallization and Electrochemical Behavior of Portland Cement
by Jeunghyeuon Cho, Byung-Hyun Shin, Miyoung You, Seongjun Kim, Jinyong Park, Jung-Woo Ok, Jonggi Hong, Taekyu Lee, Jong-Seong Bae, Pungkeun Song and Jang-Hee Yoon
Crystals 2025, 15(2), 189; https://doi.org/10.3390/cryst15020189 - 17 Feb 2025
Cited by 1 | Viewed by 486
Abstract
Cement is one of the most widely used structural materials and serves as the primary component of concrete. Among the various types, Portland cement is the most commonly utilized due to its excellent strength and corrosion resistance. Recently, efforts have been made to [...] Read more.
Cement is one of the most widely used structural materials and serves as the primary component of concrete. Among the various types, Portland cement is the most commonly utilized due to its excellent strength and corrosion resistance. Recently, efforts have been made to incorporate various functional additives into Portland cement to impart new properties; however, studies on the resulting changes in corrosion resistance remain insufficient. While the existing research has largely focused on impurities in cement, systematic studies on the effects of interstitial elements on the crystallization and electrochemical behavior of cement are scarce. This study investigates the influence of carbon (C) addition on the crystallographic structure and electrochemical properties of Portland cement. C concentrations from 0 to 10 wt.% were added. The microstructure and crystallographic structure with different C concentrations were analyzed using FE-SEM and XRD. The bonding characteristics of cement components according to the C composition were measured using XPS, hardness was measured using Vickers hardness, and electroconductivity was calculated using a 4-point probe. The electrochemical behavior was evaluated according to the ASTM G 61 standards through OCP, EIS, and potentiodynamic polarization tests. As the composition of C increased, the number of voids and cracks decreased, while the electrical conductivity increased from 1.7 × 10−4 to 4.3 × 10−2. Additionally, the resistance tended to decrease with the increase in C composition. Therefore, the concentration of C needs to be controlled depending on the required function of the cement. Full article
(This article belongs to the Special Issue Crystallization Process and Simulation Calculation, Third Edition)
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33 pages, 53585 KiB  
Article
Unraveling the Determinant Mechanisms in Flow-Mediated Crystal Growth and Phase Behaviors
by L. Connor Willis, Tesia D. Janicki, Rekha R. Rao and Z. Leonardo Liu
Crystals 2025, 15(2), 157; https://doi.org/10.3390/cryst15020157 - 4 Feb 2025
Viewed by 716
Abstract
To uncover the critical mechanisms responsible for mesoscopic level development during flow-mediated crystal growth, we develop a semi-two-way hydrodynamic coupled structural phase-field crystal formalism (HXPFC-s2). The new formalism, inspired by previous attempts at coupling hydrodynamic and phase-field crystal (PFC) equations, allows for studying [...] Read more.
To uncover the critical mechanisms responsible for mesoscopic level development during flow-mediated crystal growth, we develop a semi-two-way hydrodynamic coupled structural phase-field crystal formalism (HXPFC-s2). The new formalism, inspired by previous attempts at coupling hydrodynamic and phase-field crystal (PFC) equations, allows for studying mesoscopic flow-mediated crystallization at diffusive timescales pertinent to industrial applications. Unlike previous efforts, the devised coupling to the structural PFC (XPFC) equations allows generalization to more complex crystal structures through explicit parameterization of the direct correlation function (DCF). Utilizing the HXPFC-s2 formalism, we seek to uncover the determinant physical mechanisms in crystallization under simple shear flows by comparing temperature-driven crystallization to flow-mediated crystallization under varying flow-strengths. Parallels and deviations of under-cooling and flow-strength effects on crystal growth are drawn using the crystal cluster-size and system ordering time evolutions. In doing so, we identify scaling behaviors with a Peclet-like number, Pe∼, a critical Peclet-like number, Pe∼*, and flow-field-crystal plane-dependent interactions. Our findings may be relevant for controlling crystal growth and phase behaviors in flow applications. Full article
(This article belongs to the Special Issue Crystallization Process and Simulation Calculation, Third Edition)
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12 pages, 4780 KiB  
Article
Mathematical Modeling to Predict the Formation of Micrometer-Scale Crystals Using Reverse Anti-Solvent Crystallization
by Jianhua Wang, Fawei Wang, Xu Wen, Yankang Zhang, Jiapeng Wang and Yucun Liu
Crystals 2025, 15(2), 145; https://doi.org/10.3390/cryst15020145 - 29 Jan 2025
Viewed by 859
Abstract
The reverse addition process in anti-solvent crystallization is safer and more efficient than sieving when dealing with energetic compounds. A new mathematical model has been developed to understand the crystal size mechanism during the reverse addition of solvent in a binary system. This [...] Read more.
The reverse addition process in anti-solvent crystallization is safer and more efficient than sieving when dealing with energetic compounds. A new mathematical model has been developed to understand the crystal size mechanism during the reverse addition of solvent in a binary system. This model incorporates droplet dynamics, distribution moments, and mass balance constraints. It can be used to predict the appropriate crystal size for designing explosive recipes with a desired particle size distribution to maximize energy output. The model was validated by conducting reverse-addition crystallization of sodium chloride in a deionized water/ethanol binary system at temperatures ranging from 10 to 50 degrees Celsius. The predicted results closely matched the experimental findings, which were confirmed using a Laser Particle Size Analyzer and Electron Microscope Scanning. Full article
(This article belongs to the Special Issue Crystallization Process and Simulation Calculation, Third Edition)
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8 pages, 2174 KiB  
Article
Effect of Pyrolysis Temperature on Microwave Heating Properties of Oxidation-Cured Polycarbosilane Powder
by Chang-Hun Hwang, Jong-Ha Beak, Sang-In Kim and Se-Yun Kim
Crystals 2024, 14(12), 1080; https://doi.org/10.3390/cryst14121080 - 14 Dec 2024
Cited by 1 | Viewed by 713
Abstract
Silicon carbide (SiC) has excellent mechanical and chemical properties and is used in a wide range of applications. It has the characteristic of rapidly heating up to several hundred degrees within one minute when irradiated with microwave radiation at 2.45 GHz. In this [...] Read more.
Silicon carbide (SiC) has excellent mechanical and chemical properties and is used in a wide range of applications. It has the characteristic of rapidly heating up to several hundred degrees within one minute when irradiated with microwave radiation at 2.45 GHz. In this study, we investigated the oxidation curing process and microwave heating properties of polycarbosilane (PCS). A PCS disk-shaped green body was fabricated via uniaxial pressure molding. Silicon carbide was prepared by varying the pyrolysis temperature, and the heating characteristics of the microwaves were evaluated. The results showed that the samples pyrolyzed at 1300 °C after oxidation curing for 2 h at 180 °C rapidly heated up to 802 °C within 1 min, and the temperature remained constant for 120 min. The maximum temperature of the samples pyrolyzed at 1500 °C was relatively low, but the rate of heating was the highest. The microstructures and crystal structures of the microwaves as a function of the pyrolysis temperature were investigated. Full article
(This article belongs to the Special Issue Crystallization Process and Simulation Calculation, Third Edition)
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12 pages, 4142 KiB  
Article
Batch Cooling Crystallization of a Model System Using Direct Nucleation Control and High-Performance In Situ Microscopy
by Josip Budimir Sacher, Nenad Bolf and Marko Sejdić
Crystals 2024, 14(12), 1079; https://doi.org/10.3390/cryst14121079 - 13 Dec 2024
Viewed by 1177
Abstract
The aim of this study was to investigate the use of automated high performance in situ microscopy (HPM) for monitoring and direct nucleation control (DNC) during cooling crystallization. Compared to other techniques, HPM enables the detection of small crystals in the range of [...] Read more.
The aim of this study was to investigate the use of automated high performance in situ microscopy (HPM) for monitoring and direct nucleation control (DNC) during cooling crystallization. Compared to other techniques, HPM enables the detection of small crystals in the range of 1 to 10 μm. Therefore, a novel DNC-controlled variable was investigated to determine the potential improvement of the method. The laboratory system and process control software were developed in-house. A well-studied crystallization model system, the seeded batch cooling crystallization of α-glycine from water, was investigated under normal conditions and temperatures below 60 °C. It was postulated that length-weighted edge-to-edge counts in the range of 1 to 10 μm would be most sensitive to the onset of secondary nucleation and are therefore, used as a control variable. Linear cooling experiments were conducted to determine the initial setpoint for the DNC experiments. Three DNC experiments were then performed with different setpoints and an upper and lower counts limit. It was found that the DNC method can be destabilized with a low setpoint and narrow counts limits. In addition, the new controlled variable is highly sensitive to the formation of bubbles at the microscope window and requires careful evaluation. To address the advantages of the DNC method, an additional linear cooling experiment of the same duration was performed, and it was found that the DNC method resulted in a larger average crystal size. Overall, it can be concluded that the HPM method is suitable for DNC control and could be improved by modifying the image processing algorithm. Full article
(This article belongs to the Special Issue Crystallization Process and Simulation Calculation, Third Edition)
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14 pages, 2173 KiB  
Article
Crystallization Kinetics of Tacrolimus Monohydrate in an Ethanol–Water System
by Suoqing Zhang, Jixiang Zhao, Ming Kong, Jiahui Li, Mingxuan Li, Miao Ma, Li Tong, Tao Li and Mingyang Chen
Crystals 2024, 14(10), 849; https://doi.org/10.3390/cryst14100849 - 28 Sep 2024
Viewed by 958
Abstract
Nucleation and growth during the crystallization process are crucial steps that determine the crystal structure, size, morphology, and purity. A thorough understanding of these mechanisms is essential for producing crystalline products with consistent properties. This study investigates the solubility of tacrolimus (FK506) in [...] Read more.
Nucleation and growth during the crystallization process are crucial steps that determine the crystal structure, size, morphology, and purity. A thorough understanding of these mechanisms is essential for producing crystalline products with consistent properties. This study investigates the solubility of tacrolimus (FK506) in an ethanol–water system (1:1, v/v) and examines its crystallization kinetics using batch crystallization experiments. Initially, the solubility of FK506 was measured, and classical nucleation theory was employed to analyze the induction period to determine interfacial free energy (γ) and other nucleation parameters, including the critical nucleus radius (r*), critical free energy (∆G*), and the molecular count of the critical nucleus (i*). Crystallization kinetics under seeded conditions were also measured, and the parameters of the kinetic model were analyzed to understand the effects of process states such as temperature on the crystallization process. The results suggested that increasing temperature and supersaturation promotes nucleation. The surface entropy factor (f) indicates that the tacrolimus crystal growth mechanism is a two-dimensional nucleation growth. The growth process follows the particle size-independent growth law proposed by McCabe. The estimated kinetic parameters reveal the effects of supersaturation, temperature, and suspension density on the nucleation and growth rates. Full article
(This article belongs to the Special Issue Crystallization Process and Simulation Calculation, Third Edition)
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38 pages, 23114 KiB  
Review
Mathematical Modeling of Properties and Structures of Crystals: From Quantum Approach to Machine Learning
by Grzegorz Matyszczak, Christopher Jasiak, Gabriela Rusinkiewicz, Kinga Domian, Michał Brzozowski and Krzysztof Krawczyk
Crystals 2025, 15(1), 61; https://doi.org/10.3390/cryst15010061 - 9 Jan 2025
Viewed by 1816
Abstract
The crystalline state of matter serves as a reference point in the context of studies of properties of a variety of chemical compounds. This is due to the fact that prepared crystalline solids of practically useful materials (inorganic or organic) may be utilized [...] Read more.
The crystalline state of matter serves as a reference point in the context of studies of properties of a variety of chemical compounds. This is due to the fact that prepared crystalline solids of practically useful materials (inorganic or organic) may be utilized for the thorough characterization of important properties such as (among others) energy bandgap, light absorption, thermal and electric conductivity, and magnetic properties. For that reason it is important to develop mathematical descriptions (models) of properties and structures of crystals. They may be used for the interpretation of experimental data and, as well, for predictions of properties of novel, unknown compounds (i.e., the design of novel compounds for practical applications such as photovoltaics, catalysis, electronic devices, etc.). The aim of this article is to review the most important mathematical models of crystal structures and properties that vary, among others, from quantum models (e.g., density functional theory, DFT), through models of discrete mathematics (e.g., cellular automata, CA), to machine learning (e.g., artificial neural networks, ANNs). Full article
(This article belongs to the Special Issue Crystallization Process and Simulation Calculation, Third Edition)
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