Vega-Paredes, M.; Scheu, C.; Aymerich Armengol, R.: Expanding the Potential of Identical Location Scanning Transmission Electron Microscopy for Gas Evolving Reactions: Stability of Rhenium Molybdenum Disulfide Nanocatalysts for Hydrogen Evolution Reaction. ACS Applied Materials and Interfaces 15 (40), pp. 46895 - 46901 (2023)
Liang, Y.; Mrovec, M.; Lysogorskiy, Y.; Vega-Paredes, M.; Scheu, C.; Drautz, R.: Atomic cluster expansion for Pt–Rh catalysts: From ab initio to the simulation of nanoclusters in few steps. Journal of Materials Research 38, pp. 5125 - 5135 (2023)
Berova, V.; Garzón-Manjón, A.; Vega-Paredes, M.; Scheu, C.; Jurzinsky, T.: Influence of Shell Thickness on Durability of Ru@Pt Core-Shell Catalysts for Reformate PEM Fuel Cells. In ECS Meeting Abstracts, MA2022-01 (35), p. 1528. The Electrochemical Society (2022)
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
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…
Ever since the discovery of electricity, chemical reactions occurring at the interface between a solid electrode and an aqueous solution have aroused great scientific interest, not least by the opportunity to influence and control the reactions by applying a voltage across the interface. Our current textbook knowledge is mostly based on mesoscopic…