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126 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Batch Extraction Studies to Evaluate Trace Element Behavior in PUREX Conditions

The multilab Intentional Forensics Venture is working to identify which stable elements (i.e., taggants) at trace concentrations relative to U would persist throughout the nuclear fuel cycle in a voluntary fuel tagging scheme. A taggant would provide the nuclear forensics community with a “barcode” to help identify nuclear materials found outside of regulatory control. A portion of this project was focused on reprocessing effects and determining which, if any, elements would coextract with U(VI) in standard Pu–U reduction extraction (PUREX) conditions. Elements with a propensity to coextract could, in theory, be used as taggants from a PUREX perspective. Although retention is not a performance requirement, the taggant signature would need to partition predictably from the U stream after the PUREX process to maintain forensic utility. This report documents results from several batch extraction studies with numerous trace elements from HNO 3 (1.5–5 M), with and without U(VI), into 30% tri-n-butyl phosphate (TBP) in kerosene. Extraction and back-extraction tests were used to evaluate nearly 60 elements in surrogate conditions for PUREX, and distribution coefficients (i.e., D-values) for most species were <0.1, indicating few species are likely to co-extract with U through PUREX. Additional studies are needed to optimize sample volumes and dilutions to dial in these low D-values. The D-values (D) were determined for several of the more promising elements, including Re and Se. Ultimately, we conclude that only a limited number of the ~ 60 elements investigated are extractable in the U stream of PUREX, based on measured D values, meaning most candidate elemental taggants would likely be lost at this stage of the nuclear fuel cycle, even when considering a range of acid concentrations.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2021

Numerical and Analytical Modeling of Flow Partitioning in Partially Saturated Fracture Networks

Infiltration processes in fractured-porous media remain a crucial, yet not very well understood component of recharge and vulnerability assessment. Under partially-saturated conditions flows in fractures, percolating fracture networks, and fault zones contribute to the fastest spectrum of infiltration velocities via preferential pathways. Specifically, the partitioning dynamics at fracture intersections determine the magnitude of flow fragmentation into vertical and horizontal components and hence the bulk flow velocity and dispersion of fracture networks. In this work, we derive an approximate analytical solution for the partitioning process and validate it using smoothed particle hydrodynamics simulations. The developed transfer function is conceptually based on simulation results and laboratory experiments carried out in previous works. It allows to efficiently simulate flow through fracture networks with simple cubic structure and arbitrary number of fractures and aperture sizes via linear response theory and convolution of a given input signal. We derive a non-dimensional bulk flow velocity ($\widetilde{v}$) and dispersion coefficient ($\widetilde{D}$) to characterize fracture networks in terms of dimensionless horizontal and vertical time scales $\tau_m$ and $\tau_0$. The dispersion coefficient is shown to strongly depend on the horizontal time scale and converges towards a constant value of $0.08$ within reasonable fluid and geometrical parameter ranges, while the non-dimensional velocity exhibits a characteristic $\widetilde{v} \sim \tau_m^{-1/2}$ scaling. Given that hydraulic information is often only available at limited places within (fractured-porous) aquifer system, such as boreholes or springs, our study intends to provide a rudimentary analytical concept to potentially reconstruct internal fracture network geometries from external boundary information, e.g., the dispersive properties of discharge (groundwater level fluctuations).

42 ENGINEERING↗
DOE OSTI2022

New Particle Formation and Growth Dynamics for α-Pinene Ozonolysis in a Smog Chamber and Implications for Ambient Environments

Secondary organic aerosol (SOA) plays an important role in the Earth’s energy balance and air quality. Understanding the formation of SOA accompanying the oxidation of volatile organic compounds (VOCs) remains incomplete because of the complex reactions and partitioning mechanisms. In this study, we investigated new particle formation (NPF) of SOA derived from α-pinene ozonolysis in the Harvard Environmental Chamber using the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) with the condensing species as two products, one of lower volatility (LVOC, 0.01 μg m –3 ) and one of higher volatility (SVOC, 1.0 μg m –3 ). A module for the classical nucleation theory was included. The surface tension (σ), bulk diffusivity (D b ), and mass accommodation coefficient (α) were varied until convergence between the simulated and observed particle number size distributions. α of LVOC was constrained as 0.03–0.1 with the reported σ of α-pinene-ozonolysis-derived SOA and Tolman surface correction (~23.0–27.5 mN m –1 ) and Db from 10 –15.5 to >10 –11 cm 2 s –1 . The observed particle number size distribution of α-pinene ozonolysis was well-simulated to derive the physical parameters, including σ, Db, and α, suggesting the possible application to group the reported complex oxidation products and further application for other atmospheric-related oxidations for quantifying NPF.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Probabilistic partition of unity networks for high–dimensional regression problems

We explore the probabilistic partition of unity network (PPOU-Net) model in the context of high-dimensional regression problems and propose a general framework focusing on adaptive dimensionality reduction. With the proposed framework, the target function is approximated by a mixture of experts model on a low-dimensional manifold, where each cluster is associated with a fixed-degree polynomial. We present a training strategy that leverages the expectation maximization (EM) algorithm. During the training, we alternate between (i) applying gradient descent to update the DNN coefficients; and (ii) using closed-form formulae derived from the EM algorithm to update the mixture of experts model parameters. Under the probabilistic formulation, step (ii) admits the form of embarrassingly paralleliazable weighted least-squares solves. The PPOU-Nets consistently outperform the baseline fully-connected neural networks of comparable sizes in numerical experiments of various data dimensions. Here, we also explore the proposed model in applications of quantum computing, where the PPOU-Nets act as surrogate models for cost landscapes associated with variational quantum circuits.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Uranium and Nickel Partitioning in a Contaminated Riparian Wetland

Uranium (U) and nickel (Ni) released 50 years ago have been immobilized in the Tims Branch wetlands located on the Savannah River Site in the United States. Sediments were collected from seven locations to identify the factors responsible for this attenuation. Ni and U contents in the solids were significantly correlated, suggesting that depositional as opposed to chemical processes contributed to their spatial distribution. Based on sequential extractions, 63 ± 16% of the U was partitioned into the organic fraction, whereas Ni was distributed between several sediment fractions. An inverse pH-organic matter (OM) correlation and positive correlations of OM with total U and organic-bound U/Ni suggest that increased OM preservation and binding to the mineral surfaces were likely responsible for Ni- and especially U-sediment retention (Tims Branch pH = 4.84 ± 0.68). EXAFS analysis indicated the predominance of U(VI) coordinated with clay minerals (~65%), together with ~35% coordinated to either OM (in areas with elevated OM levels) or iron oxides. The desorption-Kd coefficients of U (3972 ± 1370 L/kg) and Ni (30 ± 8 L/kg) indicate that dissolved Ni poses a greater long-term risk than dissolved U for migrating downstream. This study suggests that a delicate balance of geochemical properties controls whether wetlands behave as sinks or sources of contaminants.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2025

A rheological model for loose sands with insights from DEM

A rheological model for loose granular media is developed to capture both solid-like and fluid-like responses during shearing. The proposed model is built by following the mathematical structure of an extended Kelvin–Voigt model, where an elastic spring and plastic slider act in parallel to a viscous damper. This arrangement requires the partition of the total stress into rate-independent and rate-dependent stress components. To model the solid-like behavior, a simple frictional plasticity model is adopted without modifications, thus contributing to the rate-independent stress. Instead, the fluid-like or rate-dependent stress is further decomposed into deviatoric and volumetric parts, by proposing a new formulation based on a combination of the μ(I) relation, originally developed under pressure-controlled shear, with a pressure-shear rate relation derived under volume-controlled shear. The proposed formulation allows the model to capture both the increase in the friction coefficient and the enhanced dilation at high shear rates. High-fidelity simulation data, obtained from discrete element method and multiscale modelling, are used to evaluate the performance of the proposed constitutive model. The model provides accurate results under both drained and undrained simple shear paths across a wide range of shear rates. Furthermore, it successfully reproduces at much lower computational cost the flowslide mobility computed through multiscale simulations, which is primarily regulated by the shear rate dependence of the material properties during the dynamic runout stage.

Elasticity↗
DOE OSTI2024

EPCAPE-PT-LANL Measurements: Photoacoustic Extinctometer

Coastal cities offer a unique environment for studying aerosol-cloud interactions and the effects of urban emissions on cloud properties. As part of the Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE), the Partitioning Thrust by Los Alamos National Laboratory (EPCAPE-PT-LANL) was conducted. Our campaign focused on measuring the optical and chemical properties of aerosols and their interactions within marine stratocumulus clouds in La Jolla, California. EPCAPE-PT-LANL enhances the primary goals of EPCAPE through innovative observations of vapor-phase transitions between aerosols and cloud droplets, the impact of black carbon on aerosol-cloud dynamics, and the effects of cloud processing on aerosol optical properties. Instrument: Photoacoustic Extinctiometer at 870 nm (Droplet Measurement Technology) Header: - AbsorptionNoise_870nm[1/Mm]: Noise level associated with the absorption measurement at 870 nm, expressed in inverse megameters (Mm⁻¹). - Absorption_870nm[1/Mm]: Absorption coefficient of aerosols measured at 870 nm, expressed in inverse megameters (Mm⁻¹). - Extinction_870nm[1/Mm]: Extinction coefficient of aerosols measured at 870 nm, expressing the sum of scattering and absorption by aerosols, in inverse megameters (Mm⁻¹). - Scattering_870nm[1/Mm]: Scattering coefficient of aerosols measured at 870 nm, expressed in inverse megameters (Mm⁻¹). - BackgroundAbsorption_870nm[1/Mm]: Background absorption measurement at 870 nm, used to correct the primary absorption data, expressed in inverse megameters (Mm⁻¹). - BackgroundScattering_870nm[1/Mm]: Background scattering measurement at 870 nm, used to correct the primary scattering data, expressed in inverse megameters (Mm⁻¹). - Temperature[C]: The ambient temperature at the time of the measurement, expressed in degrees Celsius. - RelativeHumidity[%]: The relative humidity at the time of the measurement, expressed as a percentage. - CVI_Flag[bool]: A boolean flag indicating whether the Counterflow Virtual Impactor (CVI) was active (true) or inactive (false) during the measurement

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

EPCAPE-PT-LANL Measurements: Humidified Cavity Attenuated Phase Shift Spectroscopy

Coastal cities offer a unique environment for studying aerosol-cloud interactions and the effects of urban emissions on cloud properties. As part of the Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE), the Partitioning Thrust by Los Alamos National Laboratory (EPCAPE-PT-LANL) was conducted. Our campaign focused on measuring the optical and chemical properties of aerosols and their interactions within marine stratocumulus clouds in La Jolla, California. EPCAPE-PT-LANL enhances the primary goals of EPCAPE through innovative observations of vapor-phase transitions between aerosols and cloud droplets, the impact of black carbon on aerosol-cloud dynamics, and the effects of cloud processing on aerosol optical properties. Instrument: Humidified Cavity Attenuated Phase Shift Particulate Matter Single Scattering Albedo (H-CAPS-PMSSA, Aerodyne Inc) Data Notes: The scattering truncation correction was not applied to Bsca. The Bext needs no correction. Files: data_10sec_CAPS.csv, data_10min_CAPS.csv Header: - Bext_wet_CAPS_450nm[1/Mm]: Wet aerosol extinction coefficient measured at 450 nm by the CAPS, in inverse megameters (Mm⁻¹). - Bsca_wet_CAPS_450nm[1/Mm]: Wet aerosol scattering coefficient measured at 450 nm by the CAPS, in inverse megameters (Mm⁻¹). - Temp_wet_CAPS[K]: Temperature inside the wet CAPS measurement chamber, in Kelvin. - Bext_dry_CAPS_450nm[1/Mm]: Dry aerosol extinction coefficient measured at 450 nm by the CAPS, in inverse megameters (Mm⁻¹). - Bsca_dry_CAPS_450nm[1/Mm]: Dry aerosol scattering coefficient measured at 450 nm by the CAPS, in inverse megameters (Mm⁻¹). - Temp_dry_CAPS[K]: Temperature inside the dry CAPS measurement chamber, in Kelvin. - Wet_RH_preCAPS[%]: Relative humidity before entering the wet CAPS, in percent. - Wet_RH_postCAPS[%]: Relative humidity after exiting the wet CAPS, in percent. - Humidifier_RH_CAPS[%]: Relative humidity inside the humidifier used with the CAPS, in percent. - dualCAPS_inlet_RH[%]: Relative humidity at the inlet of the dual (wet/dry) CAPS setup, in percent. - Wet_Temp_preCAPS[C]: Temperature before entering the wet CAPS, in degrees Celsius. - Wet_Temp_postCAPS[C]: Temperature after exiting the wet CAPS, in degrees Celsius. - Humidifier_Temp[C]: Temperature inside the humidifier used with the CAPS, in degrees Celsius. - dualCAPS_inlet_Temp[C]: Temperature at the inlet of the dual (wet/dry) CAPS setup, in degrees Celsius. - Zero_dry_CAPS: 1 is a calibration check for the dry CAPS to ensure zero reading under filter air conditions. - Zero_wet_CAPS: 1 is a calibration check for the wet CAPS to ensure zero reading under filter air conditions. - CVI_Flag[bool]: A boolean flag indicating whether the Counterflow Virtual Impactor (CVI) was active (true) or inactive (false) during the measurement.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Evolving radon diffusion through earthen barriers at uranium waste disposal sites

Field measurements of Rn-222 fluxes from the tops and bottoms of compacted clay radon barriers were used to calculate effective Rn diffusion coefficients (D Rn ) at four uranium waste disposal sites in the western United States to assess cover performance after more than 20 years of service. Values of D Rn ranged from 7.4 × 10 -7 to 6.0 × 10 -9 m 2 /s, averaging 1.42 × 10 -7 . Water saturation (S W ) from soil cores indicated that there was relatively little control of D Rn by S W , especially at higher moisture levels, in contrast to estimates from most steady-state diffusion models. Further, this is attributed to preferential pathways intrinsic to construction of the barriers or to natural process that have developed over time including desiccation cracks, root channels, and insect burrows in the engineered earthen barriers. A modification to some models in which fast and slow pathway D Rn values are partitioned appears to give a good representation of the data; 4% of the fast pathway was needed to fit the data regression. For locations with high S w and highest D Rn (and fluxes) at each site, the proportion of fast pathway ranged from 1.7% to 34%, but for many locations with lower fluxes, little if any fast pathway was needed.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Influences of Hydrogen Bonding-Based Stabilization of Bolaamphiphile Layers on Molecular Diffusion within Organic Nanotubes Having Inner Carboxyl Groups

This paper reports molecular diffusion behavior in two bolaamphiphile-based organic nanotubes having inner carboxyl groups with different inner dimeters (10 and 20 nm) and wall structures, COOH-ONT 10nm and COOH-ONT 20nm , using imaging fluorescence correlation spectroscopy (imaging FCS). Here, the results were compared to those previously obtained in a similar nanotube with inner amine groups (NH 2 -ONT 10nm ). COOH-ONT 10nm , as with NH 2 -ONT 10nm , were formed from a rolled bolaamphiphile layer incorporating triglycine moieties, whereas COOH-ONT 20nm consisted of four stacks of triglycine-free bolaamphiphile layers. Imaging FCS measurements were carried out for anionic sulforhodamine B (SRB), zwitterionic/cationic rhodamine B (RB) and cationic rhodamine-123 (R123) diffusing within ONTs (1–9 µm long) at different pH (3.4–8.4) and ionic strengths (1.6–500 mM). Diffusion coefficients (D) of these dyes in the ONTs were very small (0.01–0.1 µm 2 /s), reflecting the significant contributions of molecule-nanotube interactions to diffusion. The D of SRB was larger at higher pH and ionic strength, indicating the essential role of electrostatic repulsion that was enhanced by the deprotonation of the inner carboxyl groups. Importantly, the D of SRB was virtually independent of nanotube inner diameter and wall structure, indicating the diffusion of the hydrophilic molecule was controlled by short timescale adsorption/desorption processes onto the inner surface. In contrast, pH effects on D were less clear for relatively hydrophobic R123 and RB, suggesting the significant contributions of non-Coulombic interactions. Interestingly, the diffusion of these molecules in COOH-ONT 20nm was slower than in COOH-ONT 10nm . Slower diffusion in COOH-ONT20nm was attributable to relatively efficient partitioning of the hydrophobic dyes into the bolaamphiphile layers, which was reduced in COOH-ONT 10nm due to the stabilization of its layer by polyglycine-II-type hydrogen bonding networks. These results show that, by tuning the bolaamphiphile structures and their intermolecular interactions, unique environments can be created within the nanospaces for enhanced molecular separations and reactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Building a machine learning surrogate model for wildfire activities within a global Earth system model

Abstract. Wildfire is an important ecosystem process, influencing land biogeophysical and biogeochemical dynamics and atmospheric composition. Fire-driven loss of vegetation cover, for example, directly modifies the surface energy budget as a consequence of changing albedo, surface roughness, and partitioning of sensible and latent heat fluxes. Carbon dioxide and methane emitted by fires contribute to a positive atmospheric forcing, whereas emissions of carbonaceous aerosols may contribute to surface cooling. Process-based modeling of wildfires in Earth system land models is challenging due to limited understanding of human, climate, and ecosystem controls on fire counts, fire size, and burned area. Integration of mechanistic wildfire models within Earth system models requires careful parameter calibration, which is computationally expensive and subject to equifinality. To explore alternative approaches, we present a deep neural network (DNN) scheme that surrogates the process-based wildfire model with the Energy Exascale Earth System Model (E3SM) interface. The DNN wildfire model accurately simulates observed burned area with over 90 % higher accuracy with a large reduction in parameterization time compared with the current process-based wildfire model. The surrogate wildfire model successfully captured the observed monthly regional burned area during validation period 2011 to 2015 (coefficient of determination, R2=0.93). Since the DNN wildfire model has the same input and output requirements as the E3SM process-based wildfire model, our results demonstrate the applicability of machine learning for high accuracy and efficient large-scale land model development and predictions.

58 GEOSCIENCES↗
DOE OSTI2022

Black hole scattering and partition functions

When computing the ideal gas thermal canonical partition function for a scalar outside a black hole horizon, one encounters the divergent single-particle density of states (DOS) due to the continuous nature of the normal mode spectrum. Recasting the Lorentzian field equation into an effective 1D scattering problem, we argue that the scattering phases encode non-trivial information about the DOS and can be extracted by “renormalizing” the DOS with respect to a reference. This defines a renormalized free energy up to an arbitrary additive constant. Interestingly, we discover that the 1-loop Euclidean path integral, as computed by the Denef-Hartnoll-Sachdev formula, fixes the reference free energy to be that on a Rindler-like region, and the renormalized DOS captures the quasinormal modes for the scalar. We support these claims with the examples of scalars on static BTZ, Nariai black holes and the de Sitter static patch. For black holes in asymptotically flat space, the renormalized DOS is captured by the phase of the transmission coefficient whose magnitude squared is the greybody factor. We comment on possible connections with recent works from an algebraic point of view.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Estimation of secondary organic aerosol viscosity from explicit modeling of gas-phase oxidation of isoprene and α -pinene

Abstract. Secondary organic aerosols (SOA) are major components of atmospheric fine particulate matter, affecting climate and air quality. Mounting evidence exists that SOA can adopt glassy and viscous semisolid states, impacting formation and partitioning of SOA. In this study, we apply the GECKO-A (Generator of Explicit Chemistry and Kinetics of Organics in the Atmosphere) model to conduct explicit chemical modeling of isoprene photooxidation and α-pinene ozonolysis and their subsequent SOA formation. The detailed gas-phase chemical schemes from GECKO-A are implemented into a box model and coupled to our recently developed glass transition temperature parameterizations, allowing us to predict SOA viscosity. The effects of chemical composition, relative humidity, mass loadings and mass accommodation on particle viscosity are investigated in comparison with measurements of SOA viscosity. The simulated viscosity of isoprene SOA agrees well with viscosity measurements as a function of relative humidity, while the model underestimates viscosity of α-pinene SOA by a few orders of magnitude. This difference may be due to missing processes in the model, including autoxidation and particle-phase reactions, leading to the formation of high-molar-mass compounds that would increase particle viscosity. Additional simulations imply that kinetic limitations of bulk diffusion and reduction in mass accommodation coefficient may play a role in enhancing particle viscosity by suppressing condensation of semi-volatile compounds. The developed model is a useful tool for analysis and investigation of the interplay among gas-phase reactions, particle chemical composition and SOA phase state.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Photoinhibition of comammox reaction in Nitrospira inopinata in a dose- and wavelength-dependent manner

Apparent contribution of complete ammonia-oxidizing organisms (comammox) to the global nitrogen cycle highlights the necessity for understanding niche differentiation of comammox bacteria among other ammonia oxidizers. While the high affinity for ammonia of the comammox species Nitrospira inopinata suggests their niche partitioning is expected to be centered in oligotrophic environments, their absence in nutrient-depleted environments (such as the oceans) suggests that other (abiotic) factors might control their distribution and spatial localization within microbial communities. Many ammonia- and nitrite-oxidizing organisms are sensitive to light; however, the photosensitivity of comammox has not been explored. Since comammox bacteria encode enzymatic machinery homologous to canonical ammonia-and nitrite-oxidizers, we hypothesized that comammox N. inopinata, the only available pure culture of this group of microorganisms, may be inhibited by illumination in a similar manner. We evaluated the impact of light intensity, wavelength, and duration on the degree of photoinhibition for cultures of the comammox species N. inopinata and the soil ammonia-oxidizing archaea Nitrososphaera viennensis. Both species were highly sensitive to light. Interestingly, mimicking diurnal light exposure caused an uncoupling of ammonia and nitrite oxidation in N. inopinata, indicating nitrite oxidation might be more sensitive to light exposure than ammonia oxidation. It is likely that light influences comammox spatial distribution in natural environments such as surface fresh waters according to diurnal cycles, light attenuation coefficients, and the light penetration depths. Our findings therefore provide ecophysiological insights for further studies on comammox both in field and laboratory settings.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Mountain Basin Controls on the Snow-to-Streamflow Signal: An AIC-Weighted Multiple Linear Regression Framework

A regression-based analysis quantifies how basin characteristics modulate the snow-to-streamflow signal. First, we use the ERA5-Land reanalysis gridded product (European Centre for Medium Range Weather Forecasts reanalysis 5 -Land component) for 4,655 hydrologic unit code - 10 (HUC10) mountain basins across the western United States (US) for water years 1987–2024. Linear regressions are performed for peak snow water equivalent (SWE) and annual streamflow for each mountain basin. Models use ordinary least squares in Python’s statsmodels package. After which, an Akaike Information Criterion (AIC)–weighted ensemble multiple linear regression (MLR) framework with 47 watershed traits is used to predict the linear regression coefficient of determination (r-squared) defining the ability of peak SWE to predict annual streamflow across all mountain basin. Predictor sets are constrained to avoid multicollinearity by excluding models with variance inflation factors (VIF) greater than 5. Mountain basin traits included in the MLR include seasonal climate, topography, vegetation type and structure, and bedrock geology. Accepted models are considered if their AIC is within 2.0 of the model with the minimum AIC, or best model. To compare predictor influence across acceptable models, we computed standardized regression coefficients. To evaluate structural redundancy among models, we constructed binary inclusion vectors for each acceptable model, denoting whether a predictor was present (1) or absent (0). Core predictor variables are defined as occurring in at least 67% of the acceptable models. For this regional analysis, only one model was found acceptable, with higher snow-to-streamflow translation (higher r-squared) occurring in colder mountain basins with higher relative winter precipitation, more snow accumulation and a lower fraction of annual precipitation that falls in the spring and summer. The second component of the data package uses previously published, high-resolution output from an integrated hydrological model of the East River watershed using the U.S. Geological Survey Groundwater and Surface water Flow model (GSFLOW, doi:10.15485/1998576). East River MLR expands upon the approach described above to explore the response of five streamflow metrics—annual streamflow, runoff efficiency, 7-day minimum flow, low-flow duration, and non-perennial stream fraction to snow system indicators including peak SWE, snow-covered area, snow disappearance date, and the fraction of basin area characterized by low-to-no snow, as well as seasonal precipitation and temperature, and annual hydrologic variables representing soil moisture, evapotranspiration (ET), the partitioning of incoming precipitation to evapotranspiration (ET/P), groundwater storage, and groundwater inflow to streams. MLR was done on all water years (P0: 1987-2024) and for each period as determined in the split analysis using pooled regression techniques (P1: 1987-2011 and P2: 2012-2024) to evaluate shifting predictor variable emphasis on streamflow generation. Results indicate that since 2012, peak SWE has lost statistical strength in its prediction of annual streamflow and runoff efficiency, and the indirect influence of spring temperature has emerged as critically important. Low-flow metrics remain largely influenced by soil moisture, vegetation water use and groundwater inflows with summer precipitation becoming a direct influence on minimum summer flow. Together, these data and Python-based analysis tools provide a framework for identifying the key watershed characteristics that control how streamflow responds to snow from year to year. The package also helps quantify uncertainty in statistical models and assess how snow–streamflow relationships vary across regions and over time. This dataset contains comma-separated values files (.csv), text files (.txt), python code files (.py), figure files (.png), and shapefiles (.cpg, .dbf, .prj, .sbn, .sbx, .shp, .xml). Further details on file contents and MLR execution can be found in the readme file and the FLMD files. Work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Serial crystallography with multi-stage merging of thousands of images

KAMO and BLEND provide particularly effective tools to automatically manage the merging of large numbers of data sets from serial crystallography. The requirement for manual intervention in the process can be reduced by extending BLEND to support additional clustering options such as the use of more accurate cell distance metrics and the use of reflection-intensity correlation coefficients to infer `distances' among sets of reflections. This increases the sensitivity to differences in unit-cell parameters and allows clustering to assemble nearly complete data sets on the basis of intensity or amplitude differences. If the data sets are already sufficiently complete to permit it, one applies KAMO once and clusters the data using intensities only. When starting from incomplete data sets, one applies KAMO twice, first using unit-cell parameters. In this step, either the simple cell vector distance of the original BLEND or the more sensitive NCDist is used. This step tends to find clusters of sufficient size such that, when merged, each cluster is sufficiently complete to allow reflection intensities or amplitudes to be compared. One then uses KAMO again using the correlation between reflections with a common hkl to merge clusters in a way that is sensitive to structural differences that may not have perturbed the unit-cell parameters sufficiently to make meaningful clusters. Many groups have developed effective clustering algorithms that use a measurable physical parameter from each diffraction still or wedge to cluster the data into categories which then can be merged, one hopes, to yield the electron density from a single protein form. Since these physical parameters are often largely independent of one another, it should be possible to greatly improve the efficacy of data-clustering software by using a multi-stage partitioning strategy. Here, one possible approach to multi-stage data clustering is demonstrated. The strategy is to use unit-cell clustering until the merged data are sufficiently complete and then to use intensity-based clustering. Using this strategy, it is demonstrated that it is possible to accurately cluster data sets from crystals that have subtle differences.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Elucidating the speciation of extracted lanthanides by diglycolamides

Many studies over the past few decades have been devoted to addressing the application of diglycolamides (DGAs) for hydrometallurgical-based, f-element separations. Work to date has shown the molecular structure of a DGA derivative can have a significant impact on intra-lanthanide partitioning patterns. More recent studies have pushed towards probing the structure function relationship of the lanthanide-DGA complex to enable the design of more efficient lanthanide separation systems. Spectroscopic techniques, such as UV–Visible, Fourier Transformed Infrared Spectroscopy (FT-IR), Nuclear Magnetic Resonance (NMR), and Extended X-ray Absorption Fine Structure (EXAFS), provide information regarding the inner-sphere coordination of a given lanthanide-DGA complex. Scattering techniques, such as Dynamic Light Scattering (DLS), Small-Angle X-ray Scattering (SAXS), and Small-Angle Neutron Scattering (SANS), address nanoscale structures including aggregate sizes and morphology. Here, this review assesses the current state-of-knowledge regarding lanthanide-DGA hydrometallurgical (i.e., solvent extraction systems) interrogated using spectroscopic and scattering techniques to characterize the extracted Ln 3+ DGA species. Of particular interest to this review is the impact of varied diluents, inclusion and variation of phase modifiers, and DGA derivatization on system characteristics. While there has been extensive literature on the application of DGAs for f-element separations, the literature lacks a collective assessment of the speciation of Ln 3+ in the organic phase. This review provides new insights into the field of DGA separations, explicitly with an application to intra-lanthanide separations. Specifically, this review illustrates the importance of both the co-extraction anion (Cl - , NO 3 - , or ClO 4 - ) as it pertains to both the aggregate size and Ln 3+ distribution coefficient. It is evident the ability of the anion to disrupt the hydrogen bonding network limits both aggregate size and distribution coefficients according to the Hoffmeister series. This suggests the importance of the large, softer anions with a low charge-to-surface area ratio on encouraging hydrogen bond interactions. In addition, the co-extracted cation (H + vs Na + ) is important for mitigating transfer of Ln 3+ from the aqueous to the organic phase through extensive hydrogen bonding networks. These networks are responsible for forming supramolecular aggregates where a change in morphology is observed with increasing concentrations of H + and/or Ln 3+ in the organic phase.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Implementation and evaluation of multi-dual mode counter-current chromatography in the CUP Modeler software

Counter-current chromatography (CCC) is a separation technique that utilizes immiscible solvent pairs as stationary and mobile phases, which imparts numerous benefits compared to solid-liquid chromatography including the ability to treat either the more-dense or less-dense solvent layer as the mobile phase. Multi-dual mode (MDM) is a CCC elution mode capable of improving the separation of closely eluting compounds by alternating upper- and lower-layer solvent flows in opposing directions within the same separation. While some effort has been made to model MDM, implementation of these models in experimental design has yet to be widely adopted. Accordingly, we further developed our previously published cell utilized partitioning (CUP) model to include MDM predictions with CCC and packaged the full suite of CUP modeling capabilities into a user-friendly, open-source tool called the CUP Modeler. The mathematical model for MDM CCC was derived and validated with experimental separation of ethyl guaiacol (EG) and ethyl phenol (EP), two compounds that co-elute in our previously demonstrated reductive catalytic fractionation (RCF) lignin monomer isolation method. The developed MDM model provided insights into the effect of multiple operating parameters - including stationary phase retention, flow rate, column efficiency, feed concentration ratio, selectivity factor, and solute distribution ratios - on the separation yields, productivity, and purities. Our model agreed with prevailing understanding of MDM but also revealed new insights including that the ideal distribution ratios for co-eluting solutes to be separated by MDM is between 1.1 and 1.5, with the lower value ideally close to 1.25. Overall, this work provides fundamental insights for MDM process design and enables broader adoption of general liquid-liquid chromatography with a new, open-source user-friendly interface.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗