ABSTRACT
Chemical short-range order (CSRO) refers to atoms of specific elements self-organising within a disordered crystalline matrix to form particular atomic neighbourhoods. CSRO is typically characterized indirectly, using volume-averaged or through projection microscopy techniques that fail to capture the three-dimensional atomistic architectures. Here, we present a machine-learning enhanced approach to break the inherent resolution limits of atom probe tomography enabling three-dimensional imaging of multiple CSROs. We showcase our approach by addressing a long-standing question encountered in body-centred-cubic Fe-Al alloys that see anomalous property changes upon heat treatment. We use it to evidence non-statistical B2-CSRO instead of the generally-expected D03-CSRO. We introduce quantitative correlations among annealing temperature, CSRO, and nano-hardness and electrical resistivity. Our approach is further validated on modified D03-CSRO detected in Fe-Ga. The proposed strategy can be generally employed to investigate short/medium/long-range ordering phenomena in different materials and help design future high-performance materials.
ABSTRACT
The boundary between two crystal grains can decompose into arrays of facets with distinct crystallographic character. Faceting occurs to minimize the system's free energy, i.e., when the total interfacial energy of all facets is below that of the topologically shortest interface plane. In a model Al-Zn-Mg-Cu alloy, we show that faceting occurs at investigated grain boundaries and that the local chemistry is strongly correlated with the facet character. The self-consistent coevolution of facet structure and chemistry leads to the formation of periodic segregation patterns of 5-10 nm, or to preferential precipitation. This study shows that segregation-faceting interplay is not limited to bicrystals but exists in bulk engineering Al alloys and hence affects their performance.
