Katsounaros, I.; Meier, J. C.; Mayrhofer, K. J. J.: The impact of chloride ions and the catalyst loading on the reduction of H2O2 on high-surface-area platinum catalysts. Electrochimica Acta 110, pp. 790 - 795 (2013)
Klemm, S. O.; Karschin, A.; Mechler, A. K.; Topalov, A. A.; Katsounaros, I.; Mayrhofer, K. J. J.: Corrigendum to “Time and potential resolved dissolution analysis of rhodium using a microelectrochemical flow cell coupled to an ICP-MS” [Journal of Electroanalytical Chemistry 677–680 (2012) 50–55] (S1572665712001865) (10.1016/j.jelechem.2012.05.006)). Journal of Electroanalytical Chemistry 693, p. 127 (2013)
Katsounaros, I.; Mayrhofer, K. J. J.: The influence of non-covalent interactions on the hydrogen peroxide electrochemistry on platinum in alkaline electrolytes. Chemical Communications 48 (53), pp. 6660 - 6662 (2012)
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
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.
Atom probe tomography (APT) is one of the MPIE’s key experiments for understanding the interplay of chemical composition in very complex microstructures down to the level of individual atoms. In APT, a needle-shaped specimen (tip diameter ≈100nm) is prepared from the material of interest and subjected to a high voltage. Additional voltage or laser…
Ever since the discovery of electricity, chemical reactions occurring at the interface between a solid electrode and an aqueous solution have aroused great scientific interest, not least by the opportunity to influence and control the reactions by applying a voltage across the interface. Our current textbook knowledge is mostly based on mesoscopic…
Integrated Computational Materials Engineering (ICME) is one of the emerging hot topics in Computational Materials Simulation during the last years. It aims at the integration of simulation tools at different length scales and along the processing chain to predict and optimize final component properties.