Gault, B.; Shoji Aota, L.; Krämer, M.; Kim, S.-H.: From impurity ingress to high-performance doping: A perspective on atom probe tomography in energy materials. Scripta Materialia 262, 116648 (2025)
Camuti, L.; Kim, S.-H.; Podjaski, F.; Vega-Paredes, M.; Mingers, A. M.; Acartürk, T.; Starke, U.; Lotsch, B. V.; Scheu, C.; Gault, B.et al.; Zhang, S.: Kinetics and direct imaging of electrochemically formed palladium hydride for efficient hydrogen evolution reaction. Physics > Chemical Physics (2025)
Kraemer, M.; Favelukis, B.; Sokol, M.; Rosen, B. A.; Eliaz, N.; Kim, S.-H.; Gault, B.: Facilitating Atom Probe Tomography of 2D MXene Films by In Situ Sputtering. Microscopy and Microanalysis 30 (6), pp. 1057 - 1065 (2024)
Jang, K.; Kim, M.-Y.; Jung, C.; Kim, S.-H.; Choi, D.; Park, S.-C.; Scheu, C.; Choi, P.-P.: Direct Observation of Trace Elements in Barium Titanate of Multilayer Ceramic Capacitors Using Atom Probe Tomography. Microscopy and Microanalysis 30 (6), pp. 1047 - 1056 (2024)
Sharma, V. M.; Svetlizky, D.; Das, M.; Tevet, O.; Krämer, M.; Kim, S.-H.; Gault, B.; Eliaz, N.: Microstructure and mechanical properties of bulk NiTi shape memory alloy fabricated using directed energy deposition. Additive Manufacturing 86, 104224 (2024)
Krämer, M.; Favelukis, B.; El-Zoka, A.; Sokol, M.; Rosen, B. A.; Eliaz, N.; Kim, S.-H.; Gault, B.: Near-Atomic Scale Perspective on the Oxidation of Ti3C2Tx MXenes: Insights from Atom Probe Tomography. Advanced Materials 23 (3), 2305183 (2024)
Woods, E.; Singh, M. P.; Kim, S.-H.; Schwarz, T.; Douglas, J. O.; El-Zoka, A.; Giulani, F.; Gault, B.: A versatile and reproducible cryo-sample preparation methodology for atom probe studies. Microscopy and Microanalysis, ozad120 29 (6), pp. 1992 - 2003 (2023)
Yoo, S.-H.; Aota, L. S.; Shin, S.; El-Zoka, A. A.; Kang, P. W.; Lee, Y.; Lee, H.; Kim, S.-H.; Gault, B.: Dopant Evolution in Electrocatalysts after Hydrogen Oxidation Reaction in an Alkaline Environment. ACS Energy Letters 8 (8), pp. 3381 - 3386 (2023)
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
Integrated Computational Materials Engineering (ICME) is one of the emerging hot topics in Computational Materials Simulation during the last years. It aims at the integration of simulation tools at different length scales and along the processing chain to predict and optimize final component properties.