Frank, A.; Changizi, R.; Scheu, C.: Preparative and analytical challenges in electron microscopic investigation of nanostructured CuInS2 thin films for energy applications. Microscience Microscopy Congress (MMC) 2019, Manchester, UK (2019)
Gänsler, T.; Frank, A.; Betzler, S. B.; Scheu, C.: Electron microscopy studies of Nb3O7(OH) nanostructured cubes - insights in the growth mechanism. Microscience Microscopy Congress MMC2019, Manchester, UK (2019)
Scheu, C.; Folger, A.: Annealing treatment in various atmospheres: A tool to control structure and properties of TiO2 nanowires. 6th International Symposium on Metastable, Amorphous and Nanostructured Materials (ISMANAM-2019), Chennai, India (2019)
Scheu, C.; Zhang, S.: Effect of interfaces on the photoelectrochemical performance of functional oxides. PICS3 2019 Meeting, Centre Interdisciplinaire de Nanoscience de Marseille, Marseille, France (2019)
Frank, A.; Dias, M.; Hieke, S. W.; Kruth, A.; Scheu, C.: Electron microscopic investigation of the influence of plasma parameters on VOx films deposited by a plasma ion assisted process. E-MRS 2019 Spring Meeting, Nice, France (2019)
Lim, J.; Hengge, K. A.; Aymerich Armengol, R.; Gänsler, T.; Scheu, C.: Structural Investigation of 2D Nanosheets and their Assembly to 3D Porous Morphologies. 5th International Conference on Electronic Materials and Nanotechnology for Green Environment (ENGE 2018), Jeju, Korea (2018)
Scheu, C.; Hengge, K. A.: Unraveling catalyst growth and degradation mechanisms via STEM. International Workshop on Advanced and In-situ Microscopies of Functional Nanomaterials and Devices, IAMNano 2018, Hamburg, Germany (2018)
Scheu, C.: Nanostructured photocatalyst based on transition metal oxides. Seminar at National University of Singapore, Dept. of Materials Science and Engineering, Singapore, Singapore (2018)
Scheu, C.: Insights in interfaces by combining Cs corrected STEM and APT experiments with atomistic simulations. Seminar at the University of Sydney, Faculty of Engineering & Information Technologies, Sydney, Australia (2018)
Scientists of the Max-Planck-Institut für Eisenforschung pioneer new machine learning model for corrosion-resistant alloy design. Their results are now published in the journal Science Advances
Data-rich experiments such as scanning transmission electron microscopy (STEM) provide large amounts of multi-dimensional raw data that encodes, via correlations or hierarchical patterns, much of the underlying materials physics. With modern instrumentation, data generation tends to be faster than human analysis, and the full information content is…
The project’s goal is to synergize experimental phase transformations dynamics, observed via scanning transmission electron microscopy, with phase-field models that will enable us to learn the continuum description of complex material systems directly from experiment.
In order to prepare raw data from scanning transmission electron microscopy for analysis, pattern detection algorithms are developed that allow to identify automatically higher-order feature such as crystalline grains, lattice defects, etc. from atomically resolved measurements.