Lhadi, S.; Ahzi, S.; Rémond, Y.; Nikolov, S. D.; Fabritius, H.-O.: Effects of homogenization technique and introduction of interfaces in a multiscale approach to predict the elastic properties of arthropod cuticle. Journal of the Mechanical Behavior of Biomedical Materials 23, pp. 103 - 116 (2013)
Fabritius, H.; Karsten, E. S.; Balasundaram, K.; Hild, S.; Huemer, K.; Raabe, D.: Correlation of structure, composition and local mechanical properties in the dorsal carapace of the edible crab Cancer pagurus. 11, pp. 766 - 776 (2012)
Maniruzzaman, M.; Rahman, M. A.; Gafur, M. A.; Fabritius, H.; Raabe, D.: Modification of pineapple leaf fibers and graft copolymerization of acrylonitrile onto modified fibers. Journal of Composite Materials 46, pp. 79 - 90 (2012)
Van Opdenbosch, D.; Johannes, M.; Wu, X.; Fabritius, H.; Zollfrank, C.: Fabrication of high-temperature resistant threedimensional photonic crystals with tunable photonic properties by biotemplating. 4, pp. 516 - 522 (2012)
Fabritius, H.; Sachs, C.; Romano, P.; Raabe, D.: Influence of structural principles on the mechanics of a biological fiber-based composite material with hierarchical organization: The exoskeleton of the lobster Homarus americanus. Advanced Materials 21, pp. 391 - 400 (2009)
Al-Sawalmih, A.; Li, C.; Siegel, S.; Fabritius, H.; Yi, S. B.; Raabe, D.; Fratzl, P.; Paris, O.: Microtexture and Chitin/Calcite Orientation Relationship in the Mineralized Exoskeleton of the American Lobster. Advanced Functional Materials 18 (20), pp. 3307 - 3314 (2008)
Sachs, C.; Fabritius, H.; Raabe, D.: Influence of the microstructure on deformation anisotropy of mineralized cuticle from the lobster Homarus americanus. Journal of Structural Biology 161, pp. 120 - 132 (2008)
Boßelmann, F.; Romano, P.; Fabritius, H.; Raabe, D.: The composition of the exoskeleton of two crustacea: The American lobster Homarus americanus and the edible crab Cancer pagurus. Thermochimica Acta 463 (1-2), pp. 65 - 68 (2007)
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…
New product development in the steel industry nowadays requires faster development of the new alloys with increased complexity. Moreover, for these complex new steel grades, it is more challenging to control their properties during the process chain. This leads to more experimental testing, more plant trials and also higher rejections due to…
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…