Trinca, A.; Verdone, N.; Özgün, Ö.; Ma, Y.; Filho, I.; Raabe, D.; Vilardi, G.: Sustainable ironmaking from low-grade iron ores: A kinetic study on thermal decomposition and reduction of iron (II) oxalate. Journal of Environmental Chemical Engineering 13 (6), 119573 (2025)
Dong, X.; Wei, S.; Tehranchi, A.; Saksena, A.; Ponge, D.; Sun, B.; Raabe, D.: The dual role of boron on hydrogen embrittlement: example of interface-related hydrogen effects in an austenite-ferrite two-phase lightweight steel. Acta Materialia 299, 121458 (2025)
Büyükuslu, Ö.; Yang, F.; Raabe, D.; To Baben, M.; Ravensburg, A.: Using Thermodynamics and Microstructure to Mitigate Overfitting in Pellet Reduction Models. steel research international, 2500263 (2025)
Pauna, H.; Souza Filho, I. R.; Kulse, M.; Jovičević-Klug, M.; Springer, H.; Huttula, M.; Fabritius, T.; Raabe, D.: In Situ Observation of Sustainable Hematite-Magnetite-Wustite-Iron Hydrogen Plasma Reduction. Metallurgical and Materials Transactions B 56 (4), pp. 3938 - 3949 (2025)
Ratzker, B.; Ruffino, M.; Shankar, S.; Raabe, D.; Ma, Y.: Elucidating the microstructure evolution during hydrogen-based direct reduction via a case study of single crystal hematite. Acta Materialia 294, 121174 (2025)
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
Atom probe tomography (APT) provides three dimensional(3D) chemical mapping of materials at sub nanometer spatial resolution. In this project, we develop machine-learning tools to facilitate the microstructure analysis of APT data sets in a well-controlled way.