Isotta, E.: Investigating microstructure via thermal conductivity imaging: from grain boundaries, to phase segregations and material anisotropy. 50th International Conference and Expo on Advanced Ceramics and Composites (ICACC 2026), Daytona Beach, FL, USA (2026)
Isotta, E.: Investigating microstructure via thermal conductivity imaging: from grain boundaries, to material anisotropy, and phase segregations. Invited Seminar at RWTH Aachen, Physics Department, Aachen, Germany (2025)
Isotta, E.: Thermal conductivity imaging to advance microstructure engineering in thermoelectric and energy materials. Materials Science and Technology Meeting (MSandT) 2025, Columbus, OH, USA (2025)
Isotta, E.; Zhang, S.; Ghosh, S.; de Boor, J.; Balogun, O.; Snyder, G. J.; Scheu, C.: Thermal conductivity imaging to advance microstructure engineering in thermoelectrics. European Conference on Thermoelectrics 2025, Nancy, France (2025)
Isotta, E.: Thermal conductivity imaging to guide microstructure engineering in energy materials. Invited Seminar at the Karlsruhe Institute of Technology, Karlsruhe, Germany (2025)
Isotta, E.: Thermal conductivity imaging to guide microstructure engineering in energy materials. Invited Seminar at the German Aerospace Center in Cologne, Köln, Germany (2025)
Isotta, E.: Thermal conductivity imaging to guide microstructure engineering in energy materials. Iberian Workshop on Thermoelectrics 2025, Castello de la Plana, Spain (2025)
Isotta, E.: Local thermal conductivity imaging and modelling to guide microstructure engineering in energy materials. TMS 2025 Annual Meeting, Las Vegas, NV, USA (2025)
Isotta, E.: Thermal conductivity imaging to guide microstructure engineering in energy materials. Invited Seminar at the Institute of Science and Technology Austria, Klosterneuburg, Austria (2024)
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)
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
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.
New product development in the steel industry nowadays requires faster development of the new alloys with increased complexity. Moreover, for these complex new steel grades, it is more challenging to control their properties during the process chain. This leads to more experimental testing, more plant trials and also higher rejections due to…
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…
Crystal Plasticity (CP) modeling [1] is a powerful and well established computational materials science tool to investigate mechanical structure–property relations in crystalline materials. It has been successfully applied to study diverse micromechanical phenomena ranging from strain hardening in single crystals to texture evolution in…
Advanced microscopy and spectroscopy offer unique opportunities to study the structure, composition, and bonding state of individual atoms from within complex, engineering materials. Such information can be collected at a spatial resolution of as small as 0.1 nm with the help of aberration correction.