Vega-Paredes, M.: Degradation mechanisms during operation of high temperature polymer electrolyte membrane fuel cells. Bachelor, Universitat Autònoma de Barcelona, Spain (2020)
Bueno Villoro, R.: Effect of the processing route on the microstructure of Ag18Sb29Te53 (AST) based thermoelectrics. Bachelor, Universitat Autònoma de Barcelona, Spain (2018)
Wetegrove, M.; Duarte, M. J.; Taube, K.; Rohloff, M.; Gopalan, H.; Scheu, C.; Dehm, G.; Kruth, A.: Preventing Hydrogen Embrittlement: The Role of Barrier Coatings for the Hydrogen Economy, Hydrogen 4 (2 Ed.), pp. 307 - 322 (2023)
Dehm, G.; Liebscher, C.; Völker, B.; Scheu, C.: Organizer of the “IAMNano 2019 Düsseldorf” - International Workshop on Advanced In Situ Microscopies of Functional Nanomaterials and Devices. (2019)
Scheu, C.: Co-organizer of the symposium “PS12 - Materials for Energy Production, Storage and Catalysis” at the “19th International Microscopy Congress. (2018)
Scheu, C.: Co-organizer of the symposium “Experimental and Theoretical insights on Interfaces of Ceramics” at the “Conference on Electronic and Advanced Materials”. (2018)
Hübel, K.; Rohwerder, M.; Scheu, C.; Todorova, M.: Organizer of the workshop “Status and Future Challenges in Characterisation of Interfaces for Electrochemical Applications - Part 1” at the MPIE. (2016)
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
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.