Scheu, C.: Tracing impurities and structural defects in energy materials using advanced scanning transmission electron microscopy and atom probe tomography. Retreat Lotsch Group, Schloss Fürstenried, München, Germany (2022)
Vega-Paredes, M.; Garzón-Manjón, A.; Rivas Rivas, N. A.; Berova, V.; Hengge, K. A.; Gänsler, T.; Jurinsky, T.; Scheu, C.: Ruthenium-Platinum Core-Shell Nanoparticles as durable, CO tolerant catalyst for Polymer Electrolyte Membrane Fuel Cells. 5th International Caparica Symposium on Nanoparticles/Nanomaterials and Applications (ISN2A), Online (accepted)
Scheu, C.; Zhang, S.: Hematite for light induced water splitting – improving efficiency by tuning distribution of Sn dopants at the atomic scale. Karlsruher Werkstoffkolloquium_Digital (2021)
Scheu, C.; Hengge, K. A.: Insights in the stability of Pt/Ru catalyst and the effect for polymer electrolyte membrane fuel cells. Thermec 2021, Online Conference (2021)
Aymerich Armengol, R.; Lim, J.; Ledendecker, M.; Scheu, C.: The devil is in the details: correlating SMSI catalyst encapsulation layers with electrochemical properties. ElecNano9 2020, online, Paris, France (2020)
Scheu, C.: Atomic-scale characterization of complex solid solution nanoparticles using TEM. Workshop on High Entropy Alloy and Complex Solid Solution Nanoparticles for Electrocatalysis, RUB, online, Bochum, Germany (2020)
Scheu, C.: Co-organizer of the International Seminar Series on the Microstructure of Materials (on-line). International Seminar Series on the Microstructure of Materials, online (2020)
Scheu, C.; Hieke, S. W.: How stable are thin Aluminium films: Dewetting phenomena observed by in-situ electron microscopy. Microscopy Conference 2019 (MC2019), Berlin, Germany (2019)
New machine learning framework makes metal recycling more sustainable without compromising on mechanical properties. Results now published in journal Science Advances.
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
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.
The general success of large language models (LLM) raises the question if they could be applied to accelerate materials science research and to discover novel sustainable materials. Especially, interdisciplinary research fields including materials science benefit from the LLMs capability to construct a tokenized vector representation of a large…