A comprehensive modeling approach for polymorph selection in Lennard-Jones crystallization.

Computational predictions of the polymorphic outcomes of a crystallization process, referred to as polymorph selection, can accelerate the process development for manufacturing solid products with targeted properties. Polymorph selection requires understanding the interplay between the thermodynamic and kinetic factors that drive nucleation. Moreover, post-nucleation events, such as crystal growth and polymorphic transformation, can affect the resulting crystal structures. Here, the nucleation kinetics of the Lennard-Jones (LJ) fluid from the melt is investigated with a focus on the competition between FCC and HCP crystal structures. Both molecular dynamics (MD) simulations and 2D free energy calculations reveal that polymorph selection occurs not during nucleation but when the cluster sizes exceed the critical cluster size. This result contrasts with the classical nucleation mechanism, where each polymorph is assumed to nucleate independently as an ideal bulk-like cluster, comprised only of its given structure. Using the 2D free energy surface and the MD simulation-derived diffusion coefficients, a structure-dependent nucleation rate is estimated, which agrees with the rate obtained from brute force MD simulations. Furthermore, a comprehensive population balance modeling (PBM) approach for polymorph selection is proposed. The PBM combines the calculated nucleation rate with post-nucleation kinetics while accounting for the structural changes of the clusters after nucleation. When applied to the LJ system, the PBM predicts with high accuracy the polymorphic distribution found in a population of crystals generated from MD simulations. Due to the non-classical nucleation mechanism of the LJ system, post-nucleation kinetic events are crucial in determining the structures of the grown crystals.

Breakage Assessment of Lath-Like Crystals in a Novel Laboratory-Scale Agitated Filter Bed Dryer.

Agitated filter bed dryer is often the equipment of choice in the pharmaceutical industry for the isolation of potent active pharmaceutical ingredients (API) from the mother liquor and subsequent drying through intermittent agitation. The use of an impeller to promote homogeneous drying could lead to undesirable size reduction of the crystal product due to shear deformation induced by the impeller blades during agitation, potentially causing off-specification product and further downstream processing issues. An evaluation of the breakage propensity of crystals during the initial development stage is therefore critical. A new versatile scale-down agitated filter bed dryer (AFBD) has been developed for this purpose. Carbamazepine dihydrate crystals that are prone to breakage have been used as model particles. The extent of particle breakage as a function of impeller rotational speed, size of clearance between the impeller and containing walls and base, and solvent content has been evaluated. A transition of breakage behaviour is observed, where carbamazepine dihydrate crystals undergo fragmentation first along the crystallographic plane [00l]. As the crystals become smaller and more equant, the breakage pattern switches to chipping. Unbound solvent content has a strong influence on the breakage, as particles break more readily at high solvent contents. The laboratory-scale instrument developed here provides a tool for comparative assessment of the propensity of particle attrition under agitated filter bed drying conditions.

Modeling Multiphase Heat and Mass Transfer in an Agitated Filter Dryer by Integrating Experiment, Computations, and Analytical Solutions.

The drying of a wet cake consisting of an active pharmaceutical ingredient and solvent in an agitated filter-dryer is a critical and challenging unit operation in the pharmaceutical industry. The complexity of this operation is attributed to the constraints on product quality in terms of its physical properties in addition to the residual solvent content. In this manuscript, a better understanding of the drying mechanism is gained by integrating insights from three-dimensional analytical solutions and computational fluid dynamics simulations into a zero-dimensional model to explain experimental data. The approach provides the time evolution of the mass flow rate of solvent from the wet cake and the center-point temperature of the cake with good accuracy. Further investigation of the zero-dimensional model reveals important parameters such as the mass transfer rate number that predicts whether the process is convection-controlled or diffusion-controlled, and the thermal load of vaporization that estimates the fraction of solvent vaporized per unit time. These parameters can be useful in devising a drying protocol for agitated-filter dryers.

A Novel Computational Approach Coupled with Machine Learning to Predict the Extent of Agglomeration in Particulate Processes.

Solid particle agglomeration is a prevalent phenomenon in various processes across the chemical, food, and pharmaceutical industries. In pharmaceutical manufacturing, agglomeration is both desired in unit operations like wet granulation and undesired in unit operations such as agitated filter drying of highly potent active pharmaceutical ingredients (API). Agglomeration needs to be controlled for optimal physical properties of the API powder. Even after decades of work in the field, there is still very limited understanding of how to quantify, predict, and control the extent of agglomeration, owing to the complex interaction between the solvent and the solid particles and stochasticity imparted by mixing. Furthermore, a large size of industrial scale particulate process systems makes it computationally intractable. To overcome these challenges, we present a novel theory and computational methodology to predict the agglomeration extent by coupling the experimental measurements of agglomeration risk zone or "sticky zone" with discrete element method. The proposed model shows good agreement with experiments. Further, a machine learning model was built to predict agglomeration extent as a function of input variables, such as material properties and processing conditions, in order to build a digital twin of the unit operation. While the focus of the present study is the agglomeration of particles during industrial drying processes, the proposed methodology can be readily applied to numerous other particulate processes where agglomeration is either desired or undesired.

A CFD Digital Twin to Understand Miscible Fluid Blending.

The mixing of stratified miscible fluids with widely different material properties is a common step in biopharmaceutical manufacturing processes. Differences between the fluid densities and viscosities, however, can lead to order-of-magnitude increase in blend times relative to the blending of single-fluid systems. Moreover, the mixing performance in two-fluid systems can be strongly dependent on the Richardson number defined as the ratio of fluid buoyancy to fluid inertia. In this work, we combine lattice Boltzmann transport algorithms with graphics card-based computing hardware to build accelerated digital twins of a physical mixing tanks. The digital twins are designed to predict real-time fluid mechanics with a fidelity that rivals experimental characterization at orders-of-magnitude less cost than physical testing. After validating the twins against measured single- and multi-fluid mixing data, we use them to explore the physics governing fluid blending in stratified two-fluid systems. We use output from the twins to provide general guidance on stratified two-fluid mixing processes, as well as guidance for building such models for other types of physical systems.

Leveraging Lyophilization Modeling for Reliable Development, Scale-up and Technology Transfer.

Modeling of the lyophilization process, based on the steady-state heat and mass transfer, is a useful tool in understanding and optimizing of the process, developing an operating design space following the quality-by-design principle, and justifying occasional process deviations during routine manufacturing. The steady-state model relies on two critical parameters, namely, the vial heat transfer coefficient, Kv, and the cake resistance, Rp. The classical gravimetric method used to measure Kv is tedious, time- and resource-consuming, and can be challenging and costly for commercial scale dryers. This study proposes a new approach to extract both Kv and Rp directly from an experimental run (e.g., temperature and Pirani profiles). The new methodology is demonstrated using 5% w/v mannitol model system. The values of Kv obtained using this method are comparable to those measured using the classic gravimetric method. Application of the proposed approach to process scale-up and technology transfer is illustrated using a case study. The new approach makes the steady-state model a simple and reliable tool for model parameterization, thus maximizes its capability and is particularly beneficial for transfer products from lab/pilot to commercial manufacturing.

Nanocrystal Dissolution Kinetics and Solubility Increase Prediction from Molecular Dynamics: The Case of α-, ß-, and γ-Glycine.

Nanocrystals are receiving increased attention for pharmaceutical applications due to their enhanced solubility relative to their micron-sized counterpart and, in turn, potentially increased bioavailability. In this work, a computational method is proposed to predict the following: (1) polymorph specific dissolution kinetics and (2) the multiplicative increase in the polymorph specific nanocrystal solubility relative to the bulk solubility. The method uses a combination of molecular dynamics and a parametric particle size dependent mass transfer model. The method is demonstrated using a case study of α-, ß-, and γ-glycine. It is shown that only the α-glycine form is predicted to have an increasing dissolution rate with decreasing particle size over the range of particle sizes simulated. On the contrary, γ-glycine shows a monotonically increasing dissolution rate with increasing particle size and dissolves at a rate 1.5 to 2 times larger than α- or ß-glycine. The accelerated dissolution rate of γ-glycine relative to the other two polymorphs correlates directly with the interfacial energy ranking of γ > ß > α obtained from the dissolution simulations, where γ- is predicted to have an interfacial energy roughly four times larger than either α- or ß-glycine. From the interfacial energies, α- and ß-glycine nanoparticles were predicted to experience modest solubility increases of up to 1.4 and 1.8 times the bulk solubility, where as γ-glycine showed upward of an 8 times amplification in the solubility. These MD simulations represent a first attempt at a computational (pre)screening method for the rational design of experiments for future engineering of nanocrystal API formulations.

Solubility curves and nucleation rates from molecular dynamics for polymorph prediction - moving beyond lattice energy minimization.

Current polymorph prediction methods, known as lattice energy minimization, seek to determine the crystal lattice with the lowest potential energy, rendering it unable to predict solvent dependent metastable form crystallization. Facilitated by embarrassingly parallel, multiple replica, large-scale molecular dynamics simulations, we report on a new method concerned with predicting crystal structures using the kinetics and solubility of the low energy polymorphs predicted by lattice energy minimization. The proposed molecular dynamics simulation methodology provides several new predictions to the field of crystallization. (1) The methodology is shown to correctly predict the kinetic preference for ß-glycine nucleation in water relative to α- and γ-glycine. (2) Analysis of nanocrystal melting temperatures show γ- nanocrystals have melting temperatures up to 20 K lower than either α- or ß-glycine. This provides a striking explanation of how an energetically unstable classical nucleation theory (CNT) transition state complex leads to kinetic inaccessibility of γ-glycine in water, despite being the thermodynamically preferred polymorph predicted by lattice energy minimization. (3) The methodology also predicts polymorph-specific solubility curves, where the α-glycine solubility curve is reproduced to within 19% error, over a 45 K temperature range, using nothing but atomistic-level information provided from nucleation simulations. (4) Finally, the methodology produces the correct solubility ranking of ß- > α-glycine. In this work, we demonstrate how the methodology supplements lattice energy minimization with molecular dynamics nucleation simulations to give the correct polymorph prediction, at different length scales, when lattice energy minimization alone would incorrectly predict the formation of γ-glycine in water from the ranking of lattice energies. Thus, lattice energy minimization optimization algorithms are supplemented with the necessary solvent/solute dependent solubility and nucleation kinetics of polymorphs to predict which structure will come out of solution, and not merely which structure has the most stable lattice energy.

Engineering advancements in microfluidic systems for enhanced mixing at low Reynolds numbers.

Mixing within micro- and millichannels is a pivotal element across various applications, ranging from chemical synthesis to biomedical diagnostics and environmental monitoring. The inherent low Reynolds number flow in these channels often results in a parabolic velocity profile, leading to a broad residence time distribution. Achieving efficient mixing at such small scales presents unique challenges and opportunities. This review encompasses various techniques and strategies to evaluate and enhance mixing efficiency in these confined environments. It explores the significance of mixing in micro- and millichannels, highlighting its relevance for enhanced reaction kinetics, homogeneity in mixed fluids, and analytical accuracy. We discuss various mixing methodologies that have been employed to get a narrower residence time distribution. The role of channel geometry, flow conditions, and mixing mechanisms in influencing the mixing performance are also discussed. Various emerging technologies and advancements in microfluidic devices and tools specifically designed to enhance mixing efficiency are highlighted. We emphasize the potential applications of micro- and millichannels in fields of nanoparticle synthesis, which can be utilized for biological applications. Additionally, the prospects of machine learning and artificial intelligence are offered toward incorporating better mixing to achieve precise control over nanoparticle synthesis, ultimately enhancing the potential for applications in these miniature fluidic systems.

Cu-catalyzed couplings of aryl iodonium salts with sodium trifluoromethanesulfinate.

A convenient method for the preparation of aryl trifluoromethylsulfones from the reactions of diaryliodonium salts with sodium trifluoromethanesulfinate in the presence of copper catalysts is described. Cuprous oxide in DMF was found to be the optimal catalyst for the reaction. The reaction conditions are tolerant of various functional groups as well as of various counteranions of the iodonium salt. The synthetic utility of the process is demonstrated by performing the reaction on a preparative scale (88 g).

Snap-on Adaptor for Microtiter Plates to Enable Continuous-Flow Microfluidic Screening and Harvesting of Crystalline Materials.

Microtiter plate assay is a conventional and standard tool for high-throughput (HT) screening that allows the synthesis, harvesting, and analysis of crystals. The microtiter plate screening assays require a small amount of solute in each experiment, which is adequate for a solid-state crystal analysis such as X-ray diffraction (XRD) or Raman spectroscopy. Despite the advantages of these high-throughput assays, their batch operational nature results in a continuous decrease in supersaturation due to crystal nucleation and growth. Continuous-flow microfluidic mixer devices have evolved as an alternate technique for efficiently screening crystals under controlled supersaturation. However, such a microfluidic device requires a minimum of two inlets per micromixer to create cyclonic flow, thereby creating physical limitations for implementing such a device for HT screening. Additionally, the monolithic design of these microfluidic devices makes it challenging to harvest crystals for post-screening analysis. Here, we develop a snap-on adapter that can be reversibly attached to a microtiter plate and convert it into a continuous-flow microfluidic mixer device. The integration of the snap-on adapter with a flow distributor and concentration gradient generator provides greater control over screening conditions while minimizing the number of independent inlets and pumps required. The three-dimensional (3D)-printed snap-on adaptor is plugged into a 24-well plate assay to demonstrate salt screening of naproxen crystals. Different naproxen salts are crystallized using four different salt formers (SFs)-sodium hydroxide, potassium hydroxide, pyridine, and arginine-and four different solvents-ethanol, methanol, isopropyl alcohol, and deionized water. The wells are further inspected under an optical microscope to identify their morphological forms and yields. The crystals are then harvested for solid-state characterization using XRD and Fourier transform infrared spectroscopy, followed by measurement of their dissolution rates. The flexibility of the snap-on adapter to fit on a wide range of microtiter plates and the ease in harvesting and analyzing crystals postscreening are two significant advantages that make this device versatile for various applications.

An investigation of the kinetics and thermodynamics of NaCl nucleation through composite clusters.

Having a good understanding of nucleation is critical for the control of many important processes, such as polymorph selection during crystallization. However, a complete picture of the molecular-level mechanisms of nucleation remains elusive. In this work, we take an in-depth look at the NaCl homogeneous nucleation mechanism through thermodynamics. Distinguished from the classical nucleation theory, we calculate the free energy of nucleation as a function of two nucleus size coordinates: crystalline and amorphous cluster sizes. The free energy surface reveals a thermodynamic preference for a nonclassical mechanism of nucleation through a composite cluster, where the crystalline nucleus is surrounded by an amorphous layer. The thickness of the amorphous layer increases with an increase in supersaturation. The computed free energy landscape agrees well with the composite cluster-free energy model, through which phase specific thermodynamic properties are evaluated. As the supersaturation increases, there is a change in stability of the amorphous phase relative to the solution phase, resulting in a change from one-step to two-step mechanism, seen clearly from the free energy profile along the minimum free energy path crossing the transition curve. By obtaining phase-specific diffusion coefficients, we construct the full mesoscopic model and present a clear roadmap for NaCl nucleation.

Machine Learning-Driven, Sensor-Integrated Microfluidic Device for Monitoring and Control of Supersaturation for Automated Screening of Crystalline Materials.

Integrating sensors in miniaturized devices allow for fast and sensitive detection and precise control of experimental conditions. One of the potential applications of a sensor-integrated microfluidic system is to measure the solute concentration during crystallization. In this study, a continuous-flow microfluidic mixer is paired with an electrochemical sensor to enable in situ measurement of the supersaturation. This sensor is investigated as the predictive measurement of the supersaturation during the antisolvent crystallization of l-histidine in the water-ethanol mixture. Among the various metals tested in a batch system for their sensitivity toward l-histidine, Pt showed the highest sensitivity. A Pt-printed electrode was inserted in the continuous-flow microfluidic mixer, and the cyclic voltammograms of the system were obtained for different concentrations of l-histidine and different water-to-ethanol ratios. The sensor was calibrated for different ratios of antisolvent and concentrations of l-histidine with respect to the change of the measured anodic slope. Additionally, a machine-learning algorithm using neural networks was developed to predict the supersaturation of l-histidine from the measured anodic slope. The electrochemical sensors have shown sensitivity toward l-histidine, l-glutamic acid, and o-aminobenzoic acid, which consist of functional groups present in almost 80% of small-molecule drugs on the market. The machine learning-guided electrochemical sensors can be applied to other small molecules with similar functional groups for automated screening of crystallization conditions in microfluidic devices.

In-line measurement of liquid-liquid phase separation boundaries using a turbidity-sensor-integrated continuous-flow microfluidic device.

Liquid-liquid phase separation (LLPS), also known as oiling-out, is the appearance of the second liquid phase preceding the crystallization. LLPS is an undesirable phenomenon that can occur during the crystallization of active pharmaceutical ingredients (APIs), proteins, and polymers. It is typically avoided during crystallization due to its detrimental impacts on crystalline products due to lowered crystallization rate, the inclusion of impurities, and alteration in particle morphology and size distribution. In situ monitoring of phase separation enables investigating LLPS and identifying the phase separation boundaries. Various process analytical technologies (PATs) have been implemented to determine the LLPS boundaries prior to crystallization to prevent oiling out of compounds. The LLPS measurements using PATs can be time-consuming, expensive, and challenging. Here, we have implemented a fully integrated continuous-flow microfluidic device with a turbidity sensor to quickly and accurately evaluate the LLPS boundaries for a ß-alanine, water, and IPA mixture. The turbidity-sensor-integrated continuous-flow microfluidic device is also placed under an optical microscope to visually track and record the appearance and disappearance of oil droplets. Streams of an aqueous solution of ß-alanine, pure solvent (water), and pure antisolvent (IPA or ethanol) are pumped into the continuous-flow microfluidic device at various flow rates to obtain the compositions at which the solution becomes turbid. The onset of turbidity is measured using a custom-designed, in-line turbidity sensor. The LLPS boundaries can be estimated using the turbidity-sensor-integrated microfluidic device in less than 30 min, which will significantly improve and enhance the workflow of the pharmaceutical drug (or crystalline material) development process.

Patterned microfluidic devices for rapid screening of metal-organic frameworks yield insights into polymorphism and non-monotonic growth.

Metal-organic frameworks (MOFs) are porous crystalline structures that are composed of coordinated metal ligands and organic linkers. Due to their high porosity, ultra-high surface-to-volume ratio, and chemical and structural flexibility, MOFs have numerous applications. MOFs are primarily synthesized in batch reactors under harsh conditions and long synthesis times. The continuous depletion of metal ligands and linkers in batch processes affects the kinetics of the oligomerization reaction and, hence, their nucleation and growth rates. Therefore, the existing screening systems that rely on batch processes, such as microtiter plates and droplet-based microfluidics, do not provide reliable nucleation and growth rate data. Significant challenges still exist for developing a relatively inexpensive, safe, and readily scalable screening device and ensuring consistency of results before scaling up. Here, we have designed patterned-surface microfluidic devices for continuous-flow synthesis of MOFs that allow effective and rapid screening of synthesis conditions. The patterned surface reduces the induction time of MOF synthesis for rapid screening while providing support to capture MOF crystals for growth measurements. The efficacy of the continuous-flow patterned microfluidic device to screen polymorphs, morphology, and growth rates is demonstrated for the HKUST-1 MOF. The effects of solvent composition and pH modulators on the morphology, polymorphs, and size distribution of HKUST-1 are evaluated using the patterned microfluidic device. Additionally, a time-resolved FT-IR analysis coupled with the patterned microfluidic device provides quantitative insights into the non-monotonic growth of MOF crystals with respect to the progression of the bulk oligomerization reaction. The patterned microfluidic device can be used to screen crystals with a longer induction time, such as proteins, covalent-organic frameworks, and MOFs.

Advanced continuous-flow microfluidic device for parallel screening of crystal polymorphs, morphology, and kinetics at controlled supersaturation.

A flow-controlled microfluidic device for parallel and combinatorial screening of crystalline materials can profoundly impact the discovery and development of active pharmaceutical ingredients and other crystalline materials. While the existing continuous-flow microfluidic devices allow crystals to nucleate under controlled conditions in the channels, their growth consumes solute from the solution leading to variation in the downstream composition. The materials screened under such varying conditions are less reproducible in large-scale synthesis. There exists no continuous-flow microfluidic device that traps and grows crystals under controlled conditions for parallel screening. Here we show a blueprint of such a microfluidic device that has parallel-connected micromixers to trap and grow crystals under multiple conditions simultaneously. The efficacy of a multi-well microfluidic device is demonstrated to screen polymorphs, morphology, and growth rates of l-histidine via antisolvent crystallization at eight different solution conditions, including variation in molar concentration, vol% of ethanol, and supersaturation. The overall screening time for l-histidine using the multi-well microfluidic device is ∼30 min, which is at least eight times shorter than the sequential screening process. The screening results are also compared with the conventional 96-well microtiter device, which significantly overestimates the fraction of stable form as compared to metastable form and shows high uncertainty in measuring growth rates. The multi-well microfluidic device paves the way for next-generation microfluidic devices that are amenable to automation for high-throughput screening of crystalline materials.

Understanding stress-induced disorder and breakage in organic crystals: beyond crystal structure anisotropy.

Crystal engineering has advanced the strategies for design and synthesis of organic solids with the main focus being on customising the properties of the materials. Research in this area has a significant impact on large-scale manufacturing, as industrial processes may lead to the deterioration of such properties due to stress-induced transformations and breakage. In this work, we investigate the mechanical properties of structurally related labile multicomponent solids of carbamazepine (CBZ), namely the dihydrate (CBZ·2H2O), a cocrystal of CBZ with 1,4-benzoquinone (2CBZ·BZQ) and the solvates with formamide and 1,4-dioxane (CBZ·FORM and 2CBZ·DIOX, respectively). The effect of factors that are external (e.g. impact stressing) and/or internal (e.g. phase transformations and thermal motion) to the crystals are evaluated. In comparison to the other CBZ multicomponent crystal forms, CBZ·2H2O crystals tolerate less stress and are more susceptible to breakage. It is shown that this poor resistance to fracture may be a consequence of the packing of CBZ molecules and the orientation of the principal molecular axes in the structure relative to the cleavage plane. It is concluded, however, that the CBZ lattice alone is not accountable for the formation of cracks in the crystals of CBZ·2H2O. The strength and the temperature-dependence of electrostatic interactions, such as hydrogen bonds between CBZ and coformer, appear to influence the levels of stress to which the crystals are subjected that lead to fracture. Our findings show that the appropriate selection of coformer in multicomponent crystal forms, targetting superior mechanical properties, needs to account for the intrinsic stress generated by molecular vibrations and not solely by crystal anisotropy. Structural defects within the crystal lattice, although highly influenced by the crystallisation conditions and which are especially difficult to control in organic solids, may also affect breakage.

A machine learning framework for computationally expensive transient models.

Transient simulations of dynamic systems, using physics-based scientific computing tools, are practically limited by availability of computational resources and power. While the promise of machine learning has been explored in a variety of scientific disciplines, its application in creation of a framework for computationally expensive transient models has not been fully explored. Here, we present an ensemble approach where one such computationally expensive tool, discrete element method, is combined with time-series forecasting via auto regressive integrated moving average and machine learning methods to simulate a complex pharmaceutical problem: development of an agitation protocol in an agitated filter dryer to ensure uniform solid bed mixing. This ensemble approach leads to a significant reduction in the computational burden, while retaining model accuracy and performance, practically rendering simulations possible. The developed machine-learning model shows good predictability and agreement with the literature, demonstrating its tremendous potential in scientific computing.

Continuous-flow, well-mixed, microfluidic crystallization device for screening of polymorphs, morphology, and crystallization kinetics at controlled supersaturation.

Screening of crystal polymorphs and morphology and measurement of crystallization kinetics in a controlled supersaturated environment is crucial for the development of crystallization processes for pharmaceuticals, agrochemicals, semiconductors, catalysts, and other specialty chemicals. Most of the current tools including microtiter plates and droplet-based microfluidic devices suffer from depleting supersaturation in small compartments due to nucleation and growth of crystals. Such variation in supersaturation not only affects the outcome but also leads to impediments during the scale-up of the crystallizer. Here we develop an innovative technique using H-shaped and cyclone mixer designs to study crystallization at constant supersaturation maintained by a continuous flow of solution. While the H-shaped design can be used to screen crystals with slower kinetics, the cyclone mixer is better suited for crystals with faster kinetics. The polymorphs and morphology of o-aminobenzoic acid (o-ABA) at different supersaturations are analyzed using the cyclone mixer design and compared with the microtiter plate. While the polymorphs and morphology of o-ABA are affected by depleting supersaturation in a microtiter plate, the cyclone mixer design consistently screened stable and metastable polymorphs. These novel devices will also play an important role in supporting the FDA's initiative to spur innovation in continuous manufacturing for the advancements in drug development.

Assessment of impact breakage of carbamazepine dihydrate due to aerodynamic dispersion.

Acicular crystals are very common in pharmaceutical manufacturing. They are very prone to breakage, causing unwanted particle size degradation and problems such as segregation and lump formation. We investigate the breakage pattern of carbamazepine dihydrate, an acicular and platy crystal with cleavage planes. It readily undergoes attrition during isolation and drying stage, causing processing difficulties. We use the aerodynamic dispersion of a very small quantity of powder sample to induce breakage by applying a pulse of pressurised air. The dispersion unit of Morphologi G3 is used for this purpose. The broken particles settle in a chamber and are subsequently analysed using the built-in image analysis software. The shift in the particle size and shape distributions is quantified through which the extent of breakage is determined as a function of the dispersion pressure. The analysis reveals a change of breakage mechanism as the dispersion pressure is increased from primarily snapping along the crystal length to one in which chipping has also a notable contribution. The breakage data are analysed using a modified impact-based breakage model and the breakability index of the carbamazepine dihydrate is determined for the two breakage regimes. The method provides a quick and easy testing of particle breakability, a useful tool for assessing attrition in process plant and grindability in milling operations.