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)
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)
Vega-Paredes, M.; Garzón-Manjón, A.; Rivas Rivas, N. A.; Berova, V.; Hengge, K. A.; Gänsler, T.; Jurinsky, T.; Scheu, C.: Ruthenium-Platinum Core-Shell Nanoparticles as durable, CO tolerant catalyst for Polymer Electrolyte Membrane Fuel Cells. 5th International Caparica Symposium on Nanoparticles/Nanomaterials and Applications (ISN2A), Online (accepted)
Vega-Paredes, M.: Degradation mechanisms during operation of high temperature polymer electrolyte membrane fuel cells. Bachelor, Universitat Autònoma de Barcelona, Spain (2020)
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.
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…
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.
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…