7 citations · 16 across the 20 of their papers we have counts for
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An AFM-based approach for quantification of guest particle deformation during mechano-fusion
Phillip Gräfensteiner, Judith Friebel, Lisa Ditscherlein +3
During the mechano-fusion process for dry particle coating, (hetero)-aggregates are formed consisting of host particles which are coated by smaller guest particles. During this pro…
Determination of droplet size from wide-angle light scattering image data using convolutional neural networks
Tom Kirstein, Simon Aßmann, Orkun Furat +2
Wide-angle light scattering (WALS) offers the possibility of a highly temporally and spatially resolved measurement of droplets in spray-based methods for nanoparticle synthesis. T…
Using convolutional neural networks for stereological characterization of 3D hetero-aggregates based on synthetic STEM data
Lukas Fuchs, Tom Kirstein, Christoph Mahr +5
The structural characterization of hetero-aggregates in 3D is of great interest, e.g., for deriving process-structure or structure-property relationships. However, since 3D imaging…
Multidimensional characterization of particle morphology and mineralogical composition using CT data and R-vine copulas
Orkun Furat, Tom Kirstein, Thomas Leißner +4
Computed tomography (CT) can capture volumes large enough to measure a statistically meaningful number of micron-sized particles with a sufficiently good resolution to allow for th…
Mixed moving average field guided learning for spatio-temporal data
Imma Valentina Curato, Orkun Furat, Lorenzo Proietti +1
Influenced mixed moving average fields are a versatile modeling class for spatio-temporal data. However, their predictive distribution is not generally known. Under this modeling a…