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Müller-Lorenz, E. M.; Grabke, H.-J.: Metal dusting exposures of modified stainless steels. 5. Symp. on High Temperature Corrosion, pp. 955 - 962 (2001)
Piehl, C.; Tôkei, Z. S.; Grabke, H.-J.: Surface treatment and cold working as tools to improve oxidation behaviour of chromium steels. 5th Int. Symp. on High Temperature Corrosion, pp. 319 - 326 (2001)
Piehl, C.; Tôkei, Z. S.; Grabke, H.-J.: The role of fast diffusion paths in the selective oxidation of chromium steels. Defect and Diffusion Forum 194-199, pp. 1689 - 1694 (2001)
Sämann, N.; Spiegel, M.; Grabke, H.-J.: Influence of surface preparation on the corrosion of steels in simulated waste incineration environments. Materials Science Forum 369-372, pp. 963 - 970 (2001)
Grabke, H. J.; Müller-Lorenz, E. M.; Eltester, B.; Lucas, M.: Formation of chromium rich oxide scales for protection against metal dusting. Materials at High Temperatures 17 (2), pp. 339 - 345 (2000)
Grabke, H. J.; Müller-Lorenz, E. M.; Strauss, S.; Pippel, E.; Woltersdorf, J.: Effects of grain size, cold working, and surface finish on the metal-dusting resistance of steels. Oxidation of Metals 50 (3-4), pp. 241 - 254 (1998)
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Schroer, C.; Spiegel, M.; Sauthoff, G.; Grabke, H.-J.: Fe–Cr–Si-alloys with enhanced resistance against high temperature corrosion in the presence of molten sulphate/chloride mixtures and HCl containing gases. Molten Salt Forum 5-6, pp. 441 - 446 (1998)
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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
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.
The general success of large language models (LLM) raises the question if they could be applied to accelerate materials science research and to discover novel sustainable materials. Especially, interdisciplinary research fields including materials science benefit from the LLMs capability to construct a tokenized vector representation of a large…
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.
Crystal Plasticity (CP) modeling [1] is a powerful and well established computational materials science tool to investigate mechanical structure–property relations in crystalline materials. It has been successfully applied to study diverse micromechanical phenomena ranging from strain hardening in single crystals to texture evolution in…