Mondragón Ochooa, J. S.; Altin, A.; Rechmann, J.; Erbe, A.: Delamination Kinetics of Thin Film Poly(acrylate) Model Coatings Prepared by Surface Initiated Atom Transfer Radical Polymerization on Iron. Journal of the Electrochemical Society 165 (16), pp. C991 - C998 (2018)
Mondragon Ochoa, J. S.; Altin, A.; Erbe, A.: Comparison of cathodic delamination of poly(n-alkyl methacrylates) on iron. Materials and Corrosion - Werkstoffe und Korrosion 68, pp. 1326 - 1332 (2017)
Mondragon Ochoa, J. S.; Altin, A.; Rohwerder, M.; Erbe, A.: Surface Modification of Iron With Grafted Hydrophobic Acrylic Polymers and Study of Their Delamination Kinetics. Polymers and Organic Chemistry POC16, Hersonissos (Crete), Greece (2016)
Mondragon Ochoa, J. S.: Preparation of Polyacrylic Thin Films on Iron by Controlled Radical Polymerization and their Delamination Behaviour. Dissertation, Ruhr-Universität Bochum, Bochum, Germany (2018)
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…