Busch, F.; Balogun, O.; Snyder, G. J.; Scheu, C.; Isotta, E.: Unravelling grain boundary influences on electronic and lattice thermal conductivity in Mn-doped SnTe thermoelectrics. 21st European Conference on Thermoelectrics (ECT) 2025, Nancy, Frankreich (2025)
Mattlat, D. A.; Jung, C.; Ying, P.; Li, J.; He, S.; Bahrami, A.; Zhang, S.; Scheu, C.: New method of FIB TEM sample preparation for in situ heating and biasing MEMS chip used for investigation on Zn4Sb3 thermoelectric material. Microscopy Conference (MC) 2025, Karlsruhe, Germany (2025)
Mattlat, D. A.; Bueno Villoro, R.; Jung, C.; Naderloo, R. H.; He, R.; Nielsch, K.; Zavanelli, D.; Snyder, G. J.; Zhang, S.; Scheu, C.: Electron microscopy characterization of grain boundaries in Nb1-xTixFeSb based half-Heusler thermoelectric materials. Electron Microscopy Congress (EMC) 2024, Copenhagen, Denmark (2024)
Mattlat, D. A.; Bueno Villoro, R.; Jung, C.; Scheu, C.; Zhang, S.; Naderloo, R. H.; Nielsch, K.; He, .; Zavanelli, D.; Snyder, G. J.: Effective doping of InSbat the grain boundaries in Nb1-xTixFeSb based Half-Heusler thermoelectricsfor high electrical conductivity and Seebeckcoefficient. 40th International & 20th European Conference on Thermoelectrics, Krakow, Poland (accepted)
Bueno Villoro, R.; Zavanelli, D.; Jung, C.; Mattlat, D. A.; Naderloo, R. H.; Pérez, N. A.; Nielsch, K.; Snyder, G. J.; Scheu, C.; He, R.et al.; Zhang, S.: Grain Boundary Phases in NbFeSb Half-Heusler Alloys: A New Avenue to Tune Transport Properties of Thermoelectric Materials. Microscopy of semiconducting materials conference, Cambridge, UK (2023)
Bueno Villoro, R.; Luo, T.; Bishara, H.; Abdellaoui, L.; Gault, B.; Wood, M.; Snyder, G. J.; Scheu, C.; Zhang, S.: Effect of grain boundaries on electrical conductivity in Ti(Co,Fe)Sb half Heusler thermoelectrics. 719. WE-Heraeus-Seminar, Understanding Transport Processes on the Nanoscale for Energy Harvesting Devices, online (2021)
Aymerich Armengol, R.; Lim, J.; Ledendecker, M.; Scheu, C.: Structure-property relationship studies of Pt/TiO2 nanomaterials for electrochemical applications. International Workshop on Advanced and In-situ Microscopies of Functional Nanomaterials and Devices, IAMNano 2019 , Düsseldorf, Germany (2019)
Changizi, R.; Lim, J.; Zhang, S.; Schwarz, T.; Scheu, C.: Characterization of KCa2Nb3O10. IAMNano 2019, International Workshop on Advanced and In-situ Microscopies of Functional Nanomaterials and Devices, Düsseldorf, Germany (2019)
Gänsler, T.; Hengge, K. A.; Scheu, C.: 3D Reconstruction of Identical Location Electron Micrographs – Methodology and Pitfalls. IAMNano 2019, International Workshop on Advanced and In-situ Microscopies of Functional Nanomaterials and Devices, Düsseldorf, Germany (2019)
Sahu, R.; Singh Negi, D.; Scheu, C.: Local strain field in distorted 1T (1Td) MoS2 phases by GPA. International Workshop on Advanced and In-situ Microscopies of Functional Nanomaterials and Devices, IAMNano 2019, Düsseldorf, Germany (2019)
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.
Data-rich experiments such as scanning transmission electron microscopy (STEM) provide large amounts of multi-dimensional raw data that encodes, via correlations or hierarchical patterns, much of the underlying materials physics. With modern instrumentation, data generation tends to be faster than human analysis, and the full information content is…
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.