Vega-Paredes, M.; Aymerich Armengol, R.; Scheu, C.: Determining the degradation mechanisms and active species of electrocatalysts by identical location electron microscopy. NRF-DFG meeting “Electrodes for direct sea-water splitting and microstructure based stability analyses”, Korean Institute for Energy Research, Jeju, South Korea (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)
Scheu, C.: Chemistry induced phase transition at Σ7 grain boundary in Mg. Workshop on New Horizons in Materials Design, MPIE, Düsseldorf, Germany (2023)
Scheu, C.: Designing the functional properties of thermoelectric materials by grain boundary engineering. Workshop on New Horizons in Materials Design, MPIE, Düsseldorf, Germany (2023)
Vega-Paredes, M.; Arenas Esteban, D.; Garzón-Manjón, A.; Scheu, C.: How can electron tomography be used for studying the catalyst degradation of fuel cells. Advanced Electron Nanoscopy Group – Institut Catala de Nanociencia I Nanotecnologia, Bellaterra, Spain (2022)
Aymerich Armengol, R.; Cignoni, P.; Ebbinghaus, P.; Linnemann, J.; Rabe, M.; Tschulik, K.; Scheu, C.; Lim, J.: Electron microscopy insights on the mechanism of morphology/phase transformations in manganese oxides. Institut de Nanociència i Nanotecnologia (ICN2), Bellaterra, Spain (2022)
Scheu, C.: Unravelling secrets of interfaces in renewable energy application. 10th International Workshop on Interfaces, Santiago de Compostela, Spain (2022)
Aymerich Armengol, R.; Cignoni, P.; Ebbinghaus, P.; Rabe, M.; Tschulik, K.; Scheu, C.; Lim, J.: Mechanism of coupled phase/morphology transformation of 2D manganese oxides through Fe galvanic exchange reaction. Chemistry Department Seminar, Kangwon National University, Chuncheon, South Korea (2022)
Scheu, C.: Insight in the structure and stability of (photo)catalysts. Graduiertenkollegs GRK1896 „In situ microsopy with electrons, X-rays and scanning probes: Abschlusssymposium, Erlangen, Germany (2022)
New machine learning framework makes metal recycling more sustainable without compromising on mechanical properties. Results now published in journal Science Advances.
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.