Heinzl, C.; Hengge, K.; Perchthaler, M.; Hacker, V.; Scheu, C.: Insight into the Degradation of HT-PEMFCs Containing Tungsten Oxide Catalyst Support Material for the Anode. Journal of the Electrochemical Society 162 (3), pp. F280 - F290 (2015)
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.; 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)
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)
Hengge, K.: Insight into the degradation of polymer based fuel cells. 3rd international conference on Battery and Fuel Cell Technology , London, UK (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
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.