Enning, D.; Venzlaff, H.; Garrelfs, J.; Dinh, H. T.; Meyer, V.; Mayrhofer, K. J. J.; Hassel, A. W.; Stratmann, M.; Widdel, F.: Marine sulfate-reducing bacteria cause serious corrosion of iron under electroconductive biogenic mineral crust. Environmental Microbiology 14 (7), pp. 1772 - 1787 (2012)
Beese, P.; Venzlaff, H.; Enning, D.; Mayrhofer, K. J. J.; Widdel, F.; Stratmann, M.: Monitoring anerobic microbially influenced corrosion with electrochemical frequency modulation. 12th Topical Meeting of the International Society of Electrochemistry & XXII International Symposium on Bioelectrochemistry and Bioenergetics of the Bioelectrochemical Society, Bochum, Germany (2013)
Venzlaff, H.; Enning, D.; Widdel, F.; Stratmann, M.; Hassel, A. W.: A new model for microbiologically influenced corrosion. The European Corrosion Congress Eurocorr 2010, Moscow, Russia (2010)
Venzlaff, H.; Widdel, F.; Stratmann, M.; Hassel, A. W.: Microbial corrosion induced by a new highly aggressive SRB strain. 59th Annual Meeting of the International Society of Electrochemistry, Sevilla, Spain (2008)
Venzlaff, H.; Enning, D. R.; Widdel, F.; Stratmann, M.; Hassel, A. W.: Microbial corrosion induced by a highly aggressive SRB strain. 2nd International IMPRS-SurMat Workshop on Surface and Interface Engineering in Advanced Materials, Bochum, Germany (2008)
Venzlaff, H.: Die elektrisch mikrobiell beeinflusste Korrosion von Eisen durch sulfatreduzierte Bakterien. Dissertation, Fakultät für Maschinenbau der Ruhr-Universität, Bochum, Germany (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
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.