Rabe, M.; Toparli, C.; Chen, Y.-H.; Kasian, O.; Mayrhofer, K. J. J.; Erbe, A.: Alkaline manganese electrochemistry studied by in situ and operando spectroscopic methods - metal dissolution, oxide formation and oxygen evolution. Physical Chemistry Chemical Physics 21 (20), pp. 10457 - 10469 (2019)
Chen, Y.-H.; Erbe, A.: The multiple roles of an organic corrosion inhibitor on copper investigated by a combination of electrochemistry-coupled optical in situ spectroscopies. Corrosion Science 145, pp. 232 - 238 (2018)
Luo, H.; Li, Z.; Chen, Y.-H.; Ponge, D.; Rohwerder, M.; Raabe, D.: Hydrogen effects on microstructural evolution and passive film characteristics of a duplex stainless steel. Electrochemistry Communucations 79, pp. 28 - 32 (2017)
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Chen, Y.-H.: A comprehensive in situ spectroscopic study of 2-mercaptobenzothiazole as a corrosion inhibitor for copper. Dissertation, Fakultät für Chemie und Biochemie der Ruhr-Universität Bochum, Bochum, Germany (2018)
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
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…
Recent developments in experimental techniques and computer simulations provided the basis to achieve many of the breakthroughs in understanding materials down to the atomic scale. While extremely powerful, these techniques produce more and more complex data, forcing all departments to develop advanced data management and analysis tools as well as…
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.
Data-rich experiments such as scanning transmission electron microscopy (STEM) provide large amounts of multi-dimensional raw data that encodes, via correlations or hierarchical patterns, much of the underlying materials physics. With modern instrumentation, data generation tends to be faster than human analysis, and the full information content is…
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.