Brognara, A.; Best, J. P.; Djemia, P.; Faurie, D.; Dehm, G.; Ghidelli, M.: Toward engineered thin film metallic glasses with large mechanical properties: effect of composition and nanostructure. Seminar at Laboratoire des Sciences des Procédés et des Matériaux (LSPM), Paris Nord University, Paris, France (2021)
Brognara, A.; Nasri, I. F. M. A.; Bricchi, B. R.; Li Bassi, A.; Gauchotte, C.; Ghidelli, M.; Lidgi-Guigui, N.: Detection of estradiol by a SERS sensor based on TiO2 covered with gold nanoparticles. Applied Nanotechnology and Nanoscience International Conference – ANNIC 2019, Paris, France (2019)
Brognara, A.; Best, J. P.; Djemia, P.; Faurie, D.; Ghidelli, M.; Dehm, G.: On the mechanical properties and thermal stability of ZrxCu100-x thin film metallic glasses with different compositions. Nanobrücken 2021 - Nanomechanical Testing Conference virtual event, Düsseldorf, Germany (2021)
Brognara, A.; Best, J. P.; Djemia, P.; Faurie, D.; Ghidelli, M.; Dehm, G.: Effect of composition on mechanical properties and thermal stability of ZrCu thin film metallic glasses. European Materials Research Society (E-MRS) Spring Meeting 2021, Virtual Conference, Strasbourg, France (2021)
Devulapalli, V.; Frommeyer, L.; Ghidelli, M.; Liebscher, C.; Dehm, G.: From epitaxially grown thin films to grain boundary analysis in Cu and Ti. International Workshop on Advanced and In-situ Microscopies of Functional Nanomaterials and Devices, IAMNano, Düsseldorf, Germany (2019)
Brognara, A.: Design of ZrCu thin film metallic glasses with tailored mechanical properties through control of composition and nanostructure. Dissertation, RUB Bochum, Bochum, Germany (2025)
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.
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…
Advanced microscopy and spectroscopy offer unique opportunities to study the structure, composition, and bonding state of individual atoms from within complex, engineering materials. Such information can be collected at a spatial resolution of as small as 0.1 nm with the help of aberration correction.