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DOE OSTI2024

Implementation of the Glued Sphere Discrete Element Model for Non-Spherical Particles in MFiX Software

To enhance solver capabilities, simulation flexibility and model validation within the MFiX software, the U.S. Department of Energy (DOE) is funding efforts to develop and integrate the glued-sphere discrete element method into the latest version of MFiX as a dedicated computational module. The glued-sphere discrete element method is a numerical technique to depict the behavior of non-spherical particles in granular flows or particulate systems by representing them as a collection of component spheres. These spheres are bonded together to approximate the shape and mechanical/chemical properties of a more complex particle. The method effectively reuses the existing sphere-sphere collision algorithm, interphase momentum and heat transfer calculations utilized in the traditional discrete element method, extending these capabilities to non-spherical particles. Additionally, this method explicitly resolves intra-particle temperature and species distributions. The MFiX glued-sphere computational module includes tools for generating glued sphere configurations, a dedicated solver, and visualization capabilities in post-processing. More specifically within the computational module, collision detection and calculations were first performed on component spheres and then mapped onto non-spherical particles. The linear spring-dashpot model was utilized to simulate the sphere-sphere interactions.

Ke, Renjie↗

DOE OSTI2026

Towards Anomaly Detection at the CMS High-Level Trigger System

Traditional trigger strategies in CMS typically rely on model-dependent selections or rigid kinematic cuts, risking the omission of unexpected exotic signatures. To address this, we propose a novel anomaly detection (AD) algorithm for the High-Level Trigger (HLT), designed to serve as a complementary second layer of filtering to the Level-1 AXOL1TL AD algorithm. We employ a transformer-based foundation model trained on a diverse ensemble of Standard Model processes. By combining a joint contrastive and classification objective, and using particle kinematics as inputs, the model learns to map events to a physics-informed latent space where anomalous events are isolated from dominant backgrounds. Preliminary results show that this strategy enhances the signal-to-background ratio across a range of rare SM and BSM scenarios. Furthermore, this work constitutes foundational R&D for the potential implementation of an analogous AD algorithm in the Level-1 trigger system for Phase-2.

Cruz, Roy [U. Wisconsin, Madison (main)] (ORCID:00↗

DOE OSTI2019

Molecular Characterization of High Molecular Weight Polyesters by Matrix-Assisted Laser Desorption/Ionization High-Resolution Time-of-Flight Mass Spectrometry Combined with On-plate Alkaline Degradation and Mass Defect Analysis

Matrix-assisted laser desorption ionization high-resolution time-of-flight mass spectrometry (MALDI HR TOF MS) is a powerful tool for the molecular characterization of industrial polymers. However, accurate mass determination and resolution of isobaric ions are possible for oligomer samples only typically below m/z 3000. To cut long polymer chains into oligomers suitable for high-resolution mass spectrometry, we propose a simple “on-plate” alkaline degradation of polyesters as a sample pretreatment technique prior to the MALDI TOF MS measurement. This pretreatment can be performed on a MALDI target using a small amount of sample (μg or less) and 1 μL of alkaline reagent by simple pipetting. Informative mass spectra in the oligomeric mass range are successfully recorded but complicated by the variation of end-groups and the copolymeric composition of the degradation products. Data processing is assisted by a series of advanced Kendrick mass defect (KMD) analyses recently proposed by the authors to plot visually understandable two-dimensional maps. On-plate degradation pretreatment, high-resolution MALDI TOF MS measurements, and advanced KMD analyses are innovatively combined for the compositional characterization of bacterial poly(3-hydroxybutyric acid-co-3-hydroxyvaleric acid) and industrial poly(ethylene terephthalate) samples. .

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2017

Facilitating the Use of Epidemiological Models for Infectious Disease Surveillance

1. To develop a comprehensive model characterization framework to describe epidemiological models in an operational context. 2. To apply the framework to characterize “operational” models for specific infectious diseases and provide a web-based directory, the biosurveillance analytics resource directory (BARD) to the global infectious disease surveillance community. Epidemiological modeling for infectious disease is useful for disease management and routine implementation needs to be facilitated through better description of models in an operational context. A standardized model characterization process that allows selection or making manual comparisons of available models and their results is currently lacking. Los Alamos National Laboratory(LANL) has developed a comprehensive framework that can be used to characterize an infectious disease model in an operational context. We offer this framework and an associated database to stakeholders of the infectious disease modeling field as a tool for standardizing model description and facilitating the use of epidemiological models. Such a framework could help the understanding of diverse models by various stakeholders with different preconceptions, backgrounds, expertise ,and needs, and can foster greater use of epidemiological models as tools in infectious disease surveillance. We define, “operational” as the application of an epidemiological model to a real-world event for decision support and can be used by experts and non-experts alike. The term “model” covers three major types, risk mapping, disease dynamics and anomaly detection. To develop a framework for characterizing epidemiological models we collected information via a three-step process: a literature search of model characteristics, a review of current operational infectious disease epidemiological models, and subject matter expert (SME)panel consultation. We limited selection of operational models to five infectious diseases: influenza, malaria, dengue, cholera and foot-and-mouth disease (FMD). These diseases capture a variety of transmission modes, represent high or potentially high epidemic or endemic burden, and are well represented in the literature. We also developed working criteria for what attributes can be used to comprehensively describe an operational model including a model’s documentation, accessibility, and sustainability. To apply the model characterization framework, we built the BARD, which is publicly available (http://brd.bsvgateway.org).A document was also developed to describe the usability requirements for the BARD; potential users (and non-users) and use cases are formally described to explain the scope of use. 1. Framework for model characterization. The framework is divided into six major components (Figure 1):Model Purpose, Model Objective, Model Scope, Biosurveillance(BSV) goals, Conceptual Model and Model Utility; each of which has several sub-categories for characterizing each aspect of a model.2. Application to model characterization Models for five infectious diseases—cholera, malaria, influenza, FMD and dengue were characterized using the framework and are included in the BARD database. Our framework characterized disparate models in a streamlined fashion. Model information could be binned into the same categories, allowing easy manual comparison and understanding of the models.3. Development of the BARD. Our model characterization framework was implemented into an actionable tool which provides specific information about a model that has been systematically categorized. It allows manual category-to-category comparison of multiple models for a single disease and while the tool does not rank models it provides model information in a format that allows a user to make a ranking or an assessment of the utility of the model. With the model characterization framework we hope to encourage model developers to start describing the many features of their models using a common format. We illustrate the application of the framework through the development of the BARD which is a scientific and non-biased tool for selecting an appropriate epidemiological model for infectious disease surveillance. Epidemiological models are not necessarily being developed with decision makers in mind. This gap between model developers and decision makers needs to be narrowed before modeling becomes routinely implemented in decision making. The characterization framework and the tool developed (BARD) area first step towards addressing this gap.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Multiscale modeling and characterization for performance and safety of lithium-ion batteries

Lithium-ion batteries are highly complex electrochemical systems whose performance and safety are governed by coupled nonlinear electrochemical-electrical-thermal-mechanical processes over a range of spatiotemporal scales. Gaining an understanding of the role of these processes as well as development of predictive capabilities for design of better performing batteries requires synergy between theory, modeling, and simulation, and fundamental experimental work to support the models. This paper presents the overview of the work performed by the authors aligned with both experimental and computational efforts. In this paper, we describe a new, open source computational environment for battery simulations with an initial focus on lithium-ion systems but designed to support a variety of model types and formulations. This system has been used to create a three-dimensional cell and battery pack models that explicitly simulate all the battery components (current collectors, electrodes, and separator). The models are used to predict battery performance under normal operations and to study thermal and mechanical safety aspects under adverse conditions. This paper also provides an overview of the experimental techniques to obtain crucial validation data to benchmark the simulations at various scales for performance as well as abuse. We detail some initial validation using characterization experiments such as infrared and neutron imaging and micro-Raman mapping. In addition, we identify opportunities for future integration of theory, modeling, and experiments.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2017

Testing of the SIDIS framework at Jefferson Lab Hall C using precise measurements of light meson electroproduction

Semi-inclusive deep inelastic scattering (SIDIS) has proved to be an important tool for unveiling the inner structure of the core of the atom, the nucleons and their basic constituents. Azimuthal spin asymmetries in polarized SIDIS are directly related to transverse momentum dependent parton distributions (TMDs) and fragmentation functions. The TMDs allow one to probe the 3D dynamical structure of partons inside the nucleon. Understanding the nature of the SIDIS process is essential for TMD studies, in particular at modest energies where deviations from the leading-order factorized picture may be a significant. Precise maps of the meson cross sections and their ratios at low transverse momentum and Jefferson Lab beam energies provide a stringent test of the use of the standard SIDIS framework in terms of factorized parton distributions convoluted with fragmentation functions. The magnetic spectrometers in Hall C at Jefferson Lab are well suited for such precision measurements of fully L/T separated cross sections and their ratios. The addition of neutral particle detection enables additional opportunities. Plans for upcoming experiments that will investigate the SIDIS process at the 12 GeV Jefferson Lab will be presented.

Kinney, Edward↗

DOE OSTI2018

Investigating ceramic breeder pebble beds thermomechanics evolution using novel pressure mapping technology

Ceramic breeder pebble beds undergo complex thermomechanical interactions during blanket operation due to stress build-up and relaxation under the effects of confined thermal expansion, thermal cycling, and creep. Understanding the evolution of such processes can aid in guiding blanket design/assembly, breeder materials developments, predicting performance and possible failure modes. This study introduces experimental techniques that enable us to create an environment in which the pebble bed stresses are organically generated as a result of relevant temperature gradients and magnitudes that allows us to observe the combined thermomechanical interaction effects. A novel non-intrusive in-situ tactile pressure sensing technology is used to generate real-time contact pressure maps revealing the spatial and temporal stress evolution with emphasis on understanding the roles that each of the thermo-mechanical forces play in dictating the pebble bed’s equilibrium conditions. Two types of bed-wall contact pressure drop were recorded: (1) within the subsequent cycles due to pebbles irreversible rearrangements, and (2) within the cycle itself as a result of creep/stress-relaxation. Consequently, by the fourth cycle, the contact pressure drops to negligible values and remains stable. This stability is attributed to two modes of self-regulation: (1) stress self-regulation as a result of pressure rise and fall due to thermal expansion and creep/thermal cycling, respectively, and (2) temperature self-regulation due to the locally enhanced thermal conductivity in the core region of the bed accompanied by deteriorated interface conductance. These two mechanisms are desirable as they lower the probability of the events of pebbles crushing and thermal runaways under high temperatures/stresses and poor heat extraction.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2019

Optimizing Long-term Monitoring of Radiation Air Dose Rates - 19169

Radiation air dose rates near the Fukushima Daiichi Nuclear Power Plant (NPP) have been steadily decreasing over the past seven years since the accident in March 2011. After seven years, the radiation monitoring program is expected to transition from short-term emergency responses to long-term monitoring. The objectives of long-term monitoring are often different from early emergency responses, since extensive data accumulation has led to an understanding of contaminant distributions and mobility. The main long-term monitoring objectives are (1) to confirm system stability and the continuing reduction of contaminant and hazard levels, (2) to provide assurance for the public, (3) to accumulate the basic datasets for scientific knowledge and future preparation, and (4) to detect changes or anomalies in contaminant mobility (if they occur), or any unexpected processes or events. In this work, we have developed a methodology for optimizing the sampling locations of radiation air dose-rate monitoring. Three steps are taken in order to determine sampling locations in a systematic manner: (1) prioritizing the critical locations, such as schools or regulatory requirement locations, (2) diversifying locations across the key environmental controls that are known to influence contaminant mobility and distributions, and (3) capturing the heterogeneity of radiation air dose rates across the domain. In the second step, we use a Gaussian mixture model to identify the representative locations in multiple environmental variables, such as elevation and land-cover types. In the third step, we use a Gaussian process model to capture and estimate the heterogeneity of air dose rates across the domain. We use the integrated dose-rate map from the Bayesian geostatistical method as the reference map and distribute the sampling in such a way as to capture the heterogeneity of the reference map. Although we assume hypothetical sets of measurement locations to evaluate the performance of the methodology and algorithms, we have demonstrated this approach using real, spatially extensive datasets, including the integrated air-dose-rate map of 2016. Our results have shown that this approach allows us to add sampling locations in a systematic manner. Increasing the number of sampling locations can better capture the heterogeneity of dose rates, although the estimation error does not decrease further after a certain number of samples. We have also found that when there are restrictions such as pre-existing monitoring locations or the ones along roads, the spatial estimation becomes poor even with the same number of monitoring locations. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2014

Gulf of Mexico miocene CO₂ site characterization mega transect

This project characterized the Miocene-age sub-seafloor stratigraphy in the near-offshore portion of the Gulf of Mexico adjacent to the Texas coast. The large number of industrial sources of carbon dioxide (CO₂) in coastal counties and the high density of onshore urbanization and environmentally sensitive areas make this offshore region extremely attractive for long-term storage of carbon dioxide emissions from industrial sources (CCS). The study leverages dense existing geologic data from decades of hydrocarbon exploration in and around the study area to characterize the regional geology for suitability and storage capacity. Primary products of the study include: regional static storage capacity estimates, sequestration “leads” and prospects with associated dynamic capacity estimates, experimental studies of CO₂-brine-rock interaction, best practices for site characterization, a large-format ‘Atlas’ of sequestration for the study area, and characterization of potential fluid migration pathways for reducing storage risks utilizing novel high-resolution 3D (HR3D) seismic surveys. In addition, three subcontracted studies address source-to-sink matching optimization, offshore well bore management and environmental aspects. The various geologic data and interpretations are integrated and summarized in a series of cross-sections and maps, which represent a primary resource for any near-term commercial deployment of CCS in the area. The regional study characterized and mapped important geologic features (e.g., Clemente-Tomas fault zone, the regionally extensive Marginulina A and Amphistegina B confining systems, etc.) that provided an important context for regional static capacity estimates and specific sequestration prospects of the study. A static capacity estimate of the majority of the Study area (14,467 mi 2 ) was estimated at 86 metric Gigatonnes. While local capacity estimates are likely to be lower due to reservoir-scale characteristics, the offshore Miocene interval is a storage resource of National interest for providing CO₂ storage as an atmospheric emissions abatement strategy. The natural petroleum system was used as an analog to infer seal quality and predict possible migration pathways of fluids in an engineered system of anthropogenic CO₂ injection and storage. The regional structural features (e.g., Clemente-Tomas fault zone) that exert primary control on the trapping and distribution of Miocene hydrocarbons are expected to perform similarly for CCS. Industrial-scale CCS will require storage capacity utilizing well-documented Miocene hydrocarbon (dominantly depleted gas) fields and their larger structural closures, as well as barren (unproductive, brine-filled) closures. No assessment was made of potential for CO₂ utilization for enhanced oil and gas recovery. The use of 3D numerical fluid flow simulations have been used in the study to greatly assist in characterizing the potential storage capacity of a specific reservoir. Due to the complexity of geologic systems (stratigraphic heterogeneity) and inherent limitations on producing a 3D geologic model, these simulations are typically simplified scenarios that explore the influence of model property variability (sensitivity study). A specific site offshore San Luis Pass (southern Galveston Island) was undertaken successfully, indicating stacked storage potential. Downscaling regional capacity estimates to the local scale (and the inverse) has proven challenging, and remains an outstanding gap in capacity assessments. In order to characterize regional seal performance and identify potential brine and CO₂ leakage pathways, results from three high-resolution 3D (HR3D) seismic datasets acquired by the study using novel HR3D (P-Cable) acquisition system showed steady and significant improvements in data quality because of improved acquisition and processing technique. Finely detailed faults and stratigraphy in the shallowest 1000 milliseconds (~800 m) of data allowed for the identification and mapping of unconformable surfaces including what is probably a surface associated with the last Pleistocene glacial lowstand. The identification of a previously unrecognized (in commercial seismic data) gas chimney that was clearly defined in the 2013 HR3D survey, indicates that HR3D surveys may be useful as both a characterization tool for the overburden of a potential carbon sequestration site and as an additional monitoring tool for future engineered injection sites. Geochemical modeling indicated that injection of CO₂ would result in minor dissolution of calcite, K-feldspar and albite. In addition, modeling of typical brines in Miocene age rocks indicate that approximately 5% of injection capacity would result from CO₂ dissolution into the brine. After extensive searches, no rock samples of the Marginulina A and Amphistegina B seals (“caprocks”) were obtained, but analyses of available core samples of other Miocene age mudrocks (seals or caprocks) indicate that they have sealing ability sufficient for potential CO 2 storage in underlying sandstone units.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2021

Parallel projection—An improved return mapping algorithm for finite element modeling of shape memory alloys

Here, we present a novel finite element analysis of inelastic structures containing Shape Memory Alloys (SMAs). Phenomenological constitutive models for SMAs lead to material nonlinearities, that require substantial computational effort to resolve. Finite element analysis methods, which rely on Gauss quadrature integration schemes, must solve two sets of coupled differential equations: one at the global level and the other at the local, i.e. Gauss point level. In contrast to the conventional return mapping algorithm, which solves these two sets of coupled differential equations separately using a nested Newton procedure, we propose a scheme to solve the local and global differential equations simultaneously. In the process we also derive closed-form expressions used to update the internal/constitutive state variables, and unify the popular closest-point and cutting plane methods with our formulas. Numerical testing indicates that our method allows for larger thermomechanical loading steps and provides increased computational efficiency, over the standard return mapping algorithm.

42 ENGINEERING↗

DOE OSTI2025

Mapping structures and dynamics with frequency-correlated diffusion exchange

Understanding molecular motion in diffusion-driven complex environments is critical for designing sustainable materials and improving chemical processes. Here, we introduce a multidimensional nuclear magnetic resonance (NMR) method that captures how molecular populations exchange across different dynamic regimes. By extending the modulated gradient spin-echo technique to include frequency-frequency correlations, our approach reveals diffusion pathways that are otherwise obscured in heterogeneous systems. Implemented on a unilateral NMR magnet, the method eliminates gradient pulsing constraints and accesses dynamics in the kilohertz regime. We apply this technique to swelling and acid-catalyzed deconstruction of cross-linked and linear polymers to observe how structural heterogeneity evolves over time. By linking molecular motion to topology and chemical state, we extract physical metrics such as fractal surface dimensionality and reaction wavefront velocity, properties inaccessible with standard diffusion measurements. This work expands the capabilities of NMR for probing soft matter, with implications for polymer recycling and materials design.

Fricke, Sophia N. [University of California, Berke↗

DOE OSTI2023

Multidimensional visualization of the dynamic evolution of Li metal via in situ/operando methods

The growing demands for high-energy density electrical energy storage devices stimulate the coupling of conversion-type cathodes and lithium (Li) metal anodes. While promising, the use of these “Li-free” cathodes brings new challenges to the Li anode interface, as Li needs to be dissolved first during cell operation. In this study, we have achieved a direct visualization and comprehensive analysis of the dynamic evolution of the Li interface. The critical metrics of the interfacial resistance, Li growth, and solid electrolyte interface (SEI) distribution during the initial dissolution/deposition processes were systematically investigated by employing multidimensional analysis methods. They include three-electrode impedance tests, in situ atomic force microscopy, scanning electrochemical microscopy, and cryogenic scanning transmission electron microscopy. The high-resolution imaging and real-time observations show that a loose, diffuse, and unevenly distributed SEI is formed during the initial dissolution process. This leads to the dramatically fast growth of Li during the subsequent deposition, deviating from Fick’s law, which exacerbates the interfacial impedance. The compactness of the interfacial structure and enrichment of electrolyte species at the surface during the initial deposition play critical roles in the long-term stability of Li anodes, as revealed by operando confocal Raman spectroscopic mapping. In conclusion, our observations relate to ion transfer, morphological and structural evolution, and Li (de)solvation at Li interfaces, revealing the underlying pathways influenced by the initial dissolution process, which promotes a reconsideration of anode investigations and effective protection strategies.

25 ENERGY STORAGE↗

DOE OSTI2018

Application of materials informatics to vapor-grown carbon nanofiber/vinyl ester nanocomposites through self-organizing maps and clustering techniques

Data mining and knowledge discovery techniques were employed herein to acquire new information on the viscoelastic, flexural, compressive, and tensile properties of vapor-grown carbon nanofiber (VGCNF)/vinyl ester (VE) nanocomposites. Formulation and processing factors (curing environment, presence or absence of dispersing agent, mixing method, VGCNF weight fraction, VGCNF type, high-shear mixing time, and sonication time) and testing temperature were utilized as inputs and the true ultimate strength, true yield strength, engineering elastic modulus, engineering ultimate strength, flexural modulus, flexural strength, storage modulus, loss modulus, and tan delta were selected as outputs. The data mining and knowledge discovery algorithms used in this study include self-organizing maps (SOMs) and clustering techniques. SOMs demonstrated that temperature and tan delta had the most significant effects on the output responses followed by the VGCNF high-shear mixing time, and sonication time. SOMs were also used to produce optimal responses using certain combination(s) of inputs. Fuzzy C-means algorithm (FCM) was also applied to discover patterns in the nanocomposite behavior subsequent to a principal component analysis (PCA), which is a dimensionality reduction technique. Utilizing these techniques, the nanocomposite specimens were separated into different clusters based on the testing temperature (30 °C and 120 °C being the most dominant responses), tan delta, high-shear mixing time, and sonication time. Furthermore, the VGCNF/VE specimens were separated into a cluster based on their viscoelastic responses (storage and loss moduli) at the same temperature. The FCM results indicate that, while all nanocomposite properties in the new framework are essential, the viscoelastic responses of the VGCNF/VE specimens are the most significant. Here this work highlights the utility of data mining and knowledge discovery techniques in the context of materials informatics for the discovery of patterns and trends in the material behavior that are not immediately known.

36 MATERIALS SCIENCE↗

DOE OSTI2024

ARJUNA: An Electrochemical Interface Mapping Probe for Solid-State Batteries

Solid-state batteries (SSBs) are promising candidates for next-generation energy storage, although their performance can be compromised by interfacial heterogeneity within the electrolyte. Furthermore, ensuring the quality of large form-factors electrolyte film is crucial for establishing a robust manufacturing platform for solid-state batteries. Herein, we report on the use of ARJUNA, an electrochemical interface mapping system, to characterize heterogeneities at solid electrolyte interfaces and to serve as a quality control system for SSB manufacturing. In addition to spatial mapping, the proposed system can also probe the interface behavior as a function of pressure and temperature. We present the operating principle, design, instrumentation, and evaluation of the system alongside a typical hybrid solid electrolyte produced using two common manufacturing processes. This report showcases the capability of ARJUNA to probe the heterogeneity and quality of processed solid electrolyte films.

25 ENERGY STORAGE↗

DOE OSTI2026

CHESS 2025: Spectrometer orthorectified at-sensor radiance from NEON AOP imaging spectroscopy surveys

This dataset provides Level 1 (L1) orthorectified at-sensor radiance derived from measurements collected by the Imaging Spectrometer-1 (NIS-1) onboard the NEON (National Ecological Observatory Network) Airborne Observation Platform (AOP) for the 2025 Colorado Headwaters Ecological Spectroscopy Study (CHESS). NIS-1 captures light reflected from the Earth’s surface in 426 discrete wavelength bands as raw digital numbers (DNs; Level 0). These data are then calibrated to physical units (uW/cm²·sr·nm) following the processing steps described in the NEON Imaging Spectrometer Level 1B Calibrated Radiance Algorithm Theoretical Basis Document (ATBD; Gallery 2022). The data delivered here are the primary inputs for the surface reflectance product in “Custom surface reflectance, shade masks, and equivalent water thickness maps for the Colorado Headwaters Ecological Spectroscopy Study” (Carroll et al. 2026). For intertemporal comparison, the radiance data here are most directly relatable to the v2 radiance data in “NEON AOP Imaging Spectroscopy Survey of Upper East River Colorado Watersheds: Raw-Space Radiance and Observational Variable Dataset” (Goulden et al. 2018), to which the same processing methodology was applied. Together, the radiance and reflectance data enable users to exploit the unique reflection signatures of different surface objects for land cover classification, foliar trait mapping, plant vigor assessment, water content estimation, trace-element identification, and other scientific applications. The data were acquired over three study domains in the Upper Gunnison river basin: the upper East River watershed (CRBU); Almont Triangle and Taylor Canyon (ALMO); and Upper Taylor River watershed (UPTA) between 2025-06-13 and 2025-07-15. Within each domain, data are delivered by flightline as orthorectified and calibrated hyperspectral rasters in Hierarchical Data Format version 5 (HDF5) format, with radiance values provided in uW/cm²·sr·nm on a fixed, uniform Universal Transverse Mercator (UTM) grid at 1 meter spatial resolution. The radiance rasters include all 426 NIS-1 spectral bands, along with associated quality-assurance (QA) and diagnostic and ancillary layers needed for atmospheric correction workflows. Orthorectified radiance is produced from pushbroom spectrometer observations by applying NEON’s radiometric calibration (including bad pixel masking, dark subtract, dark pedestal shift correction, electronic panel ghost correction, grating ghost correction, deblur correction and flat-fielding) and spectral calibration (using spectral response function band centers and full-width at half-maximum intensity), followed by geolocation and regridding to the fixed grid. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgement: Field and remote-sensing data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). This work was also 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.

2018 NEON and 2025 CHESS Campaigns↗

DOE OSTI2025

Map-level baryonification: unified treatment of weak lensing two-point and higher-order statistics

Precision cosmology benefits from extracting maximal information from cosmic structures, motivating the use of higher-order statistics (HOS) at small spatial scales. However, predicting how baryonic processes modify matter statistics at these scales has been challenging. The baryonic correction model (BCM) addresses this by modifying dark-matter-only simulations to mimic baryonic effects, providing a flexible, simulation-based framework for predicting both two-point and HOS. We show that a 3-parameter version of the BCM can jointly fit weak lensing maps' two-point statistics, wavelet phase harmonics coefficients, scattering coefficients, and the third and fourth moments to within 2% accuracy across all scales ℓ < 2000 and tomographic bins for a DES-Y3-like redshift distribution ( z ≲ 2), using the FLAMINGO simulations. These results demonstrate the viability of BCM-assisted, simulation-based weak lensing inference of two-point and HOS, paving the way for robust cosmological constraints that fully exploit non-Gaussian information on small spatial scales.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

Emergence and consequences of lateral sample heterogeneity in glow discharge spectrometry

We report conventional glow discharge emission or mass spectrometry requires the assumption that the surface of the sample is homogeneous. However, recent developments in glow discharge imaging appear to offer an opportunity to obtain three-dimensional concentration maps, in which this assumption is no longer necessary. Here, experiments, models, and a summary of earlier work are combined to examine the sputtering behavior of elemental and morphological heterogeneities in a sample. The theoretical model reveals gaps in current knowledge of glow discharge sputtering of heterogeneous samples, particularly indicating that heterogeneity in the sample leads to roughened crater bottoms and how additional morphology can evolve. Additionally, a three-dimensional profiling microscope is used to characterize the effects of surface inclusions on the sputtering process in a DC glow discharge in a reduced-pressure argon environment. Findings have important implications for bulk analysis, depth-profiling, and elemental surface mapping with glow discharge spectrometry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Advanced Imaging of the Left Atrium with Cardiac Magnetic Resonance: A Review of Current and Emerging Methods and Clinical Applications

Purpose of Review: The thin wall of the left atrium (LA) is difficult to visualize. The high spatial and temporal resolution of cardiac magnetic resonance (CMR) allows for the most accurate assessment of left atrial structure, function, and tissue characterization. This review will describe emerging methods used to image the LA with CMR, and will discuss associated clinical applications. Recent Findings: LA function can now be described in a dynamic fashion with feature-tracking algorithms. Novel methods have been created to augment visualization of late gadolinium enhancement (LGE) in the LA, and left atrial fibrosis can be further quantified with T1 mapping algorithms. The advancement in how we image the LA with CMR has implications in atrial fibrillation (AF) and cardiomyopathic processes. Summary: A nuanced understanding of left atrial pathology is emerging with the rapid advancement of CMR technology, which has implications in the risk stratification and treatment of several cardiovascular disease states.

62 RADIOLOGY AND NUCLEAR MEDICINE↗