Krämer, M.; Favelukis, B.; El-Zoka, A.; Sokol, M.; Rosen, B. A.; Eliaz, N.; Kim, S.-H.; Gault, B.: Near-Atomic Scale Perspective on the Oxidation of Ti3C2Tx MXenes: Insights from Atom Probe Tomography. Advanced Materials 23, 2305183 (2024)
Kim, S.-H.; Shin, K.; Zhou, X.; Jung, C.; Kim, H. Y.; Pedrazzini, S.; Conroy, M.; Henkelman, G.; Gault, B.: Atom probe analysis of BaTiO3 enabled by metallic shielding. Scripta Materialia 229, 115370 (2023)
Aota, L. S.; Jung, C.; Zhang, S.; Kim, S.-H.; Gault, B.: Revealing Compositional Evolution of PdAu Electrocatalyst by Atom Probe Tomography. ACS Energy Letters 8 (6), pp. 2824 - 2830 (2023)
Kim, S.-H.; Jun, H.; Jang , K.; Choi, P.-P.; Gault, B.; Jung, C.: Exploring the Surface Segregation of Rh Dopants in PtNi Nanoparticles through Atom Probe Tomography Analysis. The Journal of Physical Chemistry C 127 (46), pp. 22721 - 22725 (2023)
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
International researcher team presents a novel microstructure design strategy for lean medium-manganese steels with optimized properties in the journal Science
Decarbonisation of the steel production to a hydrogen-based metallurgy is one of the key steps towards a sustainable economy. While still at the beginning of this transformation process, with multiple possible processing routes on different technological readiness, we conduct research into the related fundamental scientific questions at the MPIE.
In this project, we aim to enhance the mechanical properties of an equiatomic CoCrNi medium-entropy alloy (MEA) by interstitial alloying. Carbon and nitrogen with varying contents have been added into the face-centred cubic structured CoCrNi MEA.
This project is a joint project of the De Magnete group and the Atom Probe Tomography group, and was initiated by MPIE’s participation in the CRC TR 270 HOMMAGE. We also benefit from additional collaborations with the “Machine-learning based data extraction from APT” project and the Defect Chemistry and Spectroscopy group.