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DOE OSTI2025

Introducing the DREAMS Project: DaRk mattEr and Astrophysics with Machine Learning and Simulations

We introduce the DaRk mattEr and Astrophysics with Machine learning and Simulations (DREAMS) project, an innovative approach to understanding the astrophysical implications of alternative dark matter (DM) models and their effects on galaxy formation and evolution. The DREAMS project will ultimately comprise thousands of cosmological hydrodynamic simulations that simultaneously vary over DM physics, astrophysics, and cosmology in modeling a range of systems—from galaxy clusters to ultra-faint satellites. Such extensive simulation suites can provide adequate training sets for machine-learning-based analyses. This paper introduces two new cosmological hydrodynamical suites of warm dark matter (WDM), each comprising 1024 simulations generated using the arepo code. One suite consists of uniform-box simulations covering a ${(25\,{h}^{-1}\,{\rm{Mpc}})}^{3}$ volume, while the other consists of Milky Way zoom-ins with sufficient resolution to capture the properties of classical satellites. For each simulation, the WDM particle mass is varied along with the initial density field and several parameters controlling the strength of baryonic feedback within the IllustrisTNG model. We provide two examples, separately utilizing emulators and convolutional neural networks, to demonstrate how such simulation suites can be used to disentangle the effects of DM and baryonic physics on galactic properties. The DREAMS project can be extended further to include different DM models, galaxy formation physics, and astrophysical targets. In this way, it will provide an unparalleled opportunity to characterize uncertainties on predictions for small-scale observables, leading to robust predictions for testing the particle physics nature of DM on these scales.

cosmological parameters↗

DOE OSTI2024

Influences of lidar scanning parameters on wind turbine wake retrievals in complex terrain

Abstract. Scanning lidars enable the collection of spatially distributed measurements of turbine wakes and the estimation of wake properties such as magnitude, extent, and trajectory. Lidar-based characterizations, however, may be subject to distortions due to the observational system. Distortions can arise from the resolution of the measurement points across the wake, the projection of the winds onto the beam, averaging along the beam probe volume, and intervening evolution of the flow over the scan duration. Using a large-eddy simulation and simulated measurements with a virtual lidar model, we assess how scanning lidar systems may influence the properties of the retrieved wake using a case study from the Perdigão campaign. We consider three lidars performing range-height indicator sweeps in complex terrain, based on the deployments of lidars from the Danish Technical University (DTU) and German Aerospace Center (DLR) at the Perdigão site. The unwaked flow, measured by the DTU lidar, is well-captured by the lidar, even without combining data into a multi-lidar retrieval. The two DLR lidars measure a waked transect from different downwind vantage points. In the region of the wake, the observation system reacts to the smaller spatial and temporal variations of the winds, allowing more significant observation distortions to arise. While the measurements largely capture the wake structure and trajectory over its 4–5 D extent, limited spatial resolution of measurement points and volume averaging lead to a quicker loss of the two lobes in the near wake, smearing of the vertical bounds of the wake (< 30 m), wake center displacements up to 10 m, and dampening of the maximum velocity deficit by up to a third. The virtual lidar tool, coupled with simulations, provides a means for assessing measurement capabilities in advance of measurement campaigns.

17 WIND ENERGY↗

DOE OSTI2026

Assessing Photovoltaic Capacity Factor Variability Using Long-Term Satellite Derived Solar Resource Data Under Brazilian Climate

Accurate estimation of photovoltaic (PV) energy yield and its variability is essential for reducing financial risk and supporting reliable system planning for rapidly expanding PV markets. In Brazil, high solar adoption and increasing levels of distributed energy resources are beginning to introduce operational challenges such as curtailment and evolving grid requirements. Understanding how natural variability in solar resource propagates into PV system performance is therefore increasingly important for both project design and grid integration. Modern PV yield assessments commonly rely on multi-year meteorological datasets and probabilistic exceedance metrics (e.g., P50/P90) to quantify energy yield uncertainty for project financing. However, the implications of long-term solar resource variability for PV system design choices and high-adoption grid conditions remain less well characterized for rapidly expanding markets such as Brazil. In particular, understanding how weather-driven variability propagates into PV production distributions and capacity factor expectations is important for evaluating curtailment exposure, deployment strategies, and storage requirements in regions experiencing rapid growth of distributed and utility-scale PV. Seasonal and interannual variability in atmospheric conditions can produce substantial fluctuations in monthly PV energy production, which propagate into uncertainty in annual energy yield and capacity factor expectations. Characterizing this variability using long-term meteorological datasets allows probabilistic estimation of PV system performance and provides improved insight into the range of expected PV energy outcomes. This study explores the use of long-term satellite-derived meteorological data from the National Solar Radiation Database (NSRDB) to evaluate the variability of photovoltaic system performance across multiple locations in Brazil. Using a 27-year dataset (1998-2024), PV system simulations are performed to characterize the distribution of annual and seasonal capacity factors and energy yield outcomes, while propagating key sources of meteorological variability and model uncertainty through the PV modeling chain. The analysis also investigates the sensitivity of PV performance outcomes to key system design assumptions within the PV modeling chain, including tracking configuration and system sizing parameters. The resulting probabilistic performance characterization provides insight into how weather-driven variability influences PV production expectations and capacity factor distributions. These results provide a foundation for evaluating how weather-driven variability interacts with high PV adoption and potential storage or curtailment mitigation strategies.

14 SOLAR ENERGY↗

DOE OSTI2025

Design of Novel Hot Gas Component for Gas Turbine Engines Enabled by Materials and Additive Manufacturing Process Development

This CRADA project was the result of a project award under FOA-DOE-0001980. The overarching FOA project team consisted of researchers from Carpenter Technology Corporation (CTC), Solar Turbines Incorporated (Solar), Pennsylvania State University (PSU), University of California-Santa Barbara (UCSB), and Oak Ridge National Laboratory (ORNL). Evaluations were conducted on two high-γ’ superalloys that were designed by CTC and the UCSB. One alloy named GammaPrint-700 (GP-700) is a cobalt-base superalloy. The other alloy named GammaPrint-1100 (GP-1100) is a nickel-base (Ni-base) superalloy. PSU provided expertise and experimental testing of the thermal performance of AM micro-cooling architectures. ORNL provided expertise with the AM superalloy materials characterization and AM processing science. Solar provided turbine component design expertise. The focus of this CRADA report is to document the efforts between ORNL and CTC towards the development of superalloys designed for AM. The project goal was to use an AM processable high-temperature superalloy and design for Additive Manufacturing (DfAM) techniques to design an efficient turbine component (i.e. a turbine tip shoe) with enhanced cooling features that can only be fabricated through additive manufacturing (AM). The efficiencies of existing combined heat and power (CHP) engines are capped by both component design and materials limitations. However, AM of a tip shoe component from a γ’strengthened superalloy offers the design flexibility to increase the efficiency and power of an industrial gas turbine. This project brought about advancements in the DfAM tip shoe design space and in the area of high temperature superalloys processable through laser powder bed fusion (LPBF) AM. State of art computation design tools were utilized to optimize unique cooling features into a tip shoe component design. A two-prong materials development approach was taken to support development of the AM tip shoe geometry. The first approach centered on investigating the processability and the appropriate process science for the industry standard high-γ’ nickel-base (Ni-base) superalloy Mar-M247. This superalloy is typically cast and considered non-weldable by traditional welding standards. In the course of this work, the alloy was not deemed feasible for process scale-up due to significant cracking issues during printing. The second approach focused on the development and evaluation of a novel cobalt-base superalloy, GammaPrint™-700 (GP-700 and a Ni-base superalloy, GammaPrint™-1100 (GP-1100) designed to mitigate the significant AM processing issues with Mar-M247. The processability of these two alloys were investigated through electron beam melting (EBM) binder-jet AM (BJAM), and LPBF as a risk mitigation for manufacturability. To be considered a candidate material for down-selection to proceed to full-scale AM tip shoe engine testing trials, the high temperature creep rupture strength was required to achieve at a minimum, a Larsen Miller Parameter (LMP) increase of 10.9% over the baseline material LPBF AM Hastelloy X.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2025

Design of Novel Hot Gas Component for Gas Turbine Engines Enabled by Materials and Additive Manufacturing Process Development

This CRADA project was the result of a project award under FOA-DOE-0001980. The overarching FOA project team consisted of researchers from Carpenter Technology Corporation (CTC), Solar Turbines Incorporated (Solar), Pennsylvania State University (PSU), University of California-Santa Barbara (UCSB), and Oak Ridge National Laboratory (ORNL). Evaluations were conducted on two high-γ’ superalloys that were designed by CTC and the UCSB. One alloy named GammaPrint-700 (GP-700) is a cobalt-base superalloy. The other alloy named GammaPrint-1100 (GP-1100) is a nickel-base (Ni-base) superalloy. PSU provided expertise and experimental testing of the thermal performance of AM micro-cooling architectures. ORNL provided expertise with the AM superalloy materials characterization and AM processing science. Solar provided turbine component design expertise. The focus of this CRADA report is to document the efforts between ORNL and CTC towards the development of superalloys designed for AM. The project goal was to use an AM processable high-temperature superalloy and design for Additive Manufacturing (DfAM) techniques to design an efficient turbine component (i.e. a turbine tip shoe) with enhanced cooling features that can only be fabricated through additive manufacturing (AM). The efficiencies of existing combined heat and power (CHP) engines are capped by both component design and materials limitations. However, AM of a tip shoe component from a γ’strengthened superalloy offers the design flexibility to increase the efficiency and power of an industrial gas turbine. This project brought about advancements in the DfAM tip shoe design space and in the area of high temperature superalloys processable through laser powder bed fusion (LPBF) AM. State of art computation design tools were utilized to optimize unique cooling features into a tip shoe component design. A two-prong materials development approach was taken to support development of the AM tip shoe geometry. The first approach centered on investigating the processability and the appropriate process science for the industry standard high-γ’ nickel-base (Ni-base) superalloy Mar-M247. This superalloy is typically cast and considered non-weldable by traditional welding standards. In the course of this work, the alloy was not deemed feasible for process scale-up due to significant cracking issues during printing. The second approach focused on the development and evaluation of a novel cobalt-base superalloy, GammaPrint™-700 (GP-700 and a Ni-base superalloy, GammaPrint™-1100 (GP-1100) designed to mitigate the significant AM processing issues with Mar-M247. The processability of these two alloys were investigated through electron beam melting (EBM) binder-jet AM (BJAM), and LPBF as a risk mitigation for manufacturability. To be considered a candidate material for down-selection to proceed to fullscale AM tip shoe engine testing trials, the high temperature creep rupture strength was required to achieve at a minimum, a Larsen Miller Parameter (LMP) increase of 10.9% over the baseline material LPBF AM Hastelloy X.

36 MATERIALS SCIENCE↗

DOE OSTI2025

AERO-MAP: a data compilation and modeling approach to understand spatial variability in fine- and coarse-mode aerosol composition

Abstract. Aerosol particles are an important part of the Earth climate system, and their concentrations are spatially and temporally heterogeneous, as well as being variable in size and composition. Particles can interact with incoming solar radiation and outgoing longwave radiation, change cloud properties, affect photochemistry, impact surface air quality, change the albedo of snow and ice, and modulate carbon dioxide uptake by the land and ocean. High particulate matter concentrations at the surface represent an important public health hazard. There are substantial data sets describing aerosol particles in the literature or in public health databases, but they have not been compiled for easy use by the climate and air quality modeling community. Here, we present a new compilation of PM2.5 and PM10 surface observations, including measurements of aerosol composition, focusing on the spatial variability across different observational stations. Climate modelers are constantly looking for multiple independent lines of evidence to verify their models, and in situ surface concentration measurements, taken at the level of human settlement, present a valuable source of information about aerosols and their human impacts complementarily to the column averages or integrals often retrieved from satellites. We demonstrate a method for comparing the data sets to outputs from global climate models that are the basis for projections of future climate and large-scale aerosol transport patterns that influence local air quality. Annual trends and seasonal cycles are discussed briefly and are included in the compilation. Overall, most of the planet or even the land fraction does not have sufficient observations of surface concentrations – and, especially, particle composition – to characterize and understand the current distribution of particles. Climate models without ammonium nitrate aerosols omit ∼ 10 % of the globally averaged surface concentration of aerosol particles in both PM2.5 and PM10 size fractions, with up to 50 % of the surface concentrations not being included in some regions. In these regions, climate model aerosol forcing projections are likely to be incorrect as they do not include important trends in short-lived climate forcers.

Mahowald, Natalie M. (ORCID:000000022873997X)↗

DOE OSTI2024

Mapping and Characterizing the Visual Impacts of the Existing US Wind Turbine Fleet

Visual impacts of wind turbines have been a persistent concern for wind energy development in the United States (US) for decades and remain a major source of project delays and cancellations. Assessments of visual impacts are frequently performed at a local scale for individual projects, but a comprehensive understanding of broader geographic patterns in visual impacts across the US is lacking. This paper presents a visual impact assessment of the existing land-based wind turbine fleet of the contiguous United States (CONUS). The assessment combines geographic information systems and 3D simulation methods to account for key factors driving the visual magnitude of impacts from the installed turbines. The results indicate that, despite the deployment of approximately 70,000 turbines and over 144 gigawatts of land-based wind in the CONUS, the visual impacts are very small when measured as a proportion of land area, population, and sensitive visual resources. Nonetheless, visual impacts are not evenly distributed: people experience a concentrated share in a small number of natural settings, primarily including plains, prairies, and deserts. Finally, we find that although increased density of wind development consistently leads to visual impacts across a greater proportion of land, it does not always lead to impacts to a greater share of the population. These findings suggest that visual impacts from wind energy are generally well-mitigated across the CONUS to date but also highlight the need for a deeper understanding of landscape sensitivity and individual perceptions of wind turbines in the most heavily impacted natural settings.

3D simulation↗

DOE OSTI2024

Precision Agriculture using Networks of Degradable Analytical Sensors (PANDAS) (Final Technical Report)

Precision agriculture, where sensing of soil, environment and crop conditions are used to precisely synchronize inputs (such as water and fertilizer) to crop needs enhances input use efficiency. This can improve yields and farm profitability while mitigating environmental losses, improving soil carbon content and substantially decreasing energy use for food, feed and fuel crops. Unfortunately, farmers are not yet able to harness the full potential of these management technologies as there is a lack of available management information, and there is therefore a need for sensors that are able to economically measure spatio-temporal variability in soil and crop properties of extremely heterogeneous farm fields precisely at high resolution and at low cost. Real-time, in-situ monitoring of agricultural soil conditions is today carried out using devices that limit the total number of nodes that can be used economically to typically one per acre or less. Higher spatio-temporal resolution sensing would enable more precise agricultural input optimization, with significant benefits to the farmer and the environment. In order to address this issue, this project focused on developing additively manufactured, biodegradable, soil sensors with predicted costs of < $\$$1 per unit to monitor crop inputs (such as water and fertilizer) that predictably, harmlessly degrade away into the soil when no longer needed. These sensor nodes should be easy to place, accurately and continuously monitor soil and crop conditions for an entire season, be read remotely using existing farm equipment, require no ongoing maintenance, not impede farm operations and produce no persistent waste. This approach could enable a >100× increase in information density over current solutions for precision farming of row and other crops, and lead to significant reductions in input energy use and provide increased yield for biofuel crops. Over the course of this project the team at the University of Colorado Boulder, University of California Berkeley, and Colorado State University/Kansas State University investigated a wide range of printable biodegradable electronic materials and sensor designs for determining soil moisture and soil nitrate concentration. These efforts expanded the available materials set for printed soil degradable electronic materials, particularly for conductors, enabling high conductivity and stability. Printed soil moisture and nitrate sensors with suitable sensitivity and selectivity were developed and characterized. Low power and passive wireless electronic systems were integrated with the soil sensors, and testing was carried out with completed sensors to understand their functionality under agricultural conditions. Additionally, other sensor types enabled by the biodegradable materials set created during this project, such as soil microbial activity sensors, were also developed and demonstrated. Project outputs include 10 peer reviewed publications, 4 patent applications, 21 technical presentations, 3 PhD thesis, 10 media reports, 8 additional grants worth over $\$$6M, and the formation of 3 start-up companies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Summertime Carbonaceous Aerosol in Interior Versus Coastal Northern Alaska

Abstract Rapid warming is likely increasing primary production and wildfire occurrence in the Arctic. Projected changes in carbonaceous aerosols during the summer will impact atmospheric chemistry and climate, but our understanding of these processes is limited by sparse observations. Here, we characterize carbonaceous aerosol in Alaska, USA: Toolik Field Station in the Interior and the Atmospheric Radiation Measurement facility at Utqiaġvik on the Arctic coast, during the summers of 2022 and 2023. We estimated PM 2.5 and PM 10 concentrations using laser light scattering (PurpleAir sensors) and examined total carbon (TC) and its organic carbon (OC) and elemental carbon (EC) fractions in total suspended particles (TSP). We investigated the dominant sources of carbonaceous aerosol using air mass backward‐trajectories from the NOAA HYSPLIT model and radiocarbon source apportionment of TC. TC concentrations were about twice as high in the Interior compared to the coast, with contemporary sources dominating at both Toolik (95%–99%) and Utqiaġvik (86%–89%) over minor contributions from fossil sources. Elevated PM, TC, OC, and EC concentrations coincided with major boreal forest fire activity in North America that brought smoke to the region. The radiocarbon signature of EC measured at Toolik during these wildfire events indicated that over 90% of the EC came from contemporary sources. Our measurements demonstrate the potential for Arctic aerosol concentrations to respond significantly to climate warming‐induced changes to the landscape and emphasize the need for continuous atmospheric monitoring to advance our understanding of this rapidly changing environment.

Welch, Allison M. [Department of Earth System Scie↗

DOE OSTI2024

Radiation Modeling of Z1: Validation of a Novel Radioisotope System

This paper presents the development of a radiation model for a novel strontium-90 (Sr-90) radioisotope heat source developed by Zeno Power Systems (Zeno), which demonstrates a groundbreaking fuel and shielding design that significantly reduces weight compared to traditional concepts. A Monte Carlo N-Particle (MCNP) model has been created to assess the effectiveness of this fuel and shielding design, however validation of the MCNP model is desired. Zeno has developed a prototype device (Z1) to aid in this model validation effort. This project is a collaborative effort between Zeno, the University of Dayton Research Institute (UDRI) and Pacific Northwest National Laboratory (PNNL), where the Z1 prototype was constructed and characterized.

RTG↗

DOE OSTI2024

New Matrix Framework to Determine Carbon Storage Technical Viability

Carbon storage is an integral component of reducing CO2 emissions. Research over the past two decades has developed workflows for volume assessments and economic project feasibility, providing necessary and useful tools to progress geologic carbon storage (GCS) projects. These workflows and assessments have focused primarily on determining the in-situ storage resource based on geologic and engineering parameters and do not integrate subsurface characterization with surface conditions, social factors, and environmental factors that may pose a benefit or impediment to the implementation of GCS. Furthermore, the data required to assess the technical viability of GCS are myriad and disparate. There is no current methodology that identifies technical viability criteria and systematically informs how to aggregate these factors for spatial assessments. To address this gap, the National Energy Technology Laboratory has developed a Carbon Storage Technical Viability Approach (CS TVA) Matrix that incorporates a wide variety of factors to inform and accelerate screening for GCS site selection in the United States.

Mulhern, Julia↗

DOE OSTI2026

Southern Company Services: Project 2: Tall Tower Wind Technical Potential for the Southeast (Modifications 4, 5), Project 4: Offshore Wind Potential for Southern Company Services (Modifications 7, 8)

Project 2: The National Laboratory of the Rockies (NLR) and Southern Company Services (SCS) seek to establish an agreement to assess the technical potential for wind energy in the Southeastern U.S. SCS has requested that NLR conduct a detailed assessment of system performance, available capacity, transmission distance, and levelized cost of electricity (LCOE) at turbine hub heights of 100m to 160m within the SCS service territory. NLR will also provide an assessment of utility photovoltaic (PV) technical potential within the SCS service territory. NLR has unique modeling capabilities to assist SCS with high spatial and temporal resolution assessment of wind and PV technical potential, considering geospatial constraints. Project 4: NLR and SCS seek to develop a spatially explicit techno-economic assessment for offshore wind energy in the Southeastern U.S. The analysis aims to characterize the regional sensitivities of offshore wind plant costs and performance, underwater cabling, potential siting constraints, and landfall spur transmission costs.

17 WIND ENERGY↗

DOE OSTI2026

Additively Manufactured Graded Composite Transition Joints for Dissimilar Metal Weldments in Advanced Ultra-Supercritical Power Plant (Final Technical Report)

In this project, we successfully achieved all the goals we proposed: ① Code Development; ② Transition Joint Design; ③ Lab-scale GCTJ Testing Piece Development; ④ Heat Treatment; ⑤ Welding of DMW; ⑥ Mechanical Characterization; ⑦ Corrosion; ⑧ Fabrication of Prototype GCTJ; ⑨In-field Test in SWRI and ⑩Tech-economic Analysis.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Utilizing digitized occurrence records of Midwestern feral Cannabis sativa to develop ecological niche models

Hemp (Cannabis sativa L.) has historically played a vital role in agriculture across the globe. Feral and wild populations have served as genetic resources for breeding, conservation, and adaptation to changing environmental conditions. However, feral populations of Cannabis, specifically in the Midwestern United States, remain poorly understood. This study aims to characterize the abiotic tolerances of these populations, estimate suitable areas, identify regions at risk of abiotic suitability change, and highlight the utility of ecological niche models (ENMs) in germplasm conservation. The Maxent algorithm was used to construct a series of ENMs. Validation metrics and MOP (Mobility-oriented Parity) analysis were used to assess extrapolation risk and model performance. We also projected the final projected under current and future climate scenarios (2021–2040 and 2061–2080) to assess how abiotic suitability changes with time. Climate change scenarios indicated an expansion of suitable habitat, with priority areas for germplasm collection in Indiana, Illinois, Kansas, Missouri, and Nebraska. This study demonstrates the application of ENMs for characterizing feral Cannabis populations and highlights their value in germplasm conservation and breeding efforts. Populations of feral C. sativa in the Midwest are of high interest, and future research should focus on utilizing tools to aid the collection of materials for the characterization of genetic diversity and adaptation to a changing climate.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Charged Current Single Pion Production on SBND

The Short Baseline Neutrino (SBN) program at Fermilab is designed to provide precise measurements of neutrino oscillations using 3 Liquid Argon Time Projection Chambers (LArTPC) built along Fermilab's Booster Neutrino Beam (BNB). The Short Baseline Near Detector (SBND), located at only 110 m from the BNB target, will precisely characterize the neutrino flux before oscillations take place, thanks to its unprecedented neutrino interaction statistics. Due to its proximity to the neutrino production target, the detector expects over a million neutrino interactions annually, which will open the possibility of exploring exclusive channels of neutrino interactions. This poster will show SBND’s capabilities of exploring Muon Neutrino Charged Current Single Pion production, for which previous measurements in Argon were limited by statistics. The poster will introduce the relevance of this process for neutrino Physics, the unique possibilities of SBND and a preliminary event selection using SBND's current reconstruction tools.

43 PARTICLE ACCELERATORS↗

DOE OSTI2026

Results from the DUNE ND-LAr 2x2 Demonstrator Run 2

The Deep Underground Neutrino Experiment (DUNE) is a cutting-edge, long-baseline experiment under construction in the United States, based on large liquid-argon time projection chambers (LArTPCs). The DUNE Near Detector LArTPC (ND-LAr) will employ a novel modular architecture using a pixelated LArPix charge readout. To validate this design and characterize detector response, the 2×2 demonstrator—an array of eight optically isolated LArTPC modules—was deployed at Fermilab in 2024. During winter 2025, the 2x2 demonstrator was operated for a second data-taking campaign (Run 2) with the aim of studying the low-energy response of the detector. Run 2 focused on calibration and response studies using a suite of deployed radioactive sources. Gamma sources (²²Na, ⁶⁰Co, and ⁸⁸Y) were used to probe module-to-module performance variations, energy resolution, and calibrations. Neutron sources (AmBe and a pulsed neutron generator) enabled studies of neutron interactions in liquid argon, including inelastic scatters and neutron capture signals, relevant for low-energy backgrounds and detector modeling. In addition, the detector was doped with ²²⁰Rn, providing Bi–Po coincidence signals that allow precise calibration of the charge and scintillation response. This poster will present results from these calibration campaigns. These studies provide critical validation of the ND-LAr modular LArTPC concept and inform calibration and reconstruction strategies for the full DUNE near detector.

Mora-Lepin, Luis [Florida State U.] (ORCID:0000000↗

DOE OSTI2026

Characterizing the Mechanical Response of the Saturn Accelerator

The Saturn Particle Accelerator is a hot X-ray source used by the NNSA for creating conditions similar to that of a nuclear weapon. When Saturn fires some of it's energy is released in the form of mechanical shock and vibration. This mechanical output has not been characterized or understood, making design of components and diagnostics more difficult. Thus it will be helpful to understand this mechanical shock. This poster presents the beginnings of a project to do just that.

Cuneo, Nicolas Francis [Sandia National Laboratori↗

DOE OSTI2025

Organic Matter Composition in June 2023 and September 2023 Across the McKenzie Sub-Basin Impacted by the 2020 Holiday Farm Fire

This dataset represents results from a field study aiming to understand the variability in post-fire responses of dissolved organic matter and determine drivers of post-fire responses. Samples were collected at 58 sites within the McKenzie River Watershed (Oregon, USA) that were upstream, within, and downstream of the Holiday Farm Fire burn perimeter. The samples were collected in June 2023 and September 2023 during storm events, approximately 3 years post-fire. Samples were characterized for benezenepolycarboxylic acids (BPCA) and ultra-high resolution mass spectrometry. Dissolved organic carbon and optics (absorbance and fluorescence) data can be found in a separate data packages (https://ir.library.oregonstate.edu/concern/datasets/zc77sz60m, https://ir.library.oregonstate.edu/concern/datasets/mc87q034m). Related data from a subset of sites from 2020-2022 can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1869708 and https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2478546. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. This dataset contains (1) file-level metadata; (2) data dictionary; (3) data package readme; (4) metadata; (5) methods information; (6) benzene polycarboxylic acid (BPCA) concentration data; (7) Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) methods; (8) folder of high resolution characterization of organic matter via 12 Tesla FTICR-MS data generated through the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory). This package contains the following file types: csv, xml, pdf.

54 ENVIRONMENTAL SCIENCES↗