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Register a new userCo-precipitation approach to measure amount of $^{238}$U in copper to sub-ppt level using ICP-MS
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Inductively coupled plasma mass (ICP-MS) spectroscopy is widely used for screening materials of low background detectors in dark matter and double beta decay searches due to its high sensitivity to trace $^{238}$U and $^{232}$Th. This work describes a novel co-precipitation approach to measure the amount of $^{238}$U in high-purity copper to sub-ppt level. Such an approach allows the pre-concentration of U and removal of the matrix, by selecting a proper precipitator to co-precipitate with $^{238}$U and using excess ammonia water to separate the uranium hydroxide from copper by forming water-soluble tetra-amminecopper (II). The isotope dilution method and standard addition method were both used to mitigate the matrix effect and cross-check each other. The latter was also used to measure the recovery efficiency of $^{238}$U by using $^{233}$U as the tracer. The method detection limit (MDL) reached $sim$0.1 pg $^{238}$U /g Cu for both methods while the recovery efficiency of uranium robustly remains 65%--85%. Various sources of interference in the ICP-MS analysis were thoroughly evaluated, and the contamination from reagents were found to be the dominant factor that affected the MDL. Further purification will allow significant improvements in the MDL. This co-precipitate approach can be easily extended to measure $^{232}$Th by using $^{229}$Th as the tracer.
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Inductively coupled plasma mass spectroscopy is a powerful technique for measuring trace levels of radioactive contaminants, specifically Th and U, in materials for use in construction of low-background rare-event detectors such as double beta decay and dark matter detectors. I describe here a technique for measuring Th and U contamination in copper using direct acid digestion and dilution, without further chemical processing, achieving results comparable to previous work which utilized more complex chemical pre-concentration techniques. A convenient research-oriented analysis environment is described as well. Results are presented for measurements of three samples from the production line of electrolytically-purified, LME (London Metal Exchange) grade A, NA-ESN Aurubis copper. Purified samples showed levels consistent with zero contamination for both elements, while weak but inconclusive indications of contamination were present for the un-purified anode copper. The best limits achieved are near $1cdot 10^{-12}$~g/g (95% CL) for both Th and U measured for copper from the cathode of the purification process.
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Quantal diffusion mechanism of nucleon exchange is studied in the central collisions of $^{238}$U + $^{238}$U in the framework of the stochastic mean-field (SMF) approach. For bombarding energies considered in this work, the di-nuclear structure is maintained during the collision. Hence, it is possible to describe nucleon exchange as a diffusion process for mass and charge asymmetry. Quantal neutron and proton diffusion coefficients, including memory effects, are extracted from the SMF approach and the primary fragment distributions are calculated.
In mobile robotics, scan matching of point clouds using Iterative Closest Point (ICP) allows estimating sensor displacements. It may prove important to assess the associated uncertainty about the obtained rigid transformation, especially for sensor fusion purposes. In this paper we propose a novel approach to 3D uncertainty of ICP that accounts for all the sources of error as listed in Censis pioneering work [1], namely wrong convergence, underconstrained situations, and sensor noise. Our approach builds on two facts. First, the uncertainty about the ICPs output fully depends on the initialization accuracy. Thus speaking of the covariance of ICP makes sense only in relation to the initialization uncertainty, which generally stems from odometry errors. We capture this using the unscented transform, which also reflects correlations between initial and final uncertainties. Then, assuming white sensor noise leads to overoptimism as ICP is biased owing to e.g. calibration biases, which we account for. Our solution is tested on publicly available real data ranging from structured to unstructured environments, where our algorithm predicts consistent results with actual uncertainty, and compares favorably to previous methods.
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