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
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.
The general success of large language models (LLM) raises the question if they could be applied to accelerate materials science research and to discover novel sustainable materials. Especially, interdisciplinary research fields including materials science benefit from the LLMs capability to construct a tokenized vector representation of a large…
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…