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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Analytical characterization of laser induced plasmas towards uranium isotopic analysis in gaseous uranium hexafluoride

To perform direct enrichment assay on gaseous uranium hexafluoride (UF 6 ) with laser induced breakdown spectroscopy (LIBS), the dominant spectral-line features, evolution of the signal and background of the U II 424.437 nm line, and its Stark width and shift, were studied as a function of UF 6 gas pressure and pulse energy of a nanosecond Nd:YAG laser. In this work, vapor pressure of UF 6 was found to be the most important parameter for LIBS analysis of gaseous UF 6 . Spectral congestion with numerous U lines of high excitation potential was observed and signal-to-background ratio (SBR) was low for measurements with 80 Torr UF 6 . Only when both UF 6 vapor pressure and laser pulse energy were low, for example, less than 20 Torr pressure and 30 mJ pulse energy, the resultant LIBS spectra from gaseous UF 6 resembled those obtained from solid U samples. The experimental data also suggest that U and F atoms recombine back to UF 6 after the laser pulse. The U emission was found to decay fast with a persistent background signal, degrading SBR with delay time. Systematic positive biases were found for UF 6 enrichment assays performed with the 235 U– 238 U line pair at 424.412–424.437 nm, which was confirmed to be caused by self-absorption. Even with optimization of experimental parameters and incorporation of a self-absorption term into the spectral-fitting algorithm, to reduce and compensate for self-absorption, self-absorption is still a main factor limiting accurate UF 6 enrichment assay. The use of another spectral window which contains no resonance lines is a prospective solution for the self-absorption issue.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2024

Estimating soil N 2 O emissions induced by organic and inorganic fertilizer inputs using a Tier-2, regression-based meta-analytic approach for U.S. agricultural lands

Consistent methods are essential for generating country and region-specific estimates of greenhouse gas (GHG) emissions used for reporting and policymaking. The estimates of direct N 2 O emissions from U.S. agricultural soils have primarily relied on the use of emission factors (EFs, Tier-1) and process-based models (Tier-3). However, Tier-1 estimates are relatively crude while Tier-3 calculations can be costly. This work addressed this gap by developing a Tier-2, regression-based approach by leveraging a meta-database containing 1883 field N 2 O observations together with environmental and management covariates from 139 studies. Our results estimated higher monthly soil N 2 O emissions (N 2 O m , kg N/ha) during the growing season (0.38) than the fallow period (0.15), highlighting the importance of considering measurement periods when utilizing meta-databases for analyzing N 2 O drivers. Significantly different N 2 O m were found for tillage practices (conventional > no-till: 0.42 > 0.27), fertilizer type (liquid > solid manure: 0.55 > 0.32), and soil texture (fine > coarse: 0.36 > 0.22). The comparisons of the influence of crop type and rotation, water management, and soil order on N 2 O emissions are complicated by regional data availability and interactions among different factors. Additionally, the finding that N 2 O emissions reported based on area (N 2 O m ), N input rate (EF), or yield can alter treatment rankings underscores the need to establish transparent criteria for rewarding or discouraging regionally-based management practices using N 2 O metrics. Finally, we show how General Linear Models (GLMs) can be used to estimate country and regional Tier-2 N 2 O m using a suite of covariates. Our GLMs identified tillage, water management, N input type and rate, soil properties, and elevation as the most influential covariates for the conterminous U.S. The limited accuracy of regional-scale GLMs, however, suggests the need to further improve the quality and availability of GHG and covariate data through concerted efforts in data collection.

54 ENVIRONMENTAL SCIENCES↗