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
Statistical significance in materials science is a challenge that has been trying to overcome by miniaturization. However, this process is still limited to 4-5 tests per parameter variance, i.e. Size, orientation, grain size, composition, etc. as the process of fabricating pillars and testing has to be done one by one. With this project, we aim to…
Atom probe tomography (APT) provides three dimensional(3D) chemical mapping of materials at sub nanometer spatial resolution. In this project, we develop machine-learning tools to facilitate the microstructure analysis of APT data sets in a well-controlled way.