De Clercq, J.; Van de Steene, E.; Verbeken, K.; Verhaege, M.: Electrochemical oxidation of 1,4-dioxane at boron-doped diamond electrode. Journal of Chemical Technology & Biotechnology 88 (8), pp. 1162 - 1167 (2010)
De Muynck, W.; Verbeken, K.; De Belie, N.; Verstraete, W.: Influence of urea and calcium dosage on the effectiveness of bacterially induced carbonate precipitation on limestone. BioGeoCivil Engineering, pp. 99 - 111 (2010)
Gomes, E.; Schneider, J.; Verbeken, K.; Hermann, H.; Houbaert, Y.: Effect of hot and cold rolling on grain size and texture in Fe–Si strips with Si-content larger than 2 wt%. Materials Science Forum 638-642, pp. 3561 - 3566 (2010)
Gomes, E.; Schneider, J.; Verbeken, K.; Pasquarella, G.; Houbaert, Y.: Dimensional effects on magnetic properties of Fe–Si steels due to laser and mechanical cutting. IEEE Transactions on Magnetics 46 (2), pp. 213 - 216 (2010)
Hennebel, T.; De Corte, S.; Vanhaecke, L.; Vanherck, K.; Forrez, I.; De Gusseme, B.; Verhagen, P.; Verbeken, K.; Van der Bruggen, B.; Vankelecom, I.et al.; Boon, N.; Verstraete, W.: Removal of diatrizoate with catalytically active membranes incorporating microbially produced palladium nanoparticles. Water Research 44 (5), pp. 1498 - 1506 (2010)
Petrov, R.; Verbeken, K.; Bouquerel, J.; Verleysen, P.; Kestens, L.; Houbaert, Y.: OIM analysis of microstructure and texture of a TRIP assisted steel after static and dynamic deformation. Materials Science Forum 638-642, pp. 3447 - 3452 (2010)
Verbeken, K.; Gomes, E.; Schneider, J.; Houbaert, Y.: Correlation between the magnetic properties and the crystallographic texture during the processing of non oriented electrical steel. Solid State Phenomena 160, pp. 189 - 194 (2010)
Vervynck, S.; Verbeken, K.; Thibaux, P.; Houbaert, Y.: Characterization of the austenite recrystallization by comparing double deformation and stress relaxation tests. Steel Research International 81 (3), pp. 234 - 244 (2010)
Vervynckt, S.; Verbeken, K.; Thibaux, P.; Liebeherr, M.; Houbaert, Y.: Control of the Austenite Recrystallization in Niobium Microalloyed Steels. Materials Science Forum 638-642, pp. 3567 - 3572 (2010)
Bracke, L.; Verbeken, K.; Kestens, L.; Penning, J.: Microstructure and texture evolution during cold rolling and annealing of a high Mn TWIP steel. Acta Materialia 57 (5), pp. 1512 - 1524 (2009)
Verbeken, K.; Barbé, L.; Raabe, D.: Evaluation of the Crystallographic Orientation Relationships between FCC and BCC phases in TRIP Steels. ISIJ International 49 (10), pp. 1601 - 1609 (2009)
Verbeken, K.; van Caenegem, N.; Raabe, D.: Identification of ε-martensite in Fe-based shape memory alloy by means of EBSD. Micron 40, 1, pp. 151 - 156 (2009)
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.