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)
Aymerich Armengol, R.: Techniques for the assessment of the stability of (sea) water splitting nanocatalysts. Korean Institute for Energy Research, Jeju, South Korea (2023)
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)
Aymerich Armengol, R.: Determination of the structural and electrochemical stability of nanocatalysts for electrolyzer applications. Chemistry Department, Kangwon National University, Chuncheon-si, South Korea (2023)
Aymerich Armengol, R.: Understanding the stability of nanomaterials through electron microscopy techniques. Physics Department, Technical University of Denmark, Kongens Lyngby, Denmark (2023)
Aymerich Armengol, R.: Stability of 2D oxide and chalcogenide nanomaterials under synthesis and application conditions. MRSEC Seminar Series, Northwestern University, Evanston, IL, USA (2023)
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)
Aymerich Armengol, R.; Lim, J.; Ledendecker, M.; Scheu, C.: The devil is in the details: correlating SMSI catalyst encapsulation layers with electrochemical properties. ElecNano9 2020, online, Paris, France (2020)
Max Planck scientists design a process that merges metal extraction, alloying and processing into one single, eco-friendly step. Their results are now published in the journal Nature.
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 HyWay aims to promote the design of advanced materials that maintain outstanding mechanical properties while mitigating the impact of hydrogen by developing flexible, efficient tools for multiscale material modelling and characterization. These efficient material assessment suites integrate data-driven approaches, advanced…
A novel design with independent tip and sample heating is developed to characterize materials at high temperatures. This design is realized by modifying a displacement controlled room temperature micro straining rig with addition of two miniature hot stages.
Many important phenomena occurring in polycrystalline materials under large plastic strain, like microstructure, deformation localization and in-grain texture evolution can be predicted by high-resolution modeling of crystals. Unfortunately, the simulation mesh gets distorted during the deformation because of the heterogeneity of the plastic…
In this project we developed a phase-field model capable of describing multi-component and multi-sublattice ordered phases, by directly incorporating the compound energy CALPHAD formalism based on chemical potentials. We investigated the complex compositional pathway for the formation of the η-phase in Al-Zn-Mg-Cu alloys during commercial…
The Atom Probe Tomography group in the Microstructure Physics and Alloy Design department is developing integrated protocols for ultra-high vacuum cryogenic specimen transfer between platforms without exposure to atmospheric contamination.
Here, we aim to develop machine-learning enhanced atom probe tomography approaches to reveal chemical short/long-range order (S/LRO) in a series of metallic materials.