Milenkovic, S.; Drensler, S.; Hassel, A. W.: A novel concept for the preparation of alloy nanowires. Physical Status Solidi A-Applications and Materials Science 208 (6), pp. 1259 - 1264 (2011)
Chen, Y.; Milenkovic, S.; Hassel, A. W.: Reactivity of Gold Nanobelts with Unique {110} Facets. A European Journal of Chemical Physics and Physical Chemistry 11 (13), pp. 2838 - 2843 (2010)
Hassel, A. W.; Bello-Rodriguez, B.; Smith, A. J.; Chen, Y.; Milenkovic, S.: Preparation and specific properties of single crystalline metallic nanowires. Physica Status Solidi B 247, pp. 2380 - 2392 (2010)
Milenkovic, S.; Smith, A. J.; Hassel, A. W.: Single crystalline Molybdenum nanowires and nanowire arrays. J. Nanosci. Nanotechnol. 9 (6), pp. 3411 - 3417(7) (2009)
Cimalla, V.; Röhlig, C.-C.; von Pezoldt, J.; Niebelschütz, M.; Ambacher, O.; Brückner, K.; Hein, M.; Weber, J.; Milenkovic, S.; Smith, A. J.et al.; Hassel, A. W.: Nanomechanics of single crystalline tungsten nanowires. J. Nanomater. 2008, pp. 638947 - 638956 (2008)
Brittman, S.; Smith, A. J.; Milenkovic, S.; Hassel, A. W.: Copper Nanowires and Silver Micropore Arrays from the Electrochemical Treatment of a Directionally Solidified Silver-Copper Eutectic. Electrochim. Acta 53, pp. 324 - 329 (2007)
Hassel, A. W.; Milenkovic, S.; Schürmann, U.; Greve, H.; Zaporojtchenko, V.; Adelung, R.; Faupel, F.: Model systems with tuneable geometry and surface functionality for a quantitative investigation of the Lotus effect. Langmuir 23, pp. 2091 - 2094 (2007)
Milenkovic, S.; Hassel, A. W.; Schneider, A.: Effect of the Growth Conditions on the Spatial Features of Re Nanowires Produced by Directional Solidification. Nano Letters 6 (4), pp. 794 - 799 (2006)
Max Planck scientists design a process that merges metal extraction, alloying and processing into one single, eco-friendly step. Their results are now published in the journal Nature.
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
Atom probe tomography (APT) provides three dimensional(3D) chemical mapping of materials at sub nanometer spatial resolution. In this project, we develop machine-learning tools to facilitate the microstructure analysis of APT data sets in a well-controlled way.