Ankah, G. N.; Meimandi, S.; Renner, F. U.: Dealloying of Cu3Pd Single Crystal Surfaces. Journal of the Electrochemical Society 160 (8), pp. C390 - C395 (2013)
Valtiner, M.; Ankah, G. N.; Bashir, A.; Renner, F. U.: Atomic force microscope imaging and force measurements at electrified and actively corroding interfaces: Challenges and novel cell design. Review of Scientific Instruments 82 (2), pp. 023703-1 - 023703-8 (2011)
Renner, F. U.; Ankah, G.; Pareek, A.: Surface Morphology Changes during Dealloying. Pacific Rim Meetin on Electrochemical and Solid-State Science PRIME 2012 / ECS 222, Honolulu, HI, USA (2012)
Ankah, G. N.; Renner, F. U.; Rohwerder, M.: Fundamental Investigations of the Corrosion of Binary Alloys. 59th Annual Meeting of the International Society of Electrochemistry, Sevilla, Spain (2008)
Ankah, G. N.: Investigations of the Selective Dissolution of Cu3Au(111): In-situ and Ex-situ Characterization. Dissertation, Fakultät für Maschinenbau der Ruhr-Universität, Bochum, Germany (2011)
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.