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
Crystal Plasticity (CP) modeling [1] is a powerful and well established computational materials science tool to investigate mechanical structure–property relations in crystalline materials. It has been successfully applied to study diverse micromechanical phenomena ranging from strain hardening in single crystals to texture evolution in…
Complex simulation protocols combine distinctly different computer codes and have to run on heterogeneous computer architectures. To enable these complex simulation protocols, the CM department has developed pyiron.
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.