Probing the hydrolytic degradation of UF4 in humid air.

This manuscript describes the chemical transformations that occur during hydrolysis of uranium tetrafluoride (UF4) due to its storage in humid air (85% and 50% relative humidity) at ambient temperatures. This hydrolysis was previously reported to proceed slowly or not at all (depending on the percent relative humidity); however, previous reports relied primarily on X-ray diffraction methods to probe uranium speciation. In our report, we employ a battery of physiochemical probing techniques to explore potential hydrolysis, including Raman spectroscopy, powder X-ray diffraction, 19F nuclear magnetic resonance spectroscopy, scanning electron microscopy, and focused ion beam microscopy with energy-dispersive X-ray spectroscopy. Of these, only Raman spectroscopy proved to be particularly useful at observing chemical changes to UF4. It was found that anhydrous UF4 slightly oxidizes over the course of thirteen days to Schoepite-like uranium complexes and possibly UO3. In contrast, UF4 exposed to 50% relative humidity slightly decomposes into UO2F2, Schoepite-like uranium complexes, and possibly a high order uranium oxide that eluded chemical assignment (UxOy). Despite the rich chemical speciation observed in our Raman spectroscopy measurements, X-ray diffraction and 19F NMR measurements on the same material showed no changes. Microscopy measurements suggest that the observed reactions between UF4 and water occur primarily on the surface of UF4 particulates via a method that is visually similar to surface corrosion of metals. Therefore, we postulate that NMR spectroscopy and X-ray diffraction, which are well-suited for bulk analysis, are less suited than Raman spectroscopy to observe the surface-based reactions that occur to UF4 when exposed to humid air. Considering the importance of UF4 in the production of nuclear fuel and weapons, the results presented herein are widely applicable to numerous nuclear science fields where uranium detection and speciation in humid environments is of value, including nuclear nonproliferation and nuclear forensics.

Characterizing the solid hydrolysis product, UF4(H2O)2.5, generated from neat water reactions with UF4 at room temperature.

Uranium tetrafluoride (UF4) is an important intermediate in the production of UF6 and uranium metal. Room temperature hydrolysis of UF4 was investigated using a combination of Fluorine-19 nuclear magnetic resonance spectroscopy (19F NMR), Raman and infrared spectroscopy, powder X-ray diffraction, and microscopy measurements. UF4(H2O)2.5 was identified as the primary solid hydrolysis product when anhydrous UF4 was stirred in deionized water. Static NMR and 19F magic angle spinning NMR measurements revealed that a small amount of uranyl fluoride can also form when anhydrous UF4 is left in water, although this species comprises less than 5% of the total sample with the remaining parts being UF4(H2O)2.5. Since UF4 is generally considered to be stable under ambient conditions, these findings mark the first time that a room temperature reaction between UF4 and water has been detected and analyzed without interference from additional chemical reagents. The Raman characterization of UF4(H2O)2.5 presented herein is the first on record. Since UF4 is one of the most used intermediates during chemical conversion of uranium ore to uranium metal for nuclear fuel and weapons, the results presented herein are applicable to numerous nuclear science fields where solid state detection of uranium is of value, including nuclear nonproliferation, nuclear forensics, and environmental remediation.

Investigations of Uranyl Fluoride Sesquihydrate (UO2F2·1.57H2O): Combining 19F Solid-State MAS NMR Spectroscopy and GIPAW Chemical Shift Calculations.

High-resolution 19F magic-angle spinning (MAS) NMR spectra were obtained for the uranium-bearing solid uranyl fluoride sesquihydrate (UO2F2·1.57H2O). While there are seven distinct crystallographic fluorine sites, the 19F NMR spectrum reveals six peaks at -33.3, 9.1, 25.7, 33.0, 39.0, and 48.2 ppm, with the peak at 33.0 ppm twice the intensity of all the others and therefore corresponding to two sites. To assign the peaks in the experimental spectra to crystallographic sites, 19F chemical shifts were calculated using the gauge including projector augmented waves (GIPAW) plane-wave pseudopotential approach for a DFT-optimized crystal structure. The peak assignments from DFT are consistent with two-dimensional double-quantum 19F MAS NMR experiments.

Statistical analysis-based error models for the Microsoft Kinect(TM) depth sensor.

The stochastic error characteristics of the Kinect sensing device are presented for each axis direction. Depth (z) directional error is measured using a flat surface, and horizontal (x) and vertical (y) errors are measured using a novel 3D checkerboard. Results show that the stochastic nature of the Kinect measurement error is affected mostly by the depth at which the object being sensed is located, though radial factors must be considered, as well. Measurement and statistics-based models are presented for the stochastic error in each axis direction, which are based on the location and depth value of empirical data measured for each pixel across the entire field of view. The resulting models are compared against existing Kinect error models, and through these comparisons, the proposed model is shown to be a more sophisticated and precise characterization of the Kinect error distributions.

Coordination chemistry with f-element complexes for an improved understanding of factors that contribute to extraction selectivity.

Here, we highlight some recent accomplishments in f-element coordination chemistry aimed at probing the fundamental chemical differences between the 4f elements, lanthanides, and the 5f elements, actinides. The studies of particular interest are those that target improving our knowledge of fundamental chemistry to aid in increased selectivity for extractions of actinides. Two components key to understanding the challenges of actinide separations are detailed here, namely, previously described separation methods and recent investigations into the fundamental coordination chemistry of actinides. Both are aimed at probing the critical features necessary for improved selectivity of separations. This is considered a critical goal in the safe remediation of contaminated sites and reprocessing of nuclear fuel sources used in either civilian and noncivilian energy production.

Estimates of error probability for complex Gaussian channels with generalized likelihood ratio detection.

We derive approximate expressions for the probability of error in a two-class hypothesis testing problem in which the two hypotheses are characterized by zero-mean complex Gaussian distributions. These error expressions are given in terms of the moments of the test statistic employed and we derive these moments for both the likelihood ratio test, appropriate when class densities are known, and the generalized likelihood ratio test, appropriate when class densities must be estimated from training data. These moments are functions of class distribution parameters which are generally unknown so we develop unbiased moment estimators in terms of the training data. With these, accurate estimates of probability of error can be calculated quickly for both the optimal and plug-in rules from available training data. We present a detailed example of the behavior of these estimators and demonstrate their application to common pattern recognition problems, which include quantifying the incremental value of larger training data collections, evaluating relative geometry in data fusion from multiple sensors, and selecting a good subset of available features.

Quantitative statistical assessment of conditional models for synthetic aperture radar.

Many applications of object recognition in the presence of pose uncertainty rely on statistical models-conditioned on pose-for observations. The image statistics of three-dimensional (3-D) objects are often assumed to belong to a family of distributions with unknown model parameters that vary with one or more continuous-valued pose parameters. Many methods for statistical model assessment, for example the tests of Kolmogorov-Smirnov and K. Pearson, require that all model parameters be fully specified or that sample sizes be large. Assessing pose-dependent models from a finite number of observations over a variety of poses can violate these requirements. However, a large number of small samples, corresponding to unique combinations of object, pose, and pixel location, are often available. We develop methods for model testing which assume a large number of small samples and apply them to the comparison of three models for synthetic aperture radar images of 3-D objects with varying pose. Each model is directly related to the Gaussian distribution and is assessed both in terms of goodness-of-fit and underlying model assumptions, such as independence, known mean, and homoscedasticity. Test results are presented in terms of the functional relationship between a given significance level and the percentage of samples that wold fail a test at that level.