Density and Thermal Expansivity of Molten 2LiF-BeF2 (FLiBe): Measurements and Uncertainty Quantification.

The variability among prior data for FLiBe is 11% for the liquid density and 61% for the thermal expansivity. New liquid density and thermal expansivity data are collected, with particular attention to uncertainty quantification. We discuss and quantify bounds for possible sources of variability in the measurements of liquid density: salt composition (<0.6% per 1 mol % BeF2), salt contaminants at 100 s ppm to <1 mol% (2%), Li isotopic composition (2%), sample isothermal conditions (0.2%), dissolved gases (<0.3%), and evolution of bubbles with temperature transients - depending on Ar or He cover gas (0.1 or 0.6% for dilatometry, 1 or 5% for hydrostatic measurements). To aid in quantifying thermal expansivity sensitivity to composition, we review and generalize the ideal molar volume prediction for FLiBe; to improve this model, measurements are needed for the thermal expansivity of BeF2. We collect new data on the density of liquid FLiBe using the hydrostatic method and 170 g of hydrofluorinated FLiBe with less than 0.13 mol % impurities (dominantly Al, K, Na, Mg, Ca), as determined by ICP-MS. We obtain the following: The dominant sources of uncertainty are the bobber volume uncertainty (0.15%), the mass measurement uncertainty (0.2%), and possibly the wetting angle of the salt on the wire (<0.3%). Occasional noise and <0.2% deviation from linearity may be due to the formation of gas bubbles on the bobber surface from the temperature-dependence of gas solubility; repeatable results for heating and cooling runs provide confidence that bubble effects are well managed in this experimental setup. These are the first measurements of the liquid density of FLiBe that report error analysis and that measure the liquid composition before and after density measurements.

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. 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.