Mayrhofer, K. J. J.: Online investigation of the stability of electrode materials by coupling of SFC - ICP-MS. Seminar Talk at University of Ulm, Ulm, Germany (2011)
Mayrhofer, K. J. J.: Catalysis in electrochemical reactors - Fundamental investigations for real applications. Seminar talk at Fritz-Haber-Institut der MPG, Berlin, Germany (2011)
Meier, J. C.; Galeano, C.; Katsounaros, I.; Topalov, A. A.; Schüth, F.; Mayrhofer, K. J. J.: Role of Support Interactions for Activity and Stability of Fuel Cell Catalysts. ACS 15th Annual Green Chemistry & Engineering Conference, Washington, D.C., USA (2011)
Mayrhofer, K. J. J.: Electrocatalysis of PEM fuel cell reactions – fundamental investigations for real applications. 9th European Symposium on Electrochemical Engineering, Chania, Greece (2011)
Mayrhofer, K. J. J.: Elektrochemische Hochdurchsatzuntersuchungen mit gekoppelter online Analytik. 4. Korrosionsschutz-Symposium - Korrosionsschutz durch Beschichtungen in Theorie und Praxis, Trent, Rügen (2011)
Mayrhofer, K. J. J.: IL-TEM for the investigation of nanoparticle corrosion. Seminar Talk at Rheinische Friedrich-Wilhelms-Universität, Bonn, Germany (2011)
Mayrhofer, K. J. J.: Identical-Location Microscopy for the investigation of corrosion processes. 61st Annual Meeting of the International Society of Electrochemistry, Nice, France (2010)
Hodnik, N.; Dehm, G.; Mayrhofer, K. J. J.: Electrochemical water based in-situ TEM: case study of platinum based nanoparticles potential- and time-dependent changes. IAM Nano 2015 , Hamburg, Germany (2015)
Geiger, S.; Cherevko, S.; Mayrhofer, K. J. J.: Platinum dissolution in presence of chlorides. 3rd Ertl Symposium on Surface Analysis and Dynamics
, Berlin, Germany (2014)
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.
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…
Crystal Plasticity (CP) modeling [1] is a powerful and well established computational materials science tool to investigate mechanical structure–property relations in crystalline materials. It has been successfully applied to study diverse micromechanical phenomena ranging from strain hardening in single crystals to texture evolution in…