Pemma, S.; Janisch, R.; Dehm, G.; Brink, T.: Effect of the atomic structure of complexions on the active disconnection mode during shear-coupled grain boundary motion. Physical Review Materials 8 (6), 063602 (2024)
Chauniyal, A.; Dehm, G.; Janisch, R.: On the role of pre-existing defects in influencing hardness in nanoscale indentations — Insights from atomistic simulations. Journal of the Mechanics and Physics of Solids 154, 104511 (2021)
Pemma, S.; Janisch, R.; Dehm, G.; Brink, T.: Disconnection activation in complexions of a Cu grain boundary under shear. 19th International Conference on Diffusion in Solids and Liquids (DSL-2023), Heraklion, Greece (2023)
Pemma, S.; Janisch, R.; Dehm, G.; Brink, T.: Deformation mechanism of complexions in a Cu grain boundary under shear. FEMS EUROMAT 2023, Frankfurt am Main, Germany (2023)
Pemma, S.; Brink, T.; Janisch, R.; Dehm, G.: Stress driven grain boundary migration for different complexions of a Cu tilt grain boundary. Materials Science and Engineering Congress 2022, Darmstadt, Germany (2022)
Pemma, S.; Janisch, R.; Dehm, G.; Brink, T.: Atomistic simulation study of grain boundary migration for different complexions in copper. DPG-Tagung, Virtual (2021)
Arigela, V. G.; Kirchlechner, C.; Janisch, R.; Hartmaier, A.; Dehm, G.: Setup of a microscale fracture apparatus to study the interface behaviour in materials at high temperatures. Materials Day 2016, Ruhr Universitat Bochum, Bochum, Germany (2016)
Wang, Z.: Investigation of crystallographic character and molten-salt-corrosion properties of grain boundaries in a stainless steel using EBSD and ab-initio calculations. Dissertation, Ruhr-Universität Bochum, Bochum, Germany (2017)
Scientists at the Max Planck Institute for Sustainable Materials have developed a carbon-free, energy-saving method to extract nickel for batteries, magnets and stainless steel.
Max Planck scientists design a process that merges metal extraction, alloying and processing into one single, eco-friendly step. Their results are now published in the journal Nature.
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