Changizi, R.: Structural Analysis and Correlative Cathodoluminescence Investigations of Pr (doped) Niobates. Dissertation, Georessourcen und Materialtechnik, RWTH Aachen (2022)
Gomell, L.: Advancing the understanding of the microstructure-property relationship in non-toxic and cost-effective thermoelectric Heusler compounds. Dissertation, Fakultät für Georessourcen und Materialtechnik der RWTH Aachen, Germany (2022)
Abdellaoui, L.: Correlation of microstructures and thermal conductivity of the thermoelectric material Ag16.7Sb30Te53.3. Dissertation, Ruhr-Universität Bochum (2019)
Sysoltseva, M.: Characterization of aerosols and nanoparticles released during various indoor and outdoor human activities. Dissertation, RWTH Aachen University (2018)
Folger, A.: The Influence of Post-Growth Heat Treatments and Etching on the Nanostructure and Properties of Rutile TiO2 Nanowires. Dissertation, RWTH Aachen, Aachen, Germany (2017)
Gleich, S.: Investigation of Sputtered Mo2BC Hard Coatings: Correlation of Nanostructure and Mechanical Properties. Dissertation, RWTH Aachen, Aachen, Germany (2017)
Hengge, K.: Investigation of alternative catalyst and support materials and their effect on degradation in high-temperature polymer-electrolyte-membrane fuel cells. Dissertation, RWTH Aachen University, Aachen, Germany (2017)
Morsdorf, L.: Fundamentals of ferrous low-carbon lath martensite: from the as-quenched, to tempered and deformed states. Dissertation, RWTH Aachen, Aachen, Germany (2017)
Diehl, M.: High Resolution Crystal Plasticity Simulations. Dissertation, Aachen Institute for Advanced Study in Computational Engineering Science (AICES), RWTH Aachen University, Aachen, Germany (2015)
Hill, B.: Scanning electron microscopy study of the microscale degradation mechanisms in polymer electrolyte fuel cells. Master, Heinrich-Heine-Universität Düsseldorf (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
In order to prepare raw data from scanning transmission electron microscopy for analysis, pattern detection algorithms are developed that allow to identify automatically higher-order feature such as crystalline grains, lattice defects, etc. from atomically resolved measurements.
New product development in the steel industry nowadays requires faster development of the new alloys with increased complexity. Moreover, for these complex new steel grades, it is more challenging to control their properties during the process chain. This leads to more experimental testing, more plant trials and also higher rejections due to…
Crystal Plasticity (CP) modeling [1] is a powerful and well established computational materials science tool to investigate mechanical structure–property relations in crystalline materials. It has been successfully applied to study diverse micromechanical phenomena ranging from strain hardening in single crystals to texture evolution in…
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.