Allwood, J.; Raabe, D.: Sustainable metals: integrating science and systems approaches. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 382 (2284), 20230247 (2024)
Pauna, H.; Ernst, D.; Zarl, M.; Souza Filho, I. R.; Kulse, M.; Büyükuslu, Ö.; Jovičević-Klug, M.; Springer, H.; Huttula, M.; Schenk, J.et al.; Fabritius, T.; Raabe, D.: The Optical Spectra of Hydrogen Plasma Smelting Reduction of Iron Ore: Application and Requirements. Steel Research International 95 (8), 2400028 (2024)
Özgün, Ö.; Dirba, I.; Gutfleisch, O.; Ma, Y.; Raabe, D.: Green Ironmaking at Higher H2 Pressure: Reduction Kinetics and Microstructure Formation During Hydrogen-Based Direct Reduction of Hematite Pellets. Journal of Sustainable Metallurgy 10, pp. 1127 - 1140 (2024)
New machine learning framework makes metal recycling more sustainable without compromising on mechanical properties. Results now published in journal Science Advances.
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.
Atom probe tomography (APT) is one of the MPIE’s key experiments for understanding the interplay of chemical composition in very complex microstructures down to the level of individual atoms. In APT, a needle-shaped specimen (tip diameter ≈100nm) is prepared from the material of interest and subjected to a high voltage. Additional voltage or laser…