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
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…
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…