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At least 1,135 records · Page 63

Reducing waste of the hydride-dehydride process for U-6 wt% Nb spherical powders through lower impact and targeted milling

The breakdown of solid metal into powder during the hydride-dehydride process is commercially important for the formation of titanium and other metal powders. Typically, the milling of the brittle hydride powder occurs in a ball mill, where milling media impacts powder particles to break them down. The milling media can impact particles that are larger than desired, as desired, or smaller than desired, indiscriminately making all particles smaller. In this work, we investigate how to minimize waste powder production during milling using two different milling methods, planetary ball milling and milling in a sieve shaker (sieve-milling). Both processes yielded similar amounts of 20–75 μm diameter powder (the target size range); however, sieve-milling generated a significantly smaller amount of undersized waste powder. The powders were characterized by X-ray diffraction, SEM/STEM, and magnetic susceptibility. Several differences between ball milling and sieve-milling processes are discussed. We then conclude that the decreased yield of undersized powder in sieve-milling was due to a combination of lower impact energy in sieve-milling, unreacted metallic cores in the hydride flakes, and the ability to mill target particle sizes during sieve-milling. While these results are from the milling of brittle hydride powder, similar methods may be applicable to other brittle powders, including ceramics or salts.

Chemistry↗

Comparing Sensor Fusion and Multimodal Chemometric Models for Monitoring U(VI) in Complex Environments Representative of Irradiated Nuclear Fuel

Optical sensors and chemometric models were leveraged for the quantification of uranium(VI) (0–100 μg mL –1 ), europium (0–150 μg mL –1 ), samarium (0–250 μg mL –1 ), praseodymium (0–350 μg mL –1 ), neodymium (0–1000 μg mL –1 ), and HNO 3 (2–4 M) with varying corrosion product (iron, nickel, and chromium) levels using laser fluorescence, Raman scattering, and ultraviolet–visible–near-infrared absorption spectra. In this paper, an efficient approach to developing and evaluating tens of thousands of partial least-squares regression (PLSR) models, built from fused optical spectra or multimodal acquisitions, is discussed. Each PLSR model was optimized with unique preprocessing combinations, and features were selected using genetic algorithm filters. The 7-factor D-optimal design training set contained just 55 samples to minimize the number of samples. The performance of PLSR models was evaluated by using an automated latent variable selection script. PLS1 regression models tailored to each species outperformed a global PLS2 model. PLS1 models built using fused spectra data and a multimodal (i.e., analyzed separately) approach yielded similar information, resulting in percent root-mean-square error of prediction values of 0.9–5.7% for the seven factors. Further, the optical techniques and data processing strategies established in this study allow for the direct analysis of numerous species without measuring luminescence lifetimes or relying on a standard addition approach, making it optimal for near-real-time, in situ measurements. Nuclear reactor modeling helped bound training set conditions and identified elemental ratios of lanthanide fission products to characterize the burnup of irradiated nuclear fuel. Leveraging fluorescence, spectrophotometry, experimental design, and chemometrics can enable the remote quantification and characterization of complex systems with numerous species, monitor system performance, help identify the source of materials, and enable rapid high-throughput experiments in a variety of industrial processes and fundamental studies.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗