Klemm, S. O.; Topalov, A. A.; Laska, C. A.; Mayrhofer, K. J. J.: Coupling of a high throughput microelectrochemical cell with online multielemental trace analysis by ICP-MS. Electrochemistry Communications 13 (12), pp. 1533 - 1535 (2011)
Laska, C. A.; Rossrucker, L.; Klemm, S. O.; Pust, S. E.; Hüpkes, J.; Mayrhofer, K. J. J.: Die Kopplung von Elektrochemie mit zeitaufgelöster Elementanalytik am Beispiel der chemischen und elektrochemischen Oberflächentexturierung von ZnO-Dünnschichten. In: Tagungsband zur Jahrestagung der Gesellschaft für Korrosionsschutz e.V. 2013, pp. 118 - 128. Jahrestagung der Gesellschaft für Korrosionsschutz e.V. , Frankfurt am Main, Germany, November 12, 2013 - November 13, 2013. (2013)
Laska, C. A.: Development of a Scanning Flow Cell system with Dynamic Electrolyte Change for Fully Automated Parameter Screening. Dissertation, Ruhr-Universität Bochum, Bochum, Germany (2015)
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
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.