Hamilton, J.; Gianotti, S.; Fischer, J.; Della Fara, G.; Impergre, A.; De Vecchi, F.; AbuAlia, M.; Fischer, A.; Markovics, A.; Wimmer, M.: Electrophoretic Deposition of Gentamicin Into Titania Nanotubes Prevents Evidence of Infection in a Mouse Model of Periprosthetic Joint Infection. Journal of Orthopaedic Research (2025)
Wittrock, A.; Heermant, S.; Beckmann, C.; Wimmer, M.; Fischer, A.; Aßmann, M.; Debus, J.: Protein-metal interactions due to fretting corrosion at the taper junction of hip implants: An in vitro investigation using Raman spectroscopy. Acta Biomaterialia 189, pp. 621 - 632 (2024)
Fara, G. D.; Markovics, A.; Radice, S.; Hamiton, J. L.; Chiesa, R.; Sturm, A.; Angenendt, K.; Fischer, A.; Wimmer, M. A.: Electrophoretic deposition of gentamicin and chitosan into titanium nanotubes to target periprosthetic joint infection. Journal of Biomedical Materials Research Part B-Applied Biomaterials 111 (9), pp. 1697 - 1704 (2023)
Fischer, A.: Wear and Repassivation Rates of Orthopedic Metal Implants in Simulated Healthy and Inflammatory Synovial Fluids. World Tribology Congress 2022, Lyon, France (2022)
Fischer, A.: Ultra-Mild Fretting Wear – A different angle. University of Leeds, School of Mechanical Engineering, Fretting Focus Group Seminar, Leeds, UK (2022)
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
Recent developments in experimental techniques and computer simulations provided the basis to achieve many of the breakthroughs in understanding materials down to the atomic scale. While extremely powerful, these techniques produce more and more complex data, forcing all departments to develop advanced data management and analysis tools as well as…
Integrated Computational Materials Engineering (ICME) is one of the emerging hot topics in Computational Materials Simulation during the last years. It aims at the integration of simulation tools at different length scales and along the processing chain to predict and optimize final component properties.
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.