Duarte, M. J.; Fang, X.; Rao, J.; Krieger, W.; Brinckmann, S.; Dehm, G.: In situ nanoindentation during electrochemical hydrogen charging: a comparison between front-side and a novel back-side charging approach. Journal of Materials Science 56 (14), pp. 8732 - 8744 (2021)
Luo, W.; Kirchlechner, C.; Fang, X.; Brinckmann, S.; Dehm, G.; Stein, F.: Influence of composition and crystal structure on the fracture toughness of NbCo2 Laves phase studied by micro-cantilever bending tests. Materials and Design 145, pp. 116 - 121 (2018)
Li, Y.; Fang, X.; Zhang, S.; Feng, X.: Microstructure evolution of FeNiCr alloy induced by stress-oxidation coupling using high temperature nanoindentation. Corrosion Science 135, pp. 192 - 196 (2018)
Yue, M.; Dong, X.; Fang, X.; Feng, X.: Effect of interface reaction and diffusion on stress-oxidation coupling at high temperature. Journal of Applied Physics 123 (15), 155301 (2018)
Fang, X.; Dong, X.; Jiang, D.; Feng, X.: Modification of the mechanism for stress-aided grain boundary oxidation ahead of cracks. Oxidation of Metals 89 (3-4), pp. 331 - 338 (2018)
Lu, S.-Y.; Chen, Y.; Fang, X.; Feng, X.: Hydrogen peroxide sensor based on electrodeposited Prussian blue film. Journal of Applied Electrochemistry 47 (11), pp. 1261 - 1271 (2017)
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 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…
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.