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Still, Evan K.

Publications and source records attributed to Still, Evan K..

Directly resolving surface vs. lattice self-diffusion in iron at the nanoscale using in situ atom probe capabilities

Surface self-diffusion studies on metals under elevated reaction conditions are limited, as it is inherently challenging to unambiguously follow atomic transport across highly-reactive surfaces. Here, quantitative and mechanistic insight into thermally induced atomic transport processes in bcc α-iron at the sub-nanometer level was achieved using isotopic tracer techniques coupled with in situ atom probe tomography (APT) capabilities. Specifically, using a reactor directly connected to the APT, needle-shaped specimens fabricated from epitaxial thin films with an embedded 57 Fe tracer layer were annealed in Ar at 500 °C and 350 °C for 1 hour. Furthermore, the tracer was positioned at various depths in the APT specimen by field evaporation, enabling targeted and simultaneous analysis of lattice and surface diffusion. 57 Fe concentration profiles reveal lattice self-diffusion occurs at 500 °C on the order of ~7 – 9 monolayers, while lattice diffusion is not resolvable at 350 °C. Considerable surface transport was, however, observed at both conditions, where atomic transport over the specimen surface led to the formation of a thin (≤1 nm), isotopically-intermixed layer at the surface. Further, the observed isotopic redistributions at 500 °C were convoluted by additional processes occurring in the subsurface, such as atomic intermixing in correlation with lattice diffusion. However, surface diffusion was determined to be the primary transport process at 350 °C and was thereby quantified. Ultimately, these results demonstrate the significance of surface self-diffusion as a short circuit pathway. More broadly, this approach has the potential to provide detailed insight into (self-)diffusion mechanisms across various materials while targeting site-specific reactions under elevated reaction conditions.

36 MATERIALS SCIENCE↗

Bulk and Short-Circuit Anion Diffusion in Epitaxial Fe 2 O 3 Films Quantified Using Buried Isotopic Tracer Layers

Self-diffusion is a fundamental physical process that, in solid materials, is intimately correlated with both microstructure and functional properties. In this work, a universal approach is presented to precisely characterize self-diffusion in ionic solids by isotopically enriching anions and/or cations at specific locations within an epitaxial film stack, and characterize their redistribution at high spatial resolution with atom probe tomography. Nanoscale anion diffusivity is quantified in epitaxial α-Fe 2 O 3 thin films deposited by molecular beam epitaxy with a thin (10 nm) buried layer highly enriched in 18 O. The isotopic sensitivity of the atom probe allows precise measurement of 18 O distribution across the sharp interfaces between this layer and the surrounding Fe 2 O 3 after annealing. Short-circuit anion diffusion through 1D and 2D structural defects in Fe 2 O 3 are also directly visualized in 3D. This versatile approach to study precisely tailored thin film samples at high spatial and mass fidelity will facilitate a deeper understanding of atomic-scale diffusion phenomena.

36 MATERIALS SCIENCE↗

Alpha Shape Analysis (ASA) Framework for Post- Clustering Property Determination in Atom Probe Tomographic Data

While application of clustering algorithms to atom probe tomography data have enabled quantification of solute clusters in terms of number density, size, and subcomposition there exist other properties (e.g., volume, surface area, and composition) that are better determined by defining an interface between the cluster and the surrounding matrix. The limitation in composition results from an ion selection step where the expected matrix ion types are omitted from the cluster search algorithm to enhance the contrast between the matrix and cluster and to reduce the complexity of the search. Previously, composition determination within solute clusters has utilized a secondary envelopment and erosion step on top of conventional methods such as maximum separation. In this work, we present a novel stochastic method that combines the particle identification fidelity of a conventional clustering algorithm with the analytical flexibility of mesh-based approaches through the generation of alpha shapes for each identified cluster. Furthermore, the corresponding mesh accounts for concave components of the clusters and determines the volume and surface area of the clusters; additionally, the mesh boundary is utilized to update the total composition according to the internal ions.

42 ENGINEERING↗