Betzler, S. B.; Koh, A. L.; Lotsch, B. V.; Sinclair, R.; Scheu, C.: Atomic Resolution Observation of the Oxidation of Niobium Nanowires: Implications for Renewable Energy Applications. ACS Applied Nano Materials 3 (9), pp. 9285 - 9292 (2020)
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)
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)
Frank, A.; Folger, A.; Betzler, S. B.; Wochnik, A. S.; Wisnet, A.; Scheu, C.: Low-cost synthesis of semiconducting nanostructures used in energy applications. 61. Metallkunde-Kolloquium - Werkstoffforschung für Wirtschaft und Gesellschaft, Lech am Arlberg, Austria (2015)
Frank, A.; Wochnik, A. S.; Betzler, S. B.; Scheu, C.: Copper indium disulfide films synthesized with L-cysteine. Autumn School on Microstructural Characterization and Modelling of Thin-Film Solar Cells, Werder, Potsdam, Germany (2014)
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
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.