Das, S. M.; Harrison, P.; Kiranbabu, S.; Zhou, X.; Ludwig, W.; Rauch, E. F.; Herbig, M.; Liebscher, C.: Correlating Grain Boundary Character and Composition in 3-Dimensions Using 4D-Scanning Precession Electron Diffraction and Atom Probe Tomography. Small Methods 9 (5), 2401650 (2025)
Harrison, P.; Zhou, X.; Das, S. M.; Lhuissier, P.; Liebscher, C.; Herbig, M.; Ludwig, W.; Rauch, E. F.: Reconstructing dual-phase nanometer scale grains within a pearlitic steel tip in 3D through 4D-scanning precession electron diffraction tomography and automated crystal orientation mapping. Ultramicroscopy 238, 113536 (2022)
Tsai, S.-P.; Konijnenberg, P. J.; Gonzalez, I.; Hartke, S.; Griffiths, T. A.; Herbig, M.; Kawano-Miyata, K.; Taniyama, A.; Sano, N.; Zaefferer, S.: Development of a new, fully automated system for electron backscatter diffraction (EBSD)-based large volume three-dimensional microstructure mapping using serial sectioning by mechanical polishing, and its application to the analysis of special boundaries in 316L stainless steel. Review of Scientific Instruments 93, 093707 (2022)
Rauch, E.; Harrison, P.; Zhou, X.; Herbig, M.; Ludwig, W.; Veron, M.: Correction: Rauch et al. New Features in Crystal Orientation and Phase Mapping for Transmission Electron Microscopy. Symmetry 2021, 13, 1675. Symmetry 13 (12), 2339 (2021)
Rauch, E.; Harrison, P.; Zhou, X.; Herbig, M.; Ludwig, W.; Véron, M.: New Features in Crystal Orientation and Phase Mapping for Transmission Electron Microscopy. Symmetry 13 (9), 1675 (2021)
Mayweg, D.; Morsdorf, L.; Li, Y.; Herbig, M.: Correlation between grain size and carbon content in white etching areas in bearings. Acta Materialia 215, 117048 (2021)
Herbig, M.; Kumar, A.: Removal of hydrocarbon contamination and oxide films from atom probe specimens. Microscopy Research and Technique 84 (2), pp. 291 - 297 (2021)
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