Kanjilal, A.; Rehman, U.; Best, J. P.; Dehm, G.: Role of temperature on micromechanical fracture behaviour of Laves phase in Mg–Al–Ca ternary alloy. FEMS EUROMAT 2023, Frankfurt, Germany (2023)
Kanjilal, A.; Rehman, U.; Best, J. P.; Dehm, G.: Role of temperature on micromechanical fracture behavior of Laves phase in Mg–Al–Ca ternary alloy. FEMS Euromat 2023, Frankfurt am Main, Germany (2023)
Brink, T.; Langenohl, L.; Ahmad, S.; Liebscher, C.; Dehm, G.: Atomistic Modeling of the Thermodynamics of Grain Boundaries in fcc Metals. 19th International Conference on Diffusion in Solids and Liquids, Crete, Greece (2023)
Dehm, G.: Grain boundary phases in metallic materials: Structure, stability and properties. MiFuN III - Microstructural Functionality at the Nanoscale, Venice, Italy (2023)
Dehm, G.: On the interplay between grain boundary complexions and chemical composition for fcc metals. Possibilities and Limitations of Quantitative Materials Modeling and Characterization 2023, Bernkastel-Kues, Germany (2023)
Brink, T.; Bhat, M. K.; Best, J. P.; Dehm, G.: Grain-boundary segregation effects on bicrystal Cu pillar compression. DPG Spring Meeting, Dresden, Germany (2023)
Kanjilal, A.; Rehman, U.; Best, J. P.; Dehm, G.: Microscale fracture behavior of Laves phases in the Mg–Ca–Al ternary alloy system. 86. Annual Meeting of DPG and DPG-Frühjahrstagung (DPG Spring Meeting) of the Matter and Cosmos Section (SMuK), Dresden, Germany (2023)
Kanjilal, A.; Rehman, U.; Best, J. P.; Dehm, G.: Microscale fracture behavior of Laves phases in the Mg–Ca–Al ternary alloy system. DPG-Frühjahrstagung (DPG Spring Meeting) of the Condensed Matter Section (SKM), Dresden, Germany (2023)
Pemma, S.; Janisch, R.; Dehm, G.; Brink, T.: Deformation mechanism of complexions in a Cu grain boundary under shear. FEMS EUROMAT 2023, Frankfurt am Main, Germany (2023)
Pemma, S.; Janisch, R.; Dehm, G.; Brink, T.: Disconnection activation in complexions of a Cu grain boundary under shear. 19th International Conference on Diffusion in Solids and Liquids (DSL-2023), Heraklion, Greece (2023)
New machine learning framework makes metal recycling more sustainable without compromising on mechanical properties. Results now published in journal Science Advances.
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