Luan, C.; Corva, M.; Hagemann, U.; Wang, H.; Heidelmann, M.; Tschulik, K.; Li, T.: Atomic-Scale Insights into Morphological, Structural, and Compositional Evolution of CoOOH during Oxygen Evolution Reaction. ACS Catalysis 13 (2), pp. 1400 - 1411 (2023)
Piontek, S. M.; Naujoks, D.; Tabassum, T.; DelloStritto, M. J.; Jaugstetter, M.; Hosseini, P.; Corva, M.; Ludwig, Alfred, A.; Tschulik, K.; Klein, M. L.et al.; Petersen, P. B.: Probing the Gold/Water Interface with Surface-Specific Spectroscopy. ACS Physical Chemistry Au 3 (1), pp. 119 - 129 (2023)
Kanokkanchana, K.; Tschulik, K.: Electronic Circuit Simulations as a Tool to Understand Distorted Signals in Single-Entity Electrochemistry. The Journal of Physical Chemistry Letters 13 (43), pp. 10120 - 10125 (2022)
Corva, M.; Blanc, N.; Bondue, C. J.; Tschulik, K.: Differential Tafel Analysis: A Quick and Robust Tool to Inspect and Benchmark Charge Transfer in Electrocatalysis. ACS Catalysis 12, pp. 13805 - 13812 (2022)
Rurainsky, C.; Nettler, D. -.; Pahl, T.; Just, A.; Cignoni, P.; Kanokkanchana, K.; Tschulik, K.: Electrochemical dealloying in a magnetic field-Tapping the potential for catalyst and material design. Electrochimica Acta 426, 140807 (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)
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)
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
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…
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.
Data-rich experiments such as scanning transmission electron microscopy (STEM) provide large amounts of multi-dimensional raw data that encodes, via correlations or hierarchical patterns, much of the underlying materials physics. With modern instrumentation, data generation tends to be faster than human analysis, and the full information content is…