6 research outputs found
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