Gänsler, T.: Synthesis Approaches to Nb3O7(OH) Nanostructures and New Studies on Their Growth Mechanism. Master, Ludwig-Maximilians-Universität, München, Germany (2018)
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
Integrated Computational Materials Engineering (ICME) is one of the emerging hot topics in Computational Materials Simulation during the last years. It aims at the integration of simulation tools at different length scales and along the processing chain to predict and optimize final component properties.
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…