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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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DOE OSTI2024

Modeling Sorption of Water and Vanadium Cations by Ion-Exchange Membranes

We develop a macroscopic model for sorption of ions and solvent by ion-exchange membranes and use it to investigate how different membranes behave when exposed to electrolytes for vanadium flow batteries. The model combines a classical expression for the Gibbs free energy of a moderately concentrated electrolyte containing several ions with a term that describes elastic swelling of a polymer membrane. Parameters describing pairwise interactions between mobile ions and fixed membrane ions were fit to published experimental data and discussed in the context of the competition for sites in ion-exchange membranes. The model provides insights into why sorption of vanadium cations by Nafion is lower than predicted by Donnan theory and why switching to an anion-exchange membrane does not dramatically reduce vanadium sorption.

Electrochemistry↗

DOE OSTI2025

Eddy Correlation Flux Measurement System (ECOR) Instrument Handbook

The eddy correlation (ECOR) flux measurement system provides in situ, half-hour measurements of the surface turbulent fluxes of momentum, sensible heat, latent heat, and carbon dioxide (CO 2 ) (and methane at the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility’s North Slope of Alaska central facility [NSA Barrow], and at the ARM Mobile Facility (AMF) deployment at Oliktok Point). The fluxes are obtained with the eddy covariance technique, which involves correlation of the vertical wind component with the horizontal wind component, the air temperature, the water vapor density, and the CO 2 concentration. The instruments used are: a fast-response, three-dimensional (3D) wind sensor (sonic anemometer) to obtain the orthogonal wind components and the speed of sound (SOS) (used to derive the air temperature), an open-path infrared gas analyzer (IRGA) to obtain the water vapor density and the CO 2 concentration, and an open-path infrared gas analyzer (IRGA) to obtain methane density and methane flux at NSA Barrow and at the AMF deployment at Oliktok Point. The ECOR systems are deployed at the locations where other methods for surface flux measurements (e.g., energy balance Bowen ratio [EBBR] systems) are difficult to employ, primarily at the north edge of a field of crops. A surface energy balance system (SEBS) has been installed collocated with each deployed ECOR system in SGP, NSA, Tropical Western Pacific (TWP), and each of three ARM Mobile Facilities. The surface energy balance system consists of upwelling and downwelling solar and infrared radiometers within one net radiometer, a wetness sensor, and soil measurements. Finally, the SEBS measurements allow the comparison of ECOR sensible and latent heat fluxes with the energy balance determined from the SEBS and provide information on wetting of the sensors for data quality purposes.

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

Algorithms and file structures to extend and enhance liquid chromatography and ion mobility mass spectrometry workflows (CRADA Final Report)

The purpose of this project was to continue supporting customizations of algorithms and raw data file structures to enhance software workflows for liquid chromatography (LC), mass spectrometry (MS) and ion mobility mass spectrometry (IM-MS)-based protein and metabolite characterization. PNNL worked with Agilent to design, implement, evaluate, and demonstrate new algorithms and integrated them as functionalities into the PNNL-PreProcessor software. The project augmented PNNL’s capabilities to analyze complex proteomics and metabolomics samples. These capabilities are directly beneficial to DOE and PNNL efforts to characterize and analyze these compounds in microbial and plant communities. The project assisted Agilent in further developing improved instrument-software solutions combining liquid chromatography and ion mobility with mass spectrometry for widespread applications in life sciences and other fields.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Continuous Baseline Microphysical Retrieval (MICROBASE) Value-Added Product Report

This technical report describes the Continuous Baseline Microphysical Retrieval (MICROBASE) Value-Added Product (VAP) produced operationally by the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) User Facility. MICROBASE provides a continuous estimate of cloud microphysical properties at ARM fixed observatories and ARM Mobile Facility (AMF) sites. It is designed to run operationally and provide data to the ARM Data Center for scientific distribution. This technical report presents an overview of the VAP as a resource for data users and ongoing records for major updates to these products.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Design and Integration of a Compact Mobile Outdoor Soil Sampling Agricultural Robot

The lack of feedback on soil management for crop systems necessitates the collection of soil samples to derive insights into prevailing soil conditions. Achieving an accurate model of these plantations requires a substantial number of soil samples, which is a labor-intensive process that can suffer from inconsistencies in both the methodology of collection and the locations from which samples are gathered. In addition, the close spacing of crop rows constrains the maneuverability of mobile agricultural equipment during sample collection. This article proposes the use of agricultural robotics to facilitate the acquisition of composite soil samples with minimal human intervention, allowing for precise data collection for a Populus plant system. More specifically, this article presents a case study on the design and construction of a compact agricultural robot for soil sample collection. The performance of the robot, including its soil collection efficacy, maneuverability, and positional accuracy, is evaluated.

42 ENGINEERING↗

DOE OSTI2025

Uncovering heterogeneous intercommunity disease transmission from neutral allele frequency time series

The COVID-19 pandemic has underscored the need for accurate epidemic forecasting to predict pathogen spread, evolution, and evaluate intervention strategies. Forecast reliability hinges on detailed knowledge of disease transmission across population segments, which may be inferred from contact surveys or mobility data. However, these indirect approaches make it difficult to estimate rare transmissions between socially or geographically distant communities. We show that the steep ramp-up of genome sequencing surveillance during the pandemic can be leveraged to directly identify transmission patterns between geographically defined communities. Our approach uses a hidden Markov model to infer the fraction of infections a community imports from others based on how rapidly allele frequencies in the focal community converge to those in the donor communities. Applying this method to SARS-CoV-2 sequencing data from England and the United States, we uncover networks of intercommunity transmission that reflect geographical relationships while exposing significant long-range interactions. The scaling of importation rate with distance is consistent across both countries, yet weaker than expected based on mobility data, highlighting limitations of indirect inference. We show that transmission patterns can change between waves of variants of concern and analyze how the inferred heterogeneity in intercommunity transmission impacts evolutionary forecasts. While applied here to geographically defined communities, our approach could be applied to those defined by other traits (e.g., age, socioeconomic status), provided time-series data can be stratified accordingly. Overall, our study highlights population genomic time series data as a crucial record of epidemiological interactions, which can be deciphered using tree-free inference methods.

Okada, Takashi [Department of Physics; University ↗

DOE OSTI2024

Evaluation of a Reference-Free Collision Cross Section Calibration Strategy for Proteomics Using SLIM-Based High-Resolution Ion Mobility Spectrometry–Mass Spectrometry

Ion mobility spectrometry (IMS) is a gas-phase analytical technique that separates ions with different sizes and shapes and is compatible with mass spectrometry (MS) to provide an additional separation dimension. The rapid nature of the IMS separation combined with the high sensitivity of MS-based detection and the ability to derive structural information on analytes in the form of the property collision cross section (CCS) makes IMS particularly well-suited for characterizing complex samples in -omics applications. In such applications, the quality of CCS from IMS measurements is critical to confident annotation of the detected components in the complex -omics samples. However, most IMS instrumentation in mainstream use requires calibration to calculate CCS from measured arrival times, with the most notable exception being drift tube IMS measurements using multifield methods. The strategy for calibrating CCS values, particularly selection of appropriate calibrants, has important implications for CCS accuracy, reproducibility, and transferability between laboratories. The conventional approach to CCS calibration involves explicitly defining calibrants ahead of data acquisition and crucially relies upon availability of reference CCS values. In this work, we present a novel reference-free approach to CCS calibration which leverages trends among putatively identified features and computational CCS prediction to conduct calibrations post-data acquisition and without relying on explicitly defined calibrants. We demonstrated the utility of this reference-free CCS calibration strategy for proteomics application using high-resolution structures for lossless ion manipulations (SLIM)-based IMS-MS. In conclusion, we first validated the accuracy of CCS values using a set of synthetic peptides and then demonstrated using a complex peptide sample from cell lysate.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

EPCAPE-PT-LANL Measurements: Scanning Mobility Particle Sizer

Coastal cities offer a unique environment for studying aerosol-cloud interactions and the effects of urban emissions on cloud properties. As part of the Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE), the Partitioning Thrust by Los Alamos National Laboratory (EPCAPE-PT-LANL) was conducted. Our campaign focused on measuring the optical and chemical properties of aerosols and their interactions within marine stratocumulus clouds in La Jolla, California. EPCAPE-PT-LANL enhances the primary goals of EPCAPE through innovative observations of vapor-phase transitions between aerosols and cloud droplets, the impact of black carbon on aerosol-cloud dynamics, and the effects of cloud processing on aerosol optical properties. Instrument: Scanning Mobility Particle Sizer (TSI), DMA Classifier Model 3082 + CPC Detector Model 3752. Data Notes: The SMPS dataset is limited due to necessary instrument maintenance and subsequent extensive downtime. Data is only available from October 21 to November 8, 2023. Post November 22, 2023, the NanoScan instrument provided coverage for the latter half of the campaign. To ensure consistency, the data was resampled into uniform size bins ranging from 15 nm to 667 nm. This adjustment was required due to changes in size bins and the maximum diameter that occurred after maintenance and repairs. Multiple Charge Correction applied, Nanoparticle Agglomerate Mobility Analysis not applied, and Diffusion Correction applied. Header: - List [15 nm to 667 nm] middle of the size bin in nanometers: Data reports the concentration particles in this size bin per cubic centimeter, dN/dLog(dp). - CVI_Flag[bool]: A boolean flag indicating whether the Counterflow Virtual Impactor (CVI) was active (true) or inactive (false) during the measurement.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Mixed-phase Convective Clouds in the Polar Marine Boundary Layer

This proposal, a collaboration between two universities, firstly aims to describe the high-latitude CAO cloud regime using the array of instruments at the NSA (Barrow, Alaska) and at two mobile ARM sites (MARCUS 2017-18, and COMBLE 2020). These ARM data, and the multi-sensor variables we propose to derive, allow detailed descriptions of mesoscale organization of precipitation, thermodynamic profiles, vertical velocity, cloud depth, and cloud and precipitation properties. These observations are key for our second goal, which is to use output from validated cloud- and eddy-resolving simulations to gain insights into the linkages between microphysical processes, the MBL vertical structure, and the MBL dynamics that control cloud macrostructure.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Exploring the Utility-Privacy Trade-Off: Impacts of Semantic and Visit Types Ambiguities on Human Mobility Simulation

Humans are in perpetual movement, constantly traversing buildings, cities, waters, oceans, and countries. Mobility stands out as a major driving force shaping our modern societies. Capturing and explaining human behavior in a world of eight billion distinct mobility agendas is a complex challenge. With the rise of interconnected devices and platforms, such as smartphones, wearables, and point-of-interest data, largescale behavioral data has become more accessible, enabling rich insights into mobility patterns. However, the widespread availability of such data introduces significant ethical challenges. Detailed mobility data can inadvertently reveal sensitive personal information, including individuals' locations, habits, social interactions, and even political or religious affiliations. Beyond privacy breaches, the ethical implications of uncovering and potentially manipulating underlying behavioral patterns demand attention. Striking a balance between the utility of mobility models and the protection of individual privacy is therefore paramount. This paper explores the utility-privacy trade-offs in human mobility modeling, focusing on the impacts of introducing semantic and visit type ambiguities. By systematically examining how these ambiguities affect the fidelity of simulated trajectories and privacy risks, we provide a framework for evaluating ethical and privacy-conscious modeling practices. Our findings emphasize the need for methods that safeguard privacy without undermining the usefulness of mobility models, contributing to the responsible advancement of mobility science in alignment with ethical standards and societal expectations.

Amichi, Licia [ORNL] (ORCID:0000000177631394)↗

DOE OSTI2026

CHESS 2025: Location data for field observations and sampling

This dataset represents geolocation data associated with field observations and sampling from the Colorado Headwaters Ecological Spectroscopy Study (CHESS) during June and July of 2025. Location data were collected using Trimble DA2 Global Navigation Satellite System (GNSS) receivers with Trimble Catalyst 2 centimeter (cm) positioning service and the Environmental Systems Research Institute (Esri) Field Maps mobile app. Files in this data package include meadow site polygons, shrub site polygons, tree site polygons and stem point locations, and Leaf Area Index (LAI) plot polygons (.geojson). The geojson files can be opened with open-source GIS software (e.g, QGIS). A csv file is also provided with point coordinates for all locations. 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 Acknowledgment: 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 OSTI2026

Summary of Responses to the Request for Information (RFI) on Partnerships for Transformational Artificial Intelligence Models

The Department of Energy (DOE) issued a Request for Information (RFI) in December 2025 inviting public comments regarding partnerships for transformational Artificial Intelligence (AI) models for the Genesis Mission Consortium, a public-private partnership platform. This RFI solicited feedback from industry, nonprofit organizations, universities, independent research organizations and other stakeholders. Specifically, the RFI asked three questions on (1) mobilizing DOE National Laboratories to curate the scientific data in a responsible and privacy-preserving manner, (2) the extent to which existing general-purpose AI models can be leveraged and which scientific disciplines are priorities for such model development, and (3) mechanisms by which these AI models can be provided to scientific communities. This document summarizes the input from 194 unique nonproprietary responses from businesses, universities, nonprofit organizations, research institutes and laboratories as well as a variety of other contributors, including individual contributions.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Optimization of Scrap Melting Using an Electric Arc in Steel Manufacturing

Steel industry is crucial to the national economy and security. Around 67% of crude steel in the U.S is produced in electric arc furnaces (EAF), which is energy intensive. Around 140 EAFs operate in the U.S., consuming about 8.6x10 7 MMBtu/year of electricity. One of major challenges for EAFs includes maximizing the efficiency of the electrical energy provided in the form of electric arcs to melt various scrap mixes. To address this issue, a computational fluid dynamics (CFD) methodology is chosen to analyze scrap melting using the electric arc. Due to complex furnace phenomena and the wide variety of potential scenarios, high performance computing (HPC) is essential to yield comprehensive and detailed CFD analyses and systematic parametric studies for optimized EAF operation. The objectives are to 1) simulate scrap melting using electric arc, 2) evaluate electrode/arc position for optimum scrap melting and 3) establish reduced order model for CFD data-base for fast model calculation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Dataset for "A primer on forest structure measurement with lidar for ecologists"

This repository includes data and code accompanying the case study included in the manuscript "A primer on forest structure measurement with lidar for ecologists" (submitted to Ecosphere). We compiled lidar datasets from multiple platforms in a common area to: 1. Demonstrate how differences in sensor characteristics influence density and resolution of lidar data. 2. Provide open-source, co-located datasets for users to further inspect differences in lidar data. 3. Provide example code to perform basic lidar analysis. This case study is meant to allow readers to get hands-on experience with real-world data from different platforms. This case study is not meant to be a rigorous comparison of derived ecological metrics among all sensors; such comparisons can be found throughout other publications referenced throughout the main manuscript. Code includes basic functions in R commonly used to visualize and manipulate lidar data accessible with a normal laptop computer; more sophisticated algorithms for advanced users are also referenced throughout the main manuscript. Terrestrial laser scanning (TLS), mobile laser scanning (MLS), UAS laser scanning (ULS), airborne laser scanning (ALS), and spaceborne laser scanning (SLS) data were collected within the Smithsonian Environmental Research Center (SERC) forest dynamics plot in Maryland, USA. TLS, MLS, and ALS data were collected within 1 month of the 2021 growing season; ULS data were collected in November 2020 (“leaf-off” data) and July 2022 (“leaf-on” data).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

A rheological model for loose sands with insights from DEM

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

Elasticity↗

DOE OSTI2025

EPCAPE Radar b1 Data Processing: Corrections, Calibrations, and Processing Report

The U.S. Department of Energy (DOE)’s Atmospheric Radiation Measurement (ARM) user facility recently deployed its First ARM Mobile Facility (AMF1) to La Jolla, California as part of the Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) campaign. Some of the goals behind EPCAPE were to characterize the diurnal and seasonal cycles of stratocumulus clouds and to investigate the cloud-aerosol-radiation interactions and feedbacks in the area. The deployment of the AMF1 for a full year from 15 February 2023 to 14 February 2024 aided in addressing these scientific questions. While AMF1 collected data year-round, enhanced measurements were taken during two intensive operational periods (IOPs). The first IOP occurred from April to June and focused on the chemistry of low clouds (EPCAPE_Chem), while the second IOP occurred from July to September and was focused on the radiation of high clouds (EPCAPE_Radiation). Several cloud radars were deployed with AMF1 to collect valuable data on cloud properties that will help users address key science objectives. As in past ARM campaigns, a1-level radar data is extensively analyzed and calibration techniques are performed to generate b1-level data (Matthews et al. 2023, Feng et al. 2024). Radar data at the b1-level are of the highest quality and thus can be used to examine scientific questions. The status of the a1-level data and the a1-to-b1 process for the EPCAPE radars is subsequently detailed in this document.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Particle size distributions from TRACER-MAP

Particle size distributions measured using a custom scanning mobility analyzer (SMPS) and a TSI, Inc. aerodynamic particle sizer (APS). The size distributions measured with the two instruments were merged during data inversion to produce a single distribution spanning a diameter range from approximately 19 nanometers to 15 micrometers. Size-dependent cloud condensation nuclei (CCN) activity measured with a CCN counter operated in parallel to the condensation particle counter in the SMPS are reported separately. The measurements were made from the Baylor University/University of Houston Mobile Air Quality Laboratory (MAQL) between July 5 and August 30, 2022 during the TRACER-MAP project. The trailer moved between five sites in the Houston regions as follows: Aldine: 7/5-7/8, 7/21-7/26, 8/15-8/24 University of Houston: 7/8-7/16, 8/8-8/15 San Jacinto monument: 7/16-7/21 Jones State Forest: 7/26-8/1; 8/24-8/30 AMF site in La Porte: 8/1-8/8

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Particle size distributions from TRACER-MAP

54 ENVIRONMENTAL SCIENCES↗