Interfacial ice sprouting during salty water droplet freezing

Icing of seawater droplets is capable of causing catastrophic damage to vessels, buildings, and human life, yet it also holds great potential for enhancing applications such as droplet-based freeze desalination and anti-icing of sea sprays. While large-scale sea ice growth has been investigated for decades, the icing features of small salty droplets remain poorly understood. Here, we demonstrate that salty droplet icing is governed by salt rejection-accompanied ice crystal growth, resulting in freezing dynamics different from pure water. Aided by the observation of brine films emerging on top of frozen salty droplets, we propose a universal definition of freezing duration to quantify the icing rate of droplets having varying salt concentrations. Furthermore, we show that the morphology of frozen salty droplets is governed by ice crystals that sprout from the bottom of the brine film. These crystals grow until they pierce the free interface, which we term ice sprouting. We reveal that ice sprouting is controlled by condensation at the brine film free interface, a mechanism validated through molecular dynamics simulations. Our findings shed light on the distinct physics that govern salty droplet icing, knowledge that is essential for the development of related technologies.

THz to far-infrared spectra of the known crystal polymorphs of phenylalanine

There is renewed interest in the structure of the essential amino acid phenylalanine in the solid state. Three new polymorphs were found in the years 2012 to 2014. Here, we investigate the structure, stability, and energetical ordering of these phases using first-principles simulations at the level of density functional theory incorporating van der Waals interactions. Two of the distinct crystal forms are found to be structurally similar and energetically very close after vibrational free energy corrections have been taken into account. Infrared absorption spectra are likewise calculated and compared to experimental measurements. By combining measurements obtained with a commercial Fourier transform infra-red spectrometer and a homemade air-photonics-based THz time domain spectrometer, we could carry out this comparison in the vibrational frequency region from 1 to 40 THz. The excellent agreement of the line positions and the established energy ranking allow us to identify the most stable polymorph of phenylalanine.

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.

Scalable Advanced Li(Ni0.8Co0.1Mn0.1)O2 Cathode Materials from a Slug Flow Continuous Process

Li[Ni_0.8Co_0.1Mn_0.1]O₂ (LNCMO811) is the most studied cathode material for next-generation lithium-ion batteries with high energy density. However, available synthesis methods are time-consuming and complex, restricting their mass production. A scalable manufacturing process for producing NCM811 hydroxide precursors is vital for commercialization of the material. In this work, a three-phase slug flow reactor, which has been demonstrated for its ease of scale-up, better synthetic control, and excellent uniform mixing, was developed to control the initial stage of the coprecipitation of NCM811 hydroxide. Furthermore, an equilibrium model was established to predict the yield and composition of the final product. The homogeneous slurry from the slug flow system was obtained and then transferred into a ripening vessel for the necessary ripening process. Finally, the lithium-nickel-cobalt-manganese oxide was obtained through the calcination of the slug flow-derived precursor with lithium hydroxide, having a tap density of 1.3 g cm^-3 with a well-layered structure. As-synthesized LNCMO811 shows a high specific capacity of 169.5 mAh g^-1 at a current rate of 0.1C and a long cycling stability of 1000 cycling with good capacity retention. This demonstration provides a pathway toward scaling up the cathode synthesis process for large-scale battery applications.

Process intensification of atorvastatin calcium crystallization for target polymorph development via continuous combined cooling and antisolvent crystallization using an oscillatory baffled crystallizer

In this paper, continuous crystallization of Atorvastatin calcium (ASC) using a continuous oscillatory baffled crystallizer (COBC) has been investigated. Like most API manufacturing, ASC is manufactured batchwise and the pure API is recovered via batch combined cooling and antisolvent crystallization (CCAC) process, which has the challenges of low productivity, wide crystal size distribution (CSD) and sometimes polymorphic form contamination. To overcome the limitations of the batch crystallization, continuous crystallization of ASC was studied in a NiTech (United Kingdom) DN15 COBC, manufactured by Alconbury Weston Ltd. (AWL, United Kingdom), with the aim to improve productivity and CSD of the desired polymorph. The COBC has the advantage of high heat transfer rates and improved mixing that significantly reduces the crystallization time. It also has the advantage of spatial temperature distribution and multiple addition ports to control supersaturation and hence the crystallization process. This work uses an array of process analytical technology (PAT) tools to assess key process parameters that affect the polymorphic outcome and CSD. Two parameters were found to have significant impact on the polymorph, they are ratio of solvent to antisolvent at the point of mixing of the two streams and presence of seeds. The splitting of antisolvent into two addition ports in the COBC was found to give the desired form. The CCAC of ASC in COBC was found to be -30-fold more productive than the batch CCAC process. The cycle time for generating 100 g of desired polymorphic form of ASC also significantly reduced from 22 h in batch process to 12 min in the COBC. The crystals obtained using a CCAC process in a COBC had a narrower CSD compared to that from a batch crystallization process.

Material extrusion additive manufacturing of dense pastes consisting of macroscopic particles

Additive manufacturing of dense pastes, those with greater than 50 vol% particles, via material extrusion direct ink write is a promising method to produce customized structures for high-performance materials, such as energetic materials and pharmaceuticals, as well as to enable the use of waste or other locally available particles. However, the high volume fraction and the large sizes of the particles for these applications lead to significant challenges in developing inks and processing methods to prepare quality parts. In this prospective, we analyze challenges in managing particle characteristics, stabilizing the suspensions, mixing the particles and binder, and 3D printing the pastes.

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.

Feedback Control of Crystal Size Distribution for Cooling Batch Crystallization Using Deep Learning-Based Image Analysis

The shape of the crystal size distribution directly determines the quality of crystal products. It is often assumed that distributional properties of crystal size conform to the Gaussian distribution or the log normal distribution. The mean and variance or relative crystal number are widely adopted to describe the crystal size distribution and taken as the control objectives. Therefore, the resulting control methods have difficulties in controlling the crystal size distribution with a general shape. In this article, a novel feedback control system of crystal size distribution based on image analysis is designed for the effective control of crystal size distribution with a general shape. First, a deep learning network-based image analysis method is adopted and implemented to extract the crystal size distribution. Second, the crystal size distribution is approximated by a radial basis function neural network. Consequently, a feedback controller is designed and the tracking control of the target crystal size distribution is finally realized. The results of crystallization experiments demonstrate the effectiveness of the proposed method.

Molecular Weight Distribution Control for Polymerization Processes Based on the Moment-Generating Function

The molecular weight distribution is an important factor that affects the properties of polymers. A control algorithm based on the moment-generating function was proposed to regulate the molecular weight distribution for polymerization processes in this work. The B-spline model was used to approximate the molecular weight distribution, and the weight state space equation of the system was identified by the subspace state space system identification method based on the paired date of B-spline weights and control inputs. Then, a new performance criterion mainly consisting of the moment-generating function was constructed to obtain the optimal control input. The effectiveness of the proposed control method was tested in a styrene polymerization process. The molecular weight distribution of the styrene polymers can be approximated by the B-spline model effectively, and it can also be regulated towards the desired one under the proposed control method.

A review on the modernization of pharmaceutical development and manufacturing - Trends, perspectives, and the role of mathematical modeling

Recently, the pharmaceutical industry has been facing several challenges associated to the use of outdated development and manufacturing technologies. The return on investment on research and development has been shrinking, and, at the same time, an alarming number of shortages and recalls for quality concerns has been registered. The pharmaceutical industry has been responding to these issues through a technological modernization of development and manufacturing, under the support of initiatives and activities such as quality-by-design (QbD), process analytical technology, and pharmaceutical emerging technology. In this review, we analyze this modernization trend, with emphasis on the role that mathematical modeling plays within it. We begin by outlining the main socio-economic trends of the pharmaceutical industry, and by highlighting the life-cycle stages of a pharmaceutical product in which technological modernization can help both achieve consistently high product quality and increase return on investment. Then, we review the historical evolution of the pharmaceutical regulatory framework, and we discuss the current state of implementation and future trends of QbD. The pharmaceutical emerging technology is reviewed afterwards, and a discussion on the evolution of QbD into the more effective quality-by-control (QbC) paradigm is presented. Further, we illustrate how mathematical modeling can support the implementation of QbD and QbC across all stages of the pharmaceutical life-cycle. In this respect, we review academic and industrial applications demonstrating the impact of mathematical modeling on three key activities within pharmaceutical development and manufacturing, namely design space description, process monitoring, and active process control. Finally, we discuss some future research opportunities on the use of mathematical modeling in industrial pharmaceutical environments.

Design, Development, and Analysis of an Automated Sampling Loop for Online Monitoring of Chiral Crystallization

Enantiomeric purity is of prime importance for several industries, specifically in the production of pharmaceuticals. Crystallization processes can be used to obtain pure enantiomers in a suitable solid form. However, some process variants inherently rely on kinetic enhancement (preferential crystallization) of the desired enantiomer or on complex interactions of several phenomena (e.g., attrition-enhanced deracemization and Viedma ripening). Thus, a process analytical technology able to measure the enantiomeric composition of both the solid phase and the liquid phase would be valuable to track and eventually control such processes. This study presents the design and development of a novel automated analytical monitoring system that achieves this. The designed setup tracks the enantiomeric excess (ee) using a continuous closed-loop sampling loop that is coupled to a polarimeter and an attenuated total reflection Fourier transform infrared spectroscopy spectrometer. By heating the loop and alternately sampling either the liquid or the suspension, the combination of these measurements allows tracking of the ee of both the liquid and the solid. This work demonstrates a proof of concept of both the experimental and theoretical aspects of the new system.

Multiscale vapor-mediateddendritic pattern formation and bacterial aggregation in complex respiratory biofluid droplets

HYPOTHESIS

Deposits of biofluid droplets on surfaces (such as respiratory droplets formed during an expiratory) are composed of water-based salt-protein solution that may also contain an infection (bacterial/viral). The final patterns of the deposit formed and bacterial aggregation on the deposits are dictated by the fluid composition and flow dynamics within the droplet.

EXPERIMENTS

This work reports the spatio-temporal, topological regulation of deposits of respiratory fluid droplets and control of bacterial aggregation by tweaking flow inside droplets using non-contact vapor-mediated interactions. Desiccated respiratory droplets form deposits with haphazard multiscale dendritic, cruciform-shaped precipitates when evaporated on a glass substrate. However, we showcase that short and long-range vapor-mediated interaction between the droplets can be used as a tool to control these deposits at nano-micro-millimeter scales. We morphologically control hierarchial dendrite size, orientation and subsequently suppress cruciform-shaped crystals by placing a droplet of ethanol in the vicinity of the biofluid droplet. Active living matter in respiratory fluids like bacteria is preferentially segregated and agglomerated without its viability and pathogenesis attenuation.

FINDINGS

The nucleation sites can be controlled via preferential transfer of solutes in the droplets; thus, achieving control over crystal occurrence, growth dynamics, and the final topology of the deposit. For the first time, we have experimentally presented a proof-of-concept to control the aggregation of live active matter like bacteria without any direct contact. The methodology can have ramifications in biomedical applications like disease detection and bacterial segregation.

On-the-spot quenching for effective implementation of cooling crystallization in a continuous-flow microfluidic device

Illustrated is a microfludic cooling crystallization device that can effectively screen polymorphs, growth rates, and morphology of crystalline materials.

Solubility and Crystallization of Piroxicam from Different Solvents in Evaporative and Cooling Crystallization

In this work, the solubility of a non-steroidal anti-inflammatory drug (NSAID), piroxicam, is investigated. The polymorphic form II, which is the most stable form at room temperature, was investigated in seven different solvents with various polarities. It has been found that the solubility of piroxicam in the solvents is in the following order: chloroform > dichloromethane > acetone > ethyl acetate > acetonitrile > acetic acid > methanol > hexane. Crystallization of piroxicam from different solvents has been performed with evaporative crystallization and cooling crystallization; the effects of solvent evaporation rate and solute concentration have also been studied. Both form I and form II could be produced in cooling and evaporative crystallization, and no simple link can be identified between the operating parameters and the polymorphic outcome. Results obtained in the present work showed the stochastic nature of the nucleation of different polymorphs as well as the complexity of the crystallization of a polymorphic system.

Preparation, stabilisation, isolation and tableting of valsartan nanoparticles using a semi-continuous carrier particle mediated process

This work investigated the technical feasibility of preparing, stabilizing and isolating poorly water-soluble drug nanoparticles via a small-scale antisolvent precipitation process operating in semi-continuous mode. Specifically, a novel semi-continuous process was demonstrated for the carrier particle mediated production, stabilization and isolation of valsartan nanoparticles into a solid form using montmorillonite clay particles as the carrier. The semi-continuous process operated robustly for the full duration of the experiment (~16 min) and steady-state conditions were reached after ~5 min. Nanoparticles of valsartan (51 ± 1 nm) were successfully prepared, stabilized and isolated with the help of montmorillonite (MMT) or protamine functionalized montmorillonite (PA-MMT) into the dried form by this semi-continuous route. The dissolution profile of the isolated valsartan nanocomposite solids was similar to that of valsartan nanocomposite solids produced via the corresponding laboratory scale batch mode process, indicating that the product quality (principally the nanoscale particle size and solid-state form) is retained during the semi-continuous processing of the nanoparticles. Furthermore, tablets produced via direct compression of the isolated valsartan nanocomposite solids displayed a dissolution profile comparable with that of the powdered nanocomposite material. PXRD, DSC, SSNMR and dissolution studies indicate that the valsartan nanoparticles produced via this semi-continuous process were amorphous and exhibited shelf-life stability equivalent to > 10 months.

Application of PAT-Based Feedback Control Approaches in Pharmaceutical Crystallization

Crystallization is one of the important unit operations for the separation and purification of solid products in the chemical, pharmaceutical, and pesticide industries, especially for realizing high-end, high-value solid products. The precise control of the solution crystallization process determines the polymorph, crystal shape, size, and size distribution of the crystal product, which is of great significance to improve product quality and production efficiency. In order to develop the crystallization process in a scientific method that is based on process parameters and data, process analysis technology (PAT) has become an important enabling platform. In this paper, we review the development of PAT in the field of crystallization in recent years. Based on the current research status of drug crystallization process control, the monitoring methods and control strategies of feedback control in the crystallization process were systematically summarized. The focus is on the application of model-free feedback control strategies based on the solution and solid information collected by various online monitoring equipment in product engineering, including improving particle size distribution, achieving polymorphic control, and improving purity. In this paper, the challenges of feedback control strategy in the crystallization process are also discussed, and the development trend of the feedback control strategy has been prospected.

Feedback regulation of crystal growth by buffering monomer concentration

Crystallization is a ubiquitous means of self-assembly that can organize matter over length scales orders of magnitude larger than those of the monomer units. Yet crystallization is notoriously difficult to control because it is exquisitely sensitive to monomer concentration, which changes as monomers are depleted during growth. Living cells control crystallization using chemical reaction networks that offset depletion by synthesizing or activating monomers to regulate monomer concentration, stabilizing growth conditions even as depletion rates change, and thus reliably yielding desired products. Using DNA nanotubes as a model system, here we show that coupling a generic reversible bimolecular monomer buffering reaction to a crystallization process leads to reliable growth of large, uniformly sized crystals even when crystal growth rates change over time. Buffering could be applied broadly as a simple means to regulate and sustain batch crystallization and could facilitate the self-assembly of complex, hierarchical synthetic structures.

Separation Processes to Provide Pure Enantiomers and Plant Ingredients

Enantiomer separation and the isolation of natural products from plants pose challenging separation problems resulting from the similarity of molecules and the number of compounds present in synthesis or extract mixtures. Furthermore, limited theory is available to predict productivities for possible alternative separation techniques. The application and performance of chromatography- and crystallization-based processes are demonstrated for various case studies devoted to isolating valuable target compounds from complex initial mixtures. In all cases, the first emphasis is set to determine the process-specific phase equilibria to identify feasible process options. For all examples considered, yields and productivities are evaluated and compared for different scenarios. Guidelines to approach and solve similar separation tasks are given.

Process Monitoring of Antisolvent Based Crystallization in Low Conductivity Solutions Using Electrical Impedance Spectroscopy and 2-D Electrical Resistance Tomography

Industrial process monitoring is an important field of research where different chemical processes are monitored and controlled. In this work, electrical impedance spectroscopy (EIS) was used to analyze antisolvent based crystallization of sucrose solutions. The impedance and phase spectra were recorded for four known sucrose concentrations in water, and for each case, four predetermined amounts of ethanol were added. As a result, sixteen different solutions involving sucrose solutions of different concentrations and ethanol to water ratios were analyzed. Significant differences were observed in the magnitude and phase spectra of the solutions in the frequency range of 50 kHz to 300 kHz. The experimentally obtained data from the EIS were converted into frequency response models. Three continuous-time transfer function models of the first-order, second-order, and a second-order with a zero were estimated and compared. In addition, a 2-D electrical resistance tomography (ERT) system with a low conductivity sensor unit was designed and tested with demineralized water, tap water and industrial food grade saturated sucrose solution. Non-conducting phantom and sugar crystals were observed within the saturated sucrose solution using the Bayesian reconstruction algorithm. These demonstrations have the potential to be developed into a multi-frequency ERT systems for monitoring the distribution of the crystals in the reactor. The EIS modality can be a complementary process analytical technology (PAT) tool indicating supersaturation status and provide quality assurance.

Advantages of Utilizing Population Balance Modeling of Crystallization Processes for Particle Size Distribution Prediction of an Active Pharmaceutical Ingredient

Active pharmaceutical ingredient (API) particle size distribution is important for both downstream processing operations and in vivo performance. Crystallization process parameters and reactor configuration are important in controlling API particle size distribution (PSD). Given the large number of parameters and the scale-dependence of many parameters, it can be difficult to design a scalable crystallization process that delivers a target PSD. Population balance modeling is a useful tool for understanding crystallization kinetics, which are primarily scale-independent, predicting PSD, and studying the impact of process parameters on PSD. Although population balance modeling (PBM) does have certain limitations, such as scale dependency of secondary nucleation, and is currently limited in commercial software packages to one particle dimension, which has difficulty in predicting PSD for high aspect ratio morphologies, there is still much to be gained from applying PBM in API crystallization processes.

A perspective on Quality-by-Control (QbC) in pharmaceutical continuous manufacturing

The Quality-by-Design (QbD) guidance issued by the US Food and Drug Administration (FDA) has catalyzed the modernization of pharmaceutical manufacturing practices including the adoption of continuous manufacturing. Active process control was highlighted recently as a means to improve the QbD implementation. This advance has since been evolving into the concept of Quality-by-Control (QbC). In this study, the concept of QbC is discussed, including a definition of QbC, a review of the recent developments towards the QbC, and a perspective on the challenges of QbC implementation in continuous manufacturing. The QbC concept is demonstrated using a rotary tablet press, integrated into a pilot scale continuous direct compaction process. The results conclusively showed that active process control, based on product and process knowledge and advanced model-based techniques, including data reconciliation, model predictive control (MPC), and risk analysis, is indispensable to comprehensive QbC implementation, and ensures robustness and efficiency.

Research on the Adaptive Control in Sugar Evaporative Crystallization Using LSSVM and SaDE-ELM

The process of sugar evaporative crystallization is a nonlinear process with large time lag and strong coupling. It is difficult to establish a reasonable mechanism model. In this paper, we use the data driving modeling method to establish an Adaptive Control model for batch boiling sugar crystallization process. First, by analyzing the main influencing factors of the evaporative crystallization process of intermittent boiling sugar, the most important two parameters, brix and liquid level, are selected as the control object. The self-adaptive differential evolution Extreme Learning Machine (SaDE-ELM) is used to construct the control model. A least squares support vector machine (LSSVM) is established and connected in the control loop to control the opening of the feed valve so that to control the feed flowrate according to the objective values of syrup Brix and liquid level. Experiments are conducted and the obtained data are used to train and verify the learning machines. Experiments indicate that the learning machines are able to realize adaptive control to key parameters of the crystallization process. Comparison of different neural networks indicates that the LSSVM performs better than BP, RBF and ELM and SaDE-ELM with prediction error of below 0.01, and training time of below 0.05 s.

Designs of continuous-flow pharmaceutical crystallizers: developments and practice

This review of recent research advances in continuous-flow crystallization includes a five-step general design procedure, generally applicable process intensification strategies, and practical insights.