Raacke, J.; Giza, M.; Grundmeier, G.: Combination of FTIR reflection absorption spectroscopy and work function measurement for in-situ studies of plasma modification of polymer and metal surfaces. Surface and Coatings Technology 200 (1-4), pp. 280 - 283 (2005)
Giza, M.; Raacke, J.; Grundmeier, G.: Surface analysis of plasma induced reactions on organic model substrates. 17th International Symposium on Plasma Chemistry, Toronto, Canada, August 07, 2005 - August 12, 2005. (2005)
Giza, M.; Raacke, J.; Grundmeier, G.: Surface analysis of plasma induced reactions on metallic and organic model substrates. 17th International Symposium on Plasma Chemistry, Toronto, Canada (2005)
Raacke, J.; Giza, M.; Grundmeier, G.: In-situ IR-spectroscopic and Kelvin probe investigations of plasma modified model substrates. Ninth International Conference on Plasma Surface Engineering, Garmisch-Partenkirchen, Germany (2004)
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
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…