Rechmann, J.; Krzywiecki, M.; Erbe, A.: Carbon-Sulfur Bond Cleavage During Adsorption of Octadecane Thiol to Copper in Ethanol. Langmuir 35 (21), pp. 6888 - 6897 (2019)
Krzywiecki, M.; Grządziel, L.; Powroźnik, P.; Kwoka, M.; Rechmann, J.; Erbe, A.: Oxide – organic heterostructures: a case study of charge displacement absence at a SnO2 – copper phthalocyanine buried interface. Physical Chemistry Chemical Physics 20 (23), pp. 16092 - 16101 (2018)
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)
Panther, J.; Rechmann, J.; Müller, T. J. J.: Fischer indole synthesis of 3-benzyl-1H-indole via conductive and dielectric heating. Chemistry of Heterocyclic Compounds 52 (11) (2016)
Rabe, M.; Rechmann, J.; Boyle, A. L.; Erbe, A.: Designing Electro Responsive Self-Assembled Monolayers Based on the Coiled-Coil Peptide Binding Motif. 17th International Conference on Organized Molecular Films” (ICOMF17), New York, NY, USA (2018)
Rechmann, J.: Electron transfer characteristics of gold and oxide-covered copper in aqueous electrolytes modified by self-assembled monolayers. ElecNano8, the 8th international conference on Electrochemistry in Nanosciences
, Nancy, France (2018)
Rechmann, J.: Oberflächenmodifizierung von Zink (Eisen) mit Ethinylphenothiazinen und Charakterisierung. Master, Institut für Organische und Makromolekulare Chemie, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany (2014)
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 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.
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…
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…