The crystal growth kinetics of a proprietary active pharmaceutical ingredient (API) was investigated by isothermal seeded batch de-supersaturation experiments in solvent mixtures using the "true" thermodynamic representation of the supersaturation driving force, which considers the activities of the saturated and supersaturated states. Three approaches to approximate the experimentally inaccessible activity coefficients of the supersaturated state were assessed, as well as the most common approximation, which omits the activity coefficients altogether. Subsequently, the supersaturation data from the different expressions were fed into a population balance model to estimate kinetic parameters for the empirical, Burton− Cabrera−Frank, and birth-and-spread growth models. The results demonstrate that the approach used to compute the supersaturation alters the estimated kinetic parameters significantly, having potentially serious implications for their physical interpretation and for extracting the physical properties they represent in lumped form. Moreover, including the chemical activities in the supersaturation leads to kinetic parameters with a tighter joint confidence interval and weaker parameter correlation that can better explain the experimental observation of the API growing appreciably only under higher antisolvent amounts. Finally, the simultaneous occurrence of multiple crystal growth mechanisms is investigated, concluding that the additive contribution of B+S and BCF best explains the supersaturation decay observed in the experiments for this API.
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.
A new form to calculate the activity coefficient of CO2 in aqueous solutions of chloride salts is presented. The model is coupled with an external solubility model to verify its performance against experimental data in temperature range of 50ºC-120ºC and pressures up to 200 bar, showing the model gives more accurate results than some well-known solubility models. Moreover, it presents a procedure to estimate parameters in complex algebraic models via MATLAB ® , testing seven built-in optimization algorithms and mapping both individual and joint confidence interval.
A procedure to generate a suitable surface and volume meshes from image sequences or other scan data is presented. The methodology gives preference to readily available free software for the final goal of using the generated mesh for computational fluid dynamics or finite element simulations. The steps involve the extraction of a surface mesh from the image sequence, segmentation, trouble-shooting, treatment, refinement/coarsening, smoothing, translation into a volume mesh and post-processing. The user controls the detail of the final mesh through a series of algorithms included in the suggested software. The methodology is illustrated with a computer microtomography data of a carbonate rock. Finally, the mesh is imported in ANSYS ® Fluent to demonstrate that the resulting mesh can be used in simulations.
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