International researcher team presents a novel microstructure design strategy for lean medium-manganese steels with optimized properties in the journal Science
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.
This project studies the mechanical properties and microstructural evolution of a transformation-induced plasticity (TRIP)-assisted interstitial high-entropy alloy (iHEA) with a nominal composition of Fe49.5Mn30Co10Cr10C0.5 (at. %) at cryogenic temperature (77 K). We aim to understand the hardening behavior of the iHEA at 77 K, and hence guide the future design of advanced HEA for cryogenic applications.
The precipitation of intermetallic phases from a supersaturated Co(Nb) solid solution is studied in a cooperation with the Hokkaido University of Science, Sapporo.
This project (B06) is part of the SFB 1394 collaborative research centre (CRC), focused on structural and atomic complexity, defect phases and how they are related to material properties. The project started in January 2020 and has three important work packages: (i) fracture analysis of intermetallic phases, (ii) the relationship of fracture to…
Grain boundaries (GBs) affect many macroscopic properties of materials. In the case of metals grain growth, Hall–Petch hardening, diffusion, and electrical conductivity, for example, are influenced or caused by GBs. The goal of this project is to investigate the different GB phases (also called complexions) that can occur in tilt boundaries of fcc…
In order to develop more efficient catalysts for energy conversion, the relationship between the surface composition of MXene-based electrode materials and its behavior has to be understood in operando. Our group will demonstrate how APT combined with scanning photoemission electron microscopy can advance the understanding of complex relationships…
