Koprek, A.; Cojocaru-Mirédin, O.; Würz, R.; Freysoldt, C.; Gault, B.; Raabe, D.: Cd and Impurity Redistribution at the CdS/CIGS Interface After Annealing of CIGS-Based Solar Cells Resolved by Atom Probe Tomography. IEEE Journal of Photovoltaics 7 (1), 7762819, pp. 313 - 321 (2017)
Stoffers, A.; Cojocaru-Mirédin, O.; Seifert, W.; Zaefferer, S.; Riepe, S.; Raabe, D.: Grain boundary segregation in multicrystalline silicon: correlative characterization by EBSD, EBIC, and atom probe tomography. Progress in Photovoltaics: Research and Applications 23 (12), pp. 1742 - 1753 (2015)
Cojocaru-Mirédin, O.; Choi, P.; Wuerz, R.; Raabe, D.: Exploring the p-n junction region in Cu(In,Ga)Se2 thin-film solar cells at the nanometer-scale. Applied Physics Letters 101 (18), pp. 181603-1 - 181603-5 (2012)
Choi, P.; Cojocaru-Mirédin, O.; Wuerz, R.: Compositional gradients and impurity distributions in CuInSe2 thin-film solar cells studied by atom probe tomography. Surface and Interface Analysis 44 (11-12), pp. 1386 - 1388 (2012)
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 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.