RESUMEN
Microcrystal electron diffraction (microED) is an emerging technique for rapid crystallographic analysis of small molecule micro- and nanocrystals. In this report, we evaluate the applicability of microED to pharmaceutical compounds through the analysis of 30 samples obtained from the process and medicinal chemistry groups at Amgen Inc. Using only 40 h of microscope time, 15 of 30 crystal structures were elucidated. From these crystal structures, all chiral compounds had the correct absolute stereochemistry assigned by dynamical refinement of continuous rotation electron diffraction data, confirming dynamical refinement as a promising tool for the absolute stereochemistry determination of pharmaceutically relevant compounds.
Asunto(s)
Nanopartículas , Estereoisomerismo , Estructura Molecular , Preparaciones Farmacéuticas/química , Cristalografía por Rayos X , Nanopartículas/química , Modelos MolecularesRESUMEN
X-ray computed tomography (CT) is a non-destructive imaging technique in which contrast originates from the materials' absorption coefficient. The recent development of laboratory nanoscale CT (nano-CT) systems has pushed the spatial resolution for battery material imaging to voxel sizes of 50 nm, a limit previously achievable only with synchrotron facilities. Given the non-destructive nature of CT, in situ and operando studies have emerged as powerful methods to quantify morphological parameters, such as tortuosity factor, porosity, surface area and volume expansion, during battery operation or cycling. Combined with artificial intelligence and machine learning analysis techniques, nano-CT has enabled the development of predictive models to analyse the impact of the electrode microstructure on cell performances or the influence of material heterogeneities on electrochemical responses. In this Review, we discuss the role of X-ray CT and nano-CT experimentation in the battery field, discuss the incorporation of artificial intelligence and machine learning analyses and provide a perspective on how the combination of multiscale CT imaging techniques can expand the development of predictive multiscale battery behavioural models.