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)
New machine learning framework makes metal recycling more sustainable without compromising on mechanical properties. Results now published in journal Science Advances.
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 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…
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.