Functionalized Silica Nanoparticles as Additives for Polymorphic Control in Emulsion-Based Crystallization of Glycine

Emulsion-based crystallization to produce spherical crystalline agglomerates is an attractive route to control the size and morphology of active pharmaceutical ingredient (API) crystals, which in turn improves downstream processability. Here, we demonstrate the use of silica nanoparticles modified with different surface functional groups (hydroxyl, amino, carboxylic, imidazolim chloride, and chloride) as additives in water-in-oil emulsion-based crystallization of glycine, a model API molecule. Spherical agglomerates of glycine obtained under different experimental conditions are characterized by powder X-ray diffraction (XRD) and scanning electron microscopy. Our observations reveal the strong influence of particle functionalization on polymorphic outcome at near-neutral (pH ∼6) conditions, where we are able to selectively crystallize the least stable β-polymorph of glycine or tune the relative ratio of α- and β-polymorphs by selecting appropriate experimental conditions. Mixtures of α- and γ-glycine are typically obtained under acidic solutions (pH ∼3), irrespective of the functional groups used. We examine the influence of charge and immobilization density of surface functional groups and nanoparticle concentration on the polymorphic outcome and rationalize our results by analyzing molecular and functional group speciation

Prediction of Gasoline Blend Ignition Characteristics Using Machine Learning Models

Funding Information: This work was funded by Neste Corporation. Publisher Copyright: © 2021 The Authors. Published by American Chemical Society.Research Octane Number (RON), among other autoignition related properties, is a primary indicator of the grade of spark-ignition (SI) fuels. However, in many cases, the blending of various gasoline components affects the RON of the final fuel product in a nonlinear way. Currently, the lack of precise predictive models for RON challenges the accurate blending and production of commercial SI fuels. This study compares popular Machine Learning (ML) algorithms and evaluates their potential to develop state-of-the-art models able to predict key SI fuel properties. Typical gasoline composition was simplified and represented by a palette of seven characteristic molecules, including five hydrocarbons and two oxygenated species. Ordinary Least Squares (OLS), Nearest Neighbors (NN), Support Vector Machines (SVM), Decision Trees (DT), and Random Forest (RF) algorithms were trained, cross-validated, and tested using a database containing 243 gasoline-like fuel blends with known RON. Best results were obtained with nonlinear SVM algorithms able to reproduce synergistic and antagonistic molecular interactions. The Mean Absolute Error (MAE) on the test set was equal to 0.9, and the estimator maintained its accuracy when alterations were performed on the training data set. Linear methods performed better using molar compositions while predictions on a volumetric basis required nonlinear algorithms for satisfactory accuracy. Developed models allow one to quantify the nonlinear blending behavior of different hydrocarbons and oxygenates accounting for those effects during fuel blending and production. Moreover, these models contribute to a deeper understanding of the phenomena that will facilitate the introduction of alternative gasoline recipes and components.Peer reviewe

Dynamics and Morphological Outcomes in Thin-Film Spherical Crystallization of Glycine from Microfluidic Emulsions: Experimental Studies and Modeling

We present detailed experimental studies, supported by a theoretical model, of dynamics and morphological outcomes in thin-film spherical crystallization of glycine from microfluidic emulsions. Specifically, the effects of droplet size, shrinkage rate, and temperature are studied with the aim of understanding and delineating, from a processing standpoint, crystallization conditions that ensure spherulitic crystal growth in individual droplets, ultimately yielding compactly packed spherical crystal agglomerates (SAs). Our experiments reveal the existence, under all processing conditions, of a critical concentration (supersaturation) beyond which droplet shrinkage due to evaporation is dramatically arrested. The morphological outcome of the crystallization then depends on whether a nucleation event in a droplet occurs before (yielding loosely packed or faceted single crystals) or after (compactly packed SAs) this critical concentration is reached. We analyze our observations within the framework of a simple physical model based on classical nucleation theory and the theory of nonstationary Poisson processes, which accurately captures the overall trends. Experiments were conducted in a temperature range of 45–85 °C, droplet diameter range of 50–160 μm, and film thickness range of 0.5–1.5 mm. We found that smaller droplets and faster shrinkage rates (in thinner films) favor the formation of compact SAs at a given temperature, and lower temperatures generally favor compact SA formation at a fixed drop size and shrinkage rate. This work builds on our recent demonstration of spherical crystallization in microfluidic emulsions and provides valuable guidelines for the design of spherical crystallization processes using this method

Highly selective, kinetically driven polymorphic selection in microfluidic emulsion-based crystallization and formulation

Crystal Growth and Design151212-21

Spherical Crystallization of Glycine from Monodisperse Microfluidic Emulsions

Emulsion-based crystallization to produce spherical crystalline agglomerates (SAs) is an attractive route to control crystal size during downstream processing of active pharmaceutical ingredients (APIs). However, conventional methods of emulsification in stirred vessels pose several problems that limit the utility of emulsion-based crystallization. In this paper, we use capillary microfluidics to generate monodisperse water-in-oil emulsions. Capillary microfluidics, in conjunction with evaporative crystallization on a flat heated surface, enables controllable production of uniformly sized SAs of glycine in the 35–150 μm size range. We report detailed characterization of particle size, size distribution, structure, and polymorphic form. Further, online high-speed stereomicroscopic observations reveal several clearly demarcated stages in the dynamics of glycine crystallization from emulsion droplets. Rapid droplet shrinkage is followed by crystal nucleation within individual droplets. Once a nucleus is formed within a droplet, crystal growth is very rapid (<0.1 s) and occurs linearly along radially advancing fronts at speeds of up to 1 mm/s, similar to spherulitic crystal growth from impure melts. The spherulitic aggregate thus formed ages to yield the final SA morphology. Overall crystallization times are on the order of minutes, as compared to hours in conventional batch processes. We discuss these phenomena and their implications for the development of more generalized processes applicable to a variety of drug molecules. This work paves the way for microfluidics-enabled continuous spherical crystallization processes

Highly Selective, Kinetically Driven Polymorphic Selection in Microfluidic Emulsion-Based Crystallization and Formulation

We present a simple, potentially generalizable method to create highly monodisperse spherical microparticles (SMs) of ∼200 μm size containing active pharmaceutical ingredient (API) crystals and a macromolecular excipient, with unprecedented, highly specific, and selective control over the morphology and polymorphic outcome. The basic idea and novelty of our method is to control polymorphic selection within evaporating emulsion drops containing API–excipient mixtures via the kinetics of two simultaneously occurring processes: liquid–liquid phase separation and supersaturation generation, both governed by solvent evaporation. We demonstrate our method using two model hydrophobic APIs: 5-methyl-2-[(2-nitrophenyl)­amino]-3-thiophenecarbonitrile (ROY) and carbamazepine (CBZ), formulated with ethyl cellulose (EC) as excipient. We dispense monodisperse oil-in-water (O/W) emulsions containing the API–excipient mixture on a flat substrate with a predispensed film of the continuous phase, which are subsequently subjected to evaporative crystallization. We are able to control the polymorphic selection by varying solvent evaporation rate, which can be simply tuned by the film thickness; thin (∼0.5 mm) and thick (∼2 mm) films lead to completely <i>specific</i> and <i>different</i> polymorphic outcomes for both model APIs: yellow (YT04) and orange (OP) for ROY, and form II and form III for CBZ respectively. Our method paves the way for simultaneous, bottom-up crystallization and formulation processes coupled with unprecedented polymorphic selection through process driven kinetics