Li, Y.; Gault, B.: Machine Learning-enabled Tomographic Imaging of Chemical Short-range Order in Fe-based Solid-solutions. DPG 2024, Berlin, Germany (2024)
Li, Y.; Gault, B.: Machine Learning-enabled Tomographic Imaging of Chemical Short-range Order in Fe-based Solid-solutions. TMS 2024, Orlando, FL, USA (2024)
Li, Y.; Gault, B.: Quantitative three-dimensional imaging of chemical short-range order via machine learning enhanced atom probe tomography. BiGmax Spring Meeting 2022, Bochum, Germany (2022)
International researcher team presents a novel microstructure design strategy for lean medium-manganese steels with optimized properties in the journal Science
This project aims to investigate the influence of grain boundaries on mechanical behavior at ultra-high strain rates and low temperatures. For this micropillar compressions on copper bi-crystals containing different grain boundaries will be performed.
The objective of the project is to investigate grain boundary precipitation in comparison to bulk precipitation in a model Al-Zn-Mg-Cu alloy during aging.
This project aims to develop a testing methodology for the nano-scale samples inside an SEM using a high-speed nanomechanical low-load sensor (nano-Newton load resolution) and high-speed dark-field differential phase contrast imaging-based scanning transmission electron microscopy (STEM) sensor.
Understanding hydrogen-microstructure interactions in metallic alloys and composites is a key issue in the development of low-carbon-emission energy by e.g. fuel cells, or the prevention of detrimental phenomena such as hydrogen embrittlement. We develop and test infrastructure, through in-situ nanoindentation and related techniques, to study…