Hengge, K. A.; Scheu, C.: Novel electrodes for polymer based fuel cells. The 18th Israel Materials Engineering Conference (IMEC18), Dead Sea, Israel (2018)
Scheu, C.: 3D Nb3O7(OH) Nanoarrays – Structure, Stability and Functional Properties. Talk at Felix-Bloch-Institut für Festkörperphysik, Universität Leipzig, Leipzig, Germany (2018)
Hieke, S. W.; Willinger, M. G.; Wang, Z.-J.; Richter, G.; Dehm, G.; Scheu, C.: Evolution of faceted voids and fingering instabilities in a model thin film system - Insights by in-situ environmental scanning electron microscopy. Symposium - In situ Microscopy with Electrons, X‐rays and Scanning Probes, Universität Erlangen‐Nürnberg, Erlangen, Germany (2017)
Scheu, C.: Thermal stability and phase transformation of nanostructured Nb3O7(OH) photocatalyst. Material Science & Technology (MST), Salt Lake City, UT, USA (2017)
Zhang, S.; Diehl, L.; Lotsch, B. V.; Scheu, C.: In-situ heating study on the growth of NiOx nanoparticles on photocatalytic supports. International GRK 1896 Satellite Symposium “In Situ Microscopy with Electrons, X-rays and Scanning Probes, Erlangen, Germany (2017)
Betzler, S. B.; Scheu, C.: Nb3O7(OH) – a promising candidate for photocatalyst: synthesis, nanostructure and functionality. International Conference on Functional Nanomaterials and Nanodevices, Budapest, Hungary (2017)
Garzón-Manjón, A.; Zahn, G.; Kuchshaus, C.; Ludwig, A.; Scheu, C.: Observation of the Structural Transformation of Multinary Nanoparticles by In-situ Transmission Electron Microscopy. 13th Multinational Congress on Microscopy (MCM2017), Rovinj, Croatia (2017)
New machine learning framework makes metal recycling more sustainable without compromising on mechanical properties. Results now published in journal Science Advances.
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.
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…