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Organisationseinheit der BAM
Optimizing the properties of next-generation high-temperature and corrosion-resistant alloys is rooted in balancing structure-property relationships and phase chemistry. Here, we implement a complementary approach based on transmission electron microscopy (TEM) and atom probe tomography (APT) to ascertain aspects of hierarchical phase separation behavior, by understanding the microstructural evolution and the three-dimensional (3D) nanochemistry of a single crystal Fe79.5Si15.5V5.0 (at%) alloy. A maze-like hierarchical microstructure forms, in which a complex network of metastable disordered α plates (A2 phase) emerges within ordered α1 precipitates (D03 phase). The supersaturation in α1 (D03) precipitates with Fe and V drives the formation of α (A2) plates. The morphology of α (A2) plates is discussed concerning crystal structure, lattice misfit, and elastic strain. Phase compositions and a ternary phase diagram aid the thermodynamic assessment of the hierarchical phase separation mechanism via the Gibbs energy of mixing. A perspective on the stabilization of hierarchical microstructures beyond Fe79.5Si15.5V5.0 is elaborated by comparing hierarchical alloys. We find that the ratio of elastic anisotropy (Zener ratio) serves as a predictor of the hierarchical particles’ morphology. We suggest that the strengthening effect of hierarchical microstructures can be harnessed by improving the temporal and thermal stability of hierarchical particles. This can be achieved through phase-targeted alloying aiming at the hierarchical particles phase by considering the constituents partitioning behavior. Beyond Fe79.5Si15.5V5.0, our results demonstrate a potential pathway for improving the properties of high-temperature structural materials.
Single particle imaging of upconversion nanoparticles (UCNPs) has typically been realized using hexagonal (β) phase lanthanide-doped sodium yttrium fluoride (NaYF4) materials, the upconversion luminescence (UCL) of which saturates at power densities (P) of several hundred W cm−2 under 980 nm nearinfrared (NIR) excitation. Cubic (α) phase UCNPs have been mostly neglected because of their commonly observed lower UCL efficiency at comparable P in ensemble level studies. Here, we describe a set of sub-15 nm ytterbium-enriched α-NaYbF4:Er3+@CaF2 core/shell UCNPs doped with varying Er3+ concentrations (5–25%), studied over a wide P range of ∼8–105 W cm−2, which emit intense UCL even at a low P of 10 W cm−2 and also saturate at relatively low P. The highest upconversion quantum yield (ΦUC) and the highest particle brightness were obtained for an Er3+ dopant concentration of 12%, reaching the highest ΦUC of 0.77% at a saturation power density (Psat) of 110 W cm−2. These 12%Er3+-doped core/shell UCNPs were also the brightest UCNPs among this series under microscopic conditions at high P of ∼102–105 W cm−2 as demonstrated by imaging studies at the single particle level. Our results underline the potential applicability of the described sub-15 nm cubic-phase core/shell UCNPs for ensemble- and single particle-
level bioimaging.
Grain boundaries (GBs) are planar lattice defects that govern the properties of many types of polycrystalline materials. Hence, their structures have been investigated in great detail. However, much less is known about their chemical features, owing to the experimental difficulties to probe these features at the atomic length scale inside bulk material specimens. Atom probe tomography (APT) is a tool capable of accomplishing this task, with an ability to quantify chemical characteristics at near-atomic scale. Using APT data sets, we present here a machine-learning-based approach for the automated quantification of chemical features of GBs. We trained a convolutional neural network (CNN) using twenty thousand synthesized images of grain interiors, GBs, or triple junctions. Such a trained CNN automatically detects the locations of GBs from APT data. Those GBs are then subjected to compositional mapping and analysis, including revealing their in-plane chemical decoration patterns. We applied this approach to experimentally obtained APT data sets pertaining to three case studies, namely, Ni-P, Pt-Au, and Al-Zn-Mg-Cu alloys. In the first case, we extracted GB specific segregation features as a function of misorientation and coincidence site lattice character. Secondly, we revealed interfacial excesses and in-plane chemical features that could not have been found by standard compositional analyses. Lastly, we tracked the temporal evolution of chemical decoration from early-stage solute GB segregation in the dilute limit to interfacial phase separation, characterized by the evolution of complex composition patterns. This machine-learning-based approach provides quantitative, unbiased, and automated access to GB chemical analyses, serving as an enabling tool for new discoveries related to interface thermodynamics, kinetics, and the associated chemistry-structure-property relations.