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DOE OSTI2022

Synthesis and Characterization of Magnesium Vanadates as Potential Magnesium‐Ion Cathode Materials through an Ab Initio Guided Carbothermal Reduction Approach**

Abstract Many technologically relevant materials for advanced energy storage and catalysis feature reduced transition‐metal (TM) oxides that are often nontrivial to prepare because of the need to control the reducing nature of the atmosphere in which they are synthesized. Herein, we show that an ab initio predictive synthesis strategy can be used to produce multi‐gram‐scale products of various MgV x O y ‐type phases (δ‐MgV 2 O 5 , spinel MgV 2 O 4 , and MgVO 3 ) containing V 3+ or V 4+ relevant for Mg‐ion battery cathodes. Characterization of these phases using 25 Mg solid‐state NMR spectroscopy illustrates the potential of 25 Mg NMR for studying reversible magnesiation and local charge distributions. Rotor‐assisted population transfer (RAPT) is used as a much‐needed signal‐to‐noise enhancement technique. The ab initio guided synthesis method is seen as a step forward towards a predictive synthesis strategy for targeting specific complex TM oxides with variable oxidation states of technological importance.

Lee, Jeongjae↗

DOE OSTI2021

Synthesis and Characterization of Magnesium Vanadates as Potential Magnesium-Ion Cathode Materials through an Ab Initio Guided Carbothermal Reduction Approach**

Many technologically relevant materials for advanced energy storage and catalysis feature reduced transition-metal (TM) oxides that are often nontrivial to prepare because of the need to control the reducing nature of the atmosphere in which they are synthesized. In this report, we show that an ab initio predictive synthesis strategy can be used to produce multi-gram-scale products of various MgV x O y -type phases (δ-MgV 2 O 5 , spinel MgV 2 O 4 , and MgVO 3 ) containing V 3+ or V 4+ relevant for Mg-ion battery cathodes. Characterization of these phases using 25 Mg solid-state NMR spectroscopy illustrates the potential of 25 Mg NMR for studying reversible magnesiation and local charge distributions. Rotor-assisted population transfer (RAPT) is used as a much-needed signal-to-noise enhancement technique. The ab initio guided synthesis method is seen as a step forward towards a predictive synthesis strategy for targeting specific complex TM oxides with variable oxidation states of technological importance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Investigating the opioid epidemic across the United States: Associations between county-level characteristics and overdose mortality

The opioid crisis remains a critical public health challenge in the United States. Despite national efforts that reduced opioid prescribing by nearly 44% between 2011 and 2021, opioid overdose deaths more than tripled during the same period. This alarming trend reflects a major shift in the crisis, with illegal opioids now driving the majority of overdose deaths instead of prescription opioids. Although supply-side factors fueling this transition have been widely studied, the structural and community-level conditions that shape overdose mortality are less well understood. To help address this gap, this study has three primary objectives: (1) overcome structural gaps in national data to construct a complete nationwide county-level dataset from 2010 to 2022; (2) using data analysis, identify and investigate spatiotemporal anomalies in overdose mortality; and (3) using two machine-learning models, quantify the importance of thirteen social vulnerability variables in predicting overdose mortality. Our results identify unemployment and limited vehicle access as key county-level predictors of overdose mortality. Higher levels of these vulnerabilities are associated with elevated mortality, whereas lower levels are associated with reduced mortality. These findings highlight factors that may be relevant for public health planning and policy prioritization within the context of the opioid crisis.

Anomaly analysis↗

DOE OSTI2021

Machine learning based algorithms for uncertainty quantification in numerical weather prediction models

Complex numerical weather prediction models incorporate a variety of physical processes, each described by multiple alternative physical schemes with specific parameters. The selection of the physical schemes and the choice of the corresponding physical parameters during model configuration can significantly impact the accuracy of model forecasts. There is no combination of physical schemes that works best for all times, at all locations, and under all conditions. It is therefore of considerable interest to understand the interplay between the choice of physics and the accuracy of the resulting forecasts under different conditions. This paper demonstrates the use of machine learning techniques to study the uncertainty in numerical weather prediction models due to the interaction of multiple physical processes. The first problem addressed herein is the estimation of systematic model errors in output quantities of interest at future times, and the use of this information to improve the model forecasts. The second problem considered is the identification of those specific physical processes that contribute most to the forecast uncertainty in the quantity of interest under specified meteorological conditions. In order to address these questions we employ two machine learning approaches, random forests and artificial neural networks. The discrepancies between model results and observations at past times are used to learn the relationships between the choice of physical processes and the resulting forecast errors. Numerical experiments are carried out with the Weather Research and Forecasting (WRF) model. The output quantity of interest is the model precipitation, a variable that is both extremely important and very challenging to forecast. The physical processes under consideration include various micro-physics schemes, cumulus parameterizations, short wave, and long wave radiation schemes. The experiments demonstrate the strong potential of machine learning approaches to aid the study of model errors.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Storage-Induced Collapse of Lignin Macromolecular Structure and Its Impacts on the Biorefinery

Lignin plays a vital role in the economics of biorefineries, serving as a source of process energy and a feedstock for sustainable fuels and chemical production. While understanding lignin’s chemical composition is crucial, emerging evidence suggests that a more comprehensive understanding of its macromolecular structure is critical to explaining its complex behavior in the biorefinery. This study investigated the partial collapse of the lignin network in corn stover feedstock after harvest and storage as a result of the microbial digestion of hemicellulose. Fluorescence microscopy was used to detect the collapse of lignin in terms of lignin’s inter-molecular interaction and the re-orientation of lignin’s chromophores, by the changes in lignin’s fluorescence lifetime, anisotropy, and the number of effective emitters. With minimal sample perturbation, our in-situ microscopic results revealed lignin's coil-globule transition phenomena, which was only previously predicted by molecular dynamics modeling extracted lignin in solvent. This collapse of lignin macromolecular structure was confirmed by results from NMR, IR, Raman, and powder X-ray diffraction. We also investigated the impact of this storage-induced collapse on the downstream biorefinery processes. Our study revealed that the two major approaches for lignin valorization in the lignin-first biorefinery model, namely monomer extraction and milled wood lignin extraction, were negatively impacted by the lignin collapse. As changes during storage are a source of feedstock variability, our study highlights the importance of understanding the effect of feedstock handling on biorefinery operations and economics.

09 BIOMASS FUELS↗

DOE OSTI2023

Integration of Yeast Episomal/Integrative Plasmid Causes Genotypic and Phenotypic Diversity and Improved Sesquiterpene Production in Metabolically Engineered Saccharomyces cerevisiae

The variability in phenotypic outcomes among biological replicates in engineered microbial factories presents a captivating mystery. Establishing the association between phenotypic variability and genetic drivers is important to solve this intricate puzzle. Here, we applied a previously developed auxin-inducible depletion of hexokinase 2 as a metabolic engineering strategy for improved nerolidol production in Saccharomyces cerevisiae, and biological replicates exhibit a dichotomy in nerolidol production of either 3.5 or 2.5 g L –1 nerolidol. Harnessing Oxford Nanopore’s long-read genomic sequencing, we reveal a potential genetic cause—the chromosome integration of a 2μ sequence-based yeast episomal plasmid, encoding the expression cassettes for nerolidol synthetic enzymes. This finding was reinforced through chromosome integration revalidation, engineering nerolidol and valencene production strains, and generating a diverse pool of yeast clones, each uniquely fingerprinted by gene copy numbers, plasmid integrations, other genomic rearrangements, protein expression levels, growth rate, and target product productivities. Τhe best clone in two strains produced 3.5 g L –1 nerolidol and ~0.96 g L –1 valencene. Comparable genotypic and phenotypic variations were also generated through the integration of a yeast integrative plasmid lacking 2μ sequences. Our work shows that multiple factors, including plasmid integration status, subchromosomal location, gene copy number, sesquiterpene synthase expression level, and genome rearrangement, together play a complicated determinant role on the productivities of sesquiterpene product. Integration of yeast episomal/integrative plasmids may be used as a versatile method for increasing the diversity and optimizing the efficiency of yeast cell factories, thereby uncovering metabolic control mechanisms.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Subseasonal Clustering of Atmospheric Rivers Over the Western United States

Abstract The serial occurrence of atmospheric rivers (ARs) along the US West Coast can lead to prolonged and exacerbated hydrologic impacts, threatening flood‐control and water‐supply infrastructure due to soil saturation and diminished recovery time between storms. Here a statistical approach for quantifying subseasonal temporal clustering among extreme events is applied to a 41‐year (1979–2019) wintertime AR catalog across the western United States (US). Observed AR occurrence, compared against a randomly distributed AR timeseries with the same average event density, reveals temporal clustering at a greater‐than‐random rate across the western US with a distinct geographical pattern. Compared to the Pacific Northwest, significant AR clusters over the northern Coastal Range of California and Sierra Nevada are more frequent and occur over longer time periods. Clusters along the California Coastal Range typically persist for 2 weeks, are composed of 4–5 ARs per cluster, and account for over 85% of total AR occurrence. Across the northwest Coast‐Cascade Ranges, clusters account for ∼50% of total AR occurrence, typically last 8–10 days, and contain 3–4 individual AR events. Based on precipitation data from a high‐resolution dynamical downscaling of reanalysis, the fractions of total and extreme hourly precipitation attributable to AR clusters are largest along the northern California coast and in the Sierra Nevada. Interannual variability among clusters highlights their importance for determining whether a particular water year is anomalously wet or dry. The mechanisms behind this unusual clustering are unclear and require further research.

Meteorology & Atmospheric Sciences↗

DOE OSTI2024

Estimates of Lake Nitrogen, Phosphorus, and Chlorophyll‐ a Concentrations to Characterize Harmful Algal Bloom Risk Across the United States

Abstract Excess nutrient pollution contributes to the formation of harmful algal blooms (HABs) that compromise fisheries and recreation and that can directly endanger human and animal health via cyanotoxins. Efforts to quantify the occurrence, drivers, and severity of HABs across large areas is difficult due to the resource intensive nature of field monitoring of lake nutrient and chlorophyll‐aconcentrations. To better characterize how nutrients interact with other environmental factors to produce algal blooms in freshwater systems, we used spatially explicit and temporally matched climate, landscape, in‐lake characteristic, and nutrient inventory data sets to predict nutrients and chlorophyll‐aacross the conterminous US (CONUS). Using a nested modeling approach, three random forest (RF) models were trained to explain the spatiotemporal variation in total nitrogen (TN), total phosphorus (TP), and chlorophyll‐aconcentrations across US EPA's National Lakes Assessment (n = 2,062). Concentrations of TN and TP were the most important predictors and, with other variables, the RF model accounted for 68% of variation in chlorophyll‐a. We then used these RF models to extrapolate lake TN and TP predictions to lakes without nutrient observations and predict chlorophyll‐afor ∼112,000 lakes across the CONUS. Risk for high chlorophyll‐aconcentrations is highest in the agriculturally dominated Midwest, but other areas of risk emerge in nutrient pollution hot spots across the country. These catchment and lake‐specific results can help managers identify potential nutrient pollution and chlorophyll‐ahot spots that may fuel blooms, prioritize at‐risk lakes for additional monitoring, and optimize management to protect human health and other environmental end goals.

Environmental Sciences & Ecology↗

DOE OSTI2025

The Influence of Land‐Surface Conditions on the 2020–2021 Western US Drought

Abstract In summer 2021, 90% of the western United States (WUS) experienced drought, with over half of the region facing extreme or exceptional conditions, leading to water scarcity, crop loss, ecological degradation, and significant socio‐economic consequences. Beyond the established influence of oceanic forcing and internal atmospheric variability, this study highlights the importance of land‐surface conditions in the development of the 2020–2021 WUS drought, using observational data analysis and novel numerical simulations. Our results demonstrate that the soil moisture state preceding a meteorological drought, due to its intrinsic memory, is a critical factor in the development of soil droughts. Specifically, wet soil conditions can delay the transition from meteorological to soil droughts by several months or even nullify the effects of La Niña‐driven meteorological droughts, while drier conditions can exacerbate these impacts, leading to more severe soil droughts. For the same reason, soil droughts can persist well beyond the end of meteorological droughts. Our numerical experiments suggest a relatively weak soil moisture‐precipitation coupling during this drought period, corroborating the primary contributions of the ocean and atmosphere to this meteorological drought. Additionally, drought‐induced vegetation losses can mitigate soil droughts by reducing evapotranspiration and slowing the depletion of soil moisture. This study highlights the importance of soil moisture and vegetation conditions in seasonal‐to‐interannual drought predictions. Findings from this study have implications for regions like the WUS, which are experiencing anthropogenically‐driven soil aridification and vegetation greening, suggesting that future soil droughts in these areas may develop more rapidly, become more severe, and persist longer.

Jiang, Yelin [Lamont‐Doherty Earth Observatory Col↗

DOE OSTI2021

Surfactant-assisted one-pot sample preparation for label-free single-cell proteomics

Large numbers of cells are generally required for quantitative global proteome profiling due to surface adsorption losses associated with sample processing. Such bulk measurement obscures important cell-to-cell variability (cell heterogeneity) and makes proteomic profiling impossible for rare cell populations (e.g., circulating tumor cells (CTCs)). Here we report a surfactant-assisted one-pot sample preparation coupled with mass spectrometry (MS) method termed SOP-MS for label-free global single-cell proteomics. SOP-MS capitalizes on the combination of a MS-compatible nonionic surfactant, n-Dodecyl-β-D-maltoside, and hydrophobic surface-based low-bind tubes or multi-well plates for ‘all-in-one’ one-pot sample preparation. This ‘all-in-one’ method including elimination of all sample transfer steps maximally reduces surface adsorption losses for effective processing of single cells, thus improving detection sensitivity for single-cell proteomics. This method allows convenient label-free quantification of hundreds of proteins from single human cells and ~1200 proteins from small tissue sections (close to ~20 cells). When applied to a patient CTC-derived xenograft (PCDX) model at the single-cell resolution, SOP-MS can reveal distinct protein signatures between primary tumor cells and early metastatic lung cells, which are related to the selection pressure of anti-tumor immunity during breast cancer metastasis. The approach paves the way for routine, precise, quantitative single-cell proteomics.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Design and analysis of dudded fuel experiments at the National Ignition Facility

Recent experiments conducted at the National Ignition Facility (NIF) within the past 2 years have achieved the burning plasma state and exceeded the Lawson criterion for the first time in the laboratory. Here, we report on a set of experiments where the deuterium and tritium (DT) ice layers were replaced with dudded tritium, hydrogen, and deuterium (THD) fuel mixtures to remove the influence of alpha-heating on hot spot dynamics. The hot spot compression and yield in the absence of alpha particle self-heating were measured to assess the proximity of NIF implosions toward the ignition cliff. We find that the “burn-off” Lawson parameters χnoα inferred from the THD experiments are in good agreement with the inferences from postshot simulations of the DT-layered implosions. The THD for burning plasma shot N210307 yielded χnoα≈0.88±0.03 while the THD for ignition shot N210808 yielded χnoα≈1.04±0.04. These results also provide important context for the observed variability in the repeat attempts of ignition shot N210808 since implosions on the ignition cliff are expected to exhibit very large variations in the fusion yield from small changes in the initial conditions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

An Integrated Assessment of a G3 GMD Event on Large-Scale Power Grids: From Magnetometer Data to Geomagnetically Induced Current Analysis

Solar activities can cause geomagnetic disturbances (GMDs) that give rise to geomagnetically induced currents (GICs) which may compromise the reliability of the power system. In order to build more reliable models representing GMD interactions with the power grid, the power system’s detailed electrical model must be considered along with fluctuations in the earth’s magnetic and induced surface electric fields. Here, this study investigates the impact of incorporating spatially varying magnetic fields into surface electric field models on GMD risk metrics. A spatially independent magnetic field model and a spatially varying model are compared through simulations. To perform this analysis, the earth’s magnetic field disturbances are transformed into surface electric fields using respective one-dimensional earth conductivity models. Then, the modeling impact of these electric fields is studied using a 2,000-bus grid for Texas and a 25,000-bus grid for the northeast and mid- Atlantic regions of the United States. Simulation results reveal that the inclusion of spatially varying magnetic fields results in considerable differences in GMD risk metrics, highlighting the importance of accounting for spatial variability when assessing GMD risks in the power system.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Prestack Waveform Inversion at GC 955: Trials and sensitivity of PWI to high-resolution seismic data (Data Report)

Understanding the spatial extent and variability of hydrate deposits has important implications for potential economic production, climate change, and assessing natural hazards risks. Seismic reflection techniques are capable of determining the extent of gas hydrate deposits and can shed light on hydrate concentrations and properties. Using high resolution prestack time migrated seismic data collected during a 2013 US Geological Survey expedition and prestack waveform inversion (PWI) techniques, we produce highly resolved velocity models and compare them to co-located well logs. Coupling our PWI results with velocity-porosity relationships and nearby well control, we compare hydrate properties at site 955 in the Green Canyon and site 313 at Walker Ridge.

03 NATURAL GAS↗

DOE OSTI2024

Workshop Summary: Bridging the Gap Between Atmospheric Science and Grid Integration

The need for dedicated, accurate, expertly curated weather data is increasingly important as the share of variable renewable energy increases on the power system. Projections for futures with very high (50+% annual energy) shares of variable generation require ongoing assessment of data requirements from industry stakeholders in their power system operation and planning contexts. In March 2024, NREL organized a workshop entitled "Bridging the Gap Between Atmospheric Science and Grid Integration Workshop", which brought atmospheric scientists and power system experts together to refine the requirements of atmospheric datasets for grid integration, and to describe a holistic approach to creating new and regularly updated national scale wind datasets for power system planning and operations. The results of this workshop are being used to inform the near-term development and a longer-term strategy for DOE to produce relevant wind resource datasets and inform wider use of wind/solar/load data sets in power system planning. This presentation provides an overview of a preworkshop survey, an assessment of current state of the art of national-scale datasets for wind resource assessment and grid integration, insights on appropriate uses of the WTK-LED, power system perspectives on data needs, as well as recommended next steps as discussed in the workshop and how these steps support longer-term strategies.

17 WIND ENERGY↗

DOE OSTI2026

Quantitative Evaluation of Potassium Iodide Implementation Strategies for Emergency Preparedness and Response

This study evaluates the effectiveness of potassium iodide (KI) distribution strategies in mitigating exposure to radioiodine during severe nuclear power plant accidents. This analysis quantitatively compares various KI distribution methods (pre-distributed versus stockpiles), including scenarios with and without KI administration. The results indicate that differences in distribution strategies impact the projected thyroid dose by at least an order of magnitude. The results also indicate that the timing of KI administration is critical, as expected. For scenarios involving delayed releases of significant quantities of radionuclides, evacuation is the most effective protection strategy regardless of KI distribution method. For scenarios involving rapid releases, retrieving KI from stockpiles can have a detrimental effect. Pre-distributed KI is potentially the most effective approach when used as a supplement to evacuation and sheltering. However, these model results are based on idealized conditions for KI distribution and administration; the actual benefits of KI prophylaxis are likely to be less than estimated in this report due to many variables. The results highlight the importance of considering the cost and rigor of different distribution programs and public compliance with emergency instructions. This report includes suggested research to explore KI distribution plans for advanced reactors.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

NMDQi Nuclear Materials Discovery and Qualification Initiative Conference Overview

The Nuclear Materials Discovery and Qualification Initiative (NMDQi) is designed to accelerate nuclear materials qualification to fulfill the promises of early and advanced reactor technologies as a safe, clean, and low-cost baseload energy. Materials development and qualification in the nuclear industry is by definition challenging due to stringent safety requirements, limited availability of specialized facilities for materials irradiation and testing, and the challenging high-temperature, high-radiation environment. NMDQi will establish tools and capabilities that will greatly accelerate the nuclear fuels and materials development process. These tools will provide both computationally informed insights and high-throughput infrastructure with the goal of completing qualification in a single pass. In this approach, materials must be fabricated with a range of properties of interest so that materials performance can be examined in parallel, rather than with multiple discrete specimens. Advanced manufacturing (AM) techniques, which can produce complex component geometries, microstructures, and compositions, are ideal. The improved process monitoring and control that AM can provide are ideal for ensuring and reducing material variability, which will be particularly important as standards committees pursue regulations for AM components. However, gaining these benefits requires overcoming the substantial barrier of qualifying AM processes and the resulting materials and components for supporting research campaigns and eventually nuclear service.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Spatio-temporal and weather characterization of road loads of electrified heavy-duty commercial vehicles across U.S. interstate roads

Adoption of battery electric vehicles (BEV) in heavy duty (HD) commercial freight transportation is difficult due to technological and economic hurdles. Beyond safety and compliance, fleet and operational logistics necessitate both high uptime and parity with diesel system productivity/Total Cost of Ownership to support widespread deployment of electric powertrains. However, relatively high energy storage costs, along with the higher weight of BEV systems, limit the viability of HD commercial freight transport to shorter-range applications where smaller batteries will serve for mission energy requirements (single operational shift). Knowing the energy consumption and operating variations of these commercial vehicle systems is crucial for effectively sizing the energy storage systems. This paper is the first in a series of studies to understand the regional specific operating design domain variations of commercial Class 8 HD trucks and the associated impact to their energy requirements. In particular, the local weather conditions are shown to influence the total vehicle energy usage. Further, the impact of temperature, pressure, and humidity changes are shown to impact the local air density. This is needed to calculate aerodynamic drag on vehicles and has been found to play a significant role in the overall performance of a vehicle. Although the methodology of calculating air density varies only slightly throughout the literature, the application to transportation has still been limited. This study provides a means by which air density can be estimated for the contiguous U.S. using the NOAA MADIS dataset. These air density estimates were then used to determine vehicle performance in varying regions of the country to highlight the importance of consideration of weather variables when monitoring vehicle performance, but also to provide recommendations based on these locales upon fleet conversion to BEVs.

Moore, Amy↗

DOE OSTI2021

Measuring a Low Horizontal Hydraulic Gradient in a High Transmissivity Aquifer

Abstract High transmissivity aquifers typically have low hydraulic gradients (i.e., a flat water table). Measuring low gradients using water levels can be problematic because measurement error may be greater than the true difference in water levels (i.e., a low signal‐to‐noise ratio). In this study, the feasibility of measuring a hydraulic gradient in the range of 10 −6 to 10 −5 m/m was demonstrated. The study was performed at a site where the depth to water from land surface ranged from 40.1 to 94.2 m and the aquifer transmissivity was estimated at 41,300 m 2 /d (hydraulic conductivity of 18,800 m/d). The goals of the study were to reduce measurement error as much as practicable and assess the importance of factors affecting water level measurement accuracy. Well verticality was the largest source of error (0.000 to 0.168 m; median of 0.014 m), and geodetic survey of casing elevations was the next most important source of error (0.002 to 0.013 m; median of 0.005 m). Variability due to barometric pressure fluctuations was not an important factor at the site. Hydraulic heads were measured to an accuracy of ±0.0065 m, and the average hydraulic gradient was estimated to be 8.0 × 10 −6 (±0.9 × 10 −6 ) m/m. The improvement in accuracy allowed for two reversals in the groundwater flow direction to be identified, after which the gradient averaged 2.5 × 10 −5 (±0.4 × 10 −5 ) m/m. This study showed it is possible to sufficiently control sources of error to measure hydraulic gradients in the 10 −6 to 10 −5 m/m range.

McDonald, John P.↗