Zhang, S.: Electron microscopy: Resolution and imaging contrast. DMG/DGK-AK9 Summer School “Advanced methods for the characterization of applied materials”, MPI-Kohlenforschung, Mülheim (Ruhr), Germany (2023)
Zhang, S.; Kim, S.-H.; Mingers, A. M.; Gault, B.; Scheu, C.: Operando Study on the activation of hydrogen evolution electrocatalysts. NRF-DFG meeting “Electrodes for direct sea-water splitting and microstructure based stability analyses”, Korean Institute for Energy Research, Daejeon, South Korea (2023)
Jung, C.; Jang, K.; Zhang, S.; Bueno Villoro, R.; Choi, P.-P.; Scheu, C.: Sb-doping induced order to disorder transition enhances the thermal stability of NbCoSn1-xSbx half-Heusler semiconductors. The 20th International Microscopy Congress, PS-07.2. Microscopy of Semiconductor Materials and Devices, Busan, Republic of Korea (2023)
Zhang, S.; Yu, Y.; Jung, C.; Abdellaoui, L.; Scheu, C.: In situ TEM unveils dynamic doping behavior of thermoelectric materials – Microstructure and property evolution under heating and electric biasing. International Microscopy Conference IMC20, Busan, Korea (2023)
Zhang, S.; Kim, S.-H.; Mingers, A. M.; Gault, B.; Scheu, C.: Operando Study on the corrosion of photo-electrocatalysts. NRF-DFG meeting “Electrodes for direct sea-water splitting and microstructure based stability analyses”, Kangwon National University, Chuncheon-si, South Korea (2023)
Zhang, S.: Microstructure design in thermoelectric materials: in situ observation of doping behavior and role of grain boundary phases. Colloqium, Ruhr-Universität Bochum, Bochum, Germany (2023)
Zhang, S.: Microstructure design in thermoelectric materials: Decoupling the transport properties and in situ observation at operation conditions. Colloqium, TU Darmstadt, Darmstadt, Germany (2023)
Scheu, C.; Zhang, S.: Hematite for light induced water splitting – improving efficiency by tuning distribution of Sn dopants at the atomic scale. Karlsruher Werkstoffkolloquium_Digital (2021)
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
Complex simulation protocols combine distinctly different computer codes and have to run on heterogeneous computer architectures. To enable these complex simulation protocols, the CM department has developed pyiron.
Statistical significance in materials science is a challenge that has been trying to overcome by miniaturization. However, this process is still limited to 4-5 tests per parameter variance, i.e. Size, orientation, grain size, composition, etc. as the process of fabricating pillars and testing has to be done one by one. With this project, we aim to…
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…
Ever since the discovery of electricity, chemical reactions occurring at the interface between a solid electrode and an aqueous solution have aroused great scientific interest, not least by the opportunity to influence and control the reactions by applying a voltage across the interface. Our current textbook knowledge is mostly based on mesoscopic…
Recent developments in experimental techniques and computer simulations provided the basis to achieve many of the breakthroughs in understanding materials down to the atomic scale. While extremely powerful, these techniques produce more and more complex data, forcing all departments to develop advanced data management and analysis tools as well as…
Integrated Computational Materials Engineering (ICME) is one of the emerging hot topics in Computational Materials Simulation during the last years. It aims at the integration of simulation tools at different length scales and along the processing chain to predict and optimize final component properties.