Beese-Vasbender, P. F.: From Microbially Induced Corrosion to Bioelectrical Energy Conversion - Electrochemical Characterization of Sulfate-Reducing Bacteria and Methanogenic Archaea. Dissertation, Fakultät für Chemie und Biochemie der Ruhr-Universität Bochum, Bochum, Germany (2014)
Schuppert, A. K.: Combinatorial screening of fuel cell catalysts for the oxygen reduction reaction. Dissertation, Fakultät für Chemie und Biochemie, Ruhr-Universität Bochum, Bochum, Germany (2014)
Meier, J. C.: Degradation phenomena and design principles for stable and active Pt/C fuel cell catalysts. Dissertation, Fakultät für Chemie und Biochemie, Ruhr-Universität Bochum, Bochum, Germany (2013)
Rabe, M.; Kasian, O.; Mayrhofer, K. J. J.; Erbe, A.: Schlussbericht zum Vorhaben: Mechanistische Untersuchungen der elektrochemischen Sauerstoffentwicklung auf Modellelektroden - Stabilität der Elektroden, Natur der Oxide und Intermediate - Teilvorhaben des Clusterprojekts "Mangan". Technische Informationsbibliothek (TIB) Hannover, Hannover, Germany (2019), 32 pp.
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
The project’s goal is to synergize experimental phase transformations dynamics, observed via scanning transmission electron microscopy, with phase-field models that will enable us to learn the continuum description of complex material systems directly from experiment.
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.
The general success of large language models (LLM) raises the question if they could be applied to accelerate materials science research and to discover novel sustainable materials. Especially, interdisciplinary research fields including materials science benefit from the LLMs capability to construct a tokenized vector representation of a large…
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…