Sachs, C.; Nikolov, S.; Fabritius, H.; Raabe, D.: Investigation and Modeling of the Elastic Properties of Lobster Cuticle Depending on its Grade of Mineralization. MRS Spring Meeting, San Francisco, CA, USA (2008)
Al-Sawalmih, A.; Romano, P.; Sachs, C.; Raabe, D.: Structure and texture analysis of chitin-bio-nanocomposites using synchrotron radiation. MRS Spring Meeting, San Francisco, CA, USA (2005)
Romano, P.; Al-Sawalmih, A.; Sachs, C.; Raabe, D.; Brokmeier, H. G.: Mesostructure, microstructure and anisotropy of the lobster cuticle. MRS Spring Meeting, San Francisco, CA, USA (2005)
Romano, P.; Raabe, D.; Al-Sawalmih, A.; Sachs, C.; Servos, G.; Hartwig, H. G.: Influence of sample preparation and anisotropy on lobster claw studied by LOM, SEM and TEM. Focus on Microscopy, Jena, Germany (2005)
Sachs, C.: Microstructure and mechanical properties of the exoskeleton of the lobster Homarus americanus as an example of a biological composite material. Dissertation, RWTH Aachen, Aachen, Germany (2008)
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.
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…