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)
Gänsler, T.; Frank, A.; Betzler, S. B.; Scheu, C.: Electron microscopy studies of Nb3O7(OH) nanostructured cubes - insights in the growth mechanism. Microscience Microscopy Congress MMC2019, Manchester, UK (2019)
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)
Gänsler, T.; Hengge, K. A.; Scheu, C.: 3D Reconstruction of Identical Location Electron Micrographs – Methodology and Pitfalls. IAMNano 2019, International Workshop on Advanced and In-situ Microscopies of Functional Nanomaterials and Devices, Düsseldorf, Germany (2019)
Gänsler, T.; Hengge, K. A.; Beetz, M.; Pizzutilo, E.; Scheu, C.: Tracking the Degradation of Fuel Cell Catalyst Particles: 3D Reconstruction of Nanoscale Transmission Electron Micrographs. CINEMAX IV, "Best poster Award at the Summer School", Toreby, Denmark (2018)
Gänsler, T.: Synthesis Approaches to Nb3O7(OH) Nanostructures and New Studies on Their Growth Mechanism. Master, Ludwig-Maximilians-Universität, München, Germany (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
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.
Ever since the discovery of electricity, chemical reactions occurring at the interface between a solid electrode and an aqueous solution have aroused great scientific interest, not least by the opportunity to influence and control the reactions by applying a voltage across the interface. Our current textbook knowledge is mostly based on mesoscopic…
Recent developments in experimental techniques and computer simulations provided the basis to achieve many of the breakthroughs in understanding materials down to the atomic scale. While extremely powerful, these techniques produce more and more complex data, forcing all departments to develop advanced data management and analysis tools as well as…
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…