Asteman, H.; Spiegel, M.: Investigation of the chlorine attack caused by HCl (g) on oxide scales formed on pre-oxidized pure metals and commercial alloys. EUROCORR 2006, Maastricht, The Netherlands (2006)
Asteman, H.; Lill, K. A.; Hassel, A. W.; Spiegel, M.: Preparation and electrochemical characterisation by SDC of thin Cr2O3, Fe2O3 and (Fe,Cr)2O3 films, thermally grown on Pt-substrates. 6th Int. Symposium on Electrochemical Micro and Nanosystem Technologies, Düsseldorf, Germany (2006)
Spiegel, M.: Laboruntersuchungen zur Korrosion in thermischen Anlagen. Fachtagung: Werkstoffe und Verfahren der Energietechnik, Sulzbach-Rosenberg, Germany (2006)
Spiegel, M.: Einfluss der Veränderungen von gasförmigem Chloranteil und Rohrwandtemperaturen auf die Korrosion unter Belägen. VDI Wissensforum: Beläge und Korrosion in Großfeuerungsanlagen, Hannover, Germany (2006)
Spiegel, M.; Stein, F.; Pöter, B.: Initial Stages of Oxide Growth on Fe–Al Alloys. 3rd Disc.Meeting on the Development of Innovative Iron Aluminium Alloys, Mettmann-Düsseldorf, Germany (2006)
Asteman, H.; Spiegel, M.: Investigation of the chemical breakdown of protective oxides formed on pre-oxidized alloys caused by HCl (g) and H2O (g). Eurocorr 2005, Lisbon, Portugal (2005)
Asteman, H.; Lill, K.; Hassel, A. W.; Spiegel, M.: Local Measurements of the Semi conducting Properties of alpha-Fe2O3 and Cr2O3 Films by Impedance Measurement using the Scanning Droplet Cell Technique. 9th International Symposium on the Passivity of Metals and Semiconductors, Paris, France (2005)
Park, E.; Spiegel, M.: Development and Composition of the High Temperature Oxide Film Grown on Fe-15Cr during Annealing. Passivity 9, Paris, France (2005)
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
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.