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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Columbia River Surface Water and Pore Water Unaligned FTICR-MS Data Associated with “Using Community Assembly Metrics from Metacommunity Ecology to Understand Environmental Metabolomes”

Surface water and pore water samples were collected at five sites along a shoreline transect of the Columbia River, southeastern WA, USA in 2017. This dataset is comprised of (1) high resolution characterization of dissolved organic matter via 12 Tesla Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) analyzed through the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory) and (2) a metadata csv file. The FTICR-MS files are .xml, and the data package includes instructions for using Formularity (https://omics.pnl.gov/software/formularity) and an R script to process the data based on the user’s specific needs. The data are associated with the publication “Using community assembly metrics from metacommunity ecology to understand environmental metabolomes” that will be published in Nature Communications (Danczak et al.). The study aimed to investigate gaps in understanding the spatiotemporal organization of environmental metabolomes to improve the ability to predict ecosystem function. Associated sequencing data will be published via NCBI. The access information for NCBI and the publication DOI will be added to this abstract when they are available. Please use the data package’s DOI to cite the data package. We ask that you acknowledge the U.S. Department of Energy (DOE) Biological and Environmental Research (BER) Subsurface Biogeochemical Research (SBR) program when using the data. All data are free to be used for any purpose, such as for manuscripts, presentations, and grant proposals. There is no obligation to include data package authors as co-authors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Aligned FTICR-MS, Respiration, and Biochemical Transformation Data from Columbia River Sediment Incubations Associated with: “Carbon Limitation Leads to Thermodynamic Regulation of Aerobic Metabolism”

Columbia River hyporheic zone sediments from the Hanford Site 300 Area in eastern Washington were incubated in a full factorial design with different organic matter (OM) amendments. The aim of the experiment was to test the influence of OM thermodynamics on aerobic respiration across different organic carbon pools and concentrations. This dataset is comprised of (1) a csv with high resolution characterization of dissolved organic matter via 12 Tesla Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) analyzed through the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory), (2) a csv with sample IDs, replicates, amendments, dissolved oxygen (DO) measurements, and sampling time points, (3) a csv with sample IDs, replicates, respiration rates, FTICR summary metrics, and relative abundance of chemical classes; and (4) a csv with the number of chemical transformations in each sample replicate (microcosm). The data are associated with the publication “Carbon Limitation Leads to Thermodynamic Regulation of Aerobic Metabolism” published in ES&T Letters (Garayburu-Caruso et al., 2020). Please use the data package’s DOI to cite the data package. We ask that you acknowledge the U.S. Department of Energy (DOE) Biological and Environmental Research (BER) Subsurface Biogeochemical Research (SBR) program when using the data. All data are free to be used for any purpose, such as for manuscripts, presentations, and grant proposals. There is no obligation to include data package authors as co-authors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Open Reproducible Electron Microscopy Data Analysis

Electron microscopy (EM) is a cornerstone technique in the materials and biological sciences capable of imaging structures at nano- to atomic-scale resolution. Advances in technologies mean that one acquires datasets at increasing data rates and sizes. These advancements present enormous opportunities for researchers to understand complex systems. However, processing the resulting large-scale, complex data in a reproducible and shareable way is a real challenge for researchers. The building, managing, and maintaining complex workflows in a reproducible manner requires extensive knowledge in several areas outside the researchers’ core skill sets, such as software engineering, data science, and high-performance computing (HPC). Our work demonstrates an innovative approach to solving these problems, enabling reproducible EM data analysis through container encapsulated pipelines. Using modern container technologies, we encapsulate processing elements and connect them using shared memory. We expose user-friendly, advanced algorithms and tools to allow end users to utilize without expert programming skills. The platform enables reproducible, scalable, shareable pipelines for the analysis and visualization of EM data. Focusing on interoperability, we leverage the DOE and other agencies’ existing investments to provide a powerful software platform for EM data analysis.

Harris, Christopher↗
DOE OSTI2024

Roadmap on data-centric materials science

Science is and always has been based on data, but the terms ‘data-centric’ and the ‘4th paradigm’ of materials research indicate a radical change in how information is retrieved, handled and research is performed. It signifies a transformative shift towards managing vast data collections, digital repositories, and innovative data analytics methods. The integration of artificial intelligence and its subset machine learning, has become pivotal in addressing all these challenges. This Roadmap on Data-Centric Materials Science explores fundamental concepts and methodologies, illustrating diverse applications in electronic-structure theory, soft matter theory, microstructure research, and experimental techniques like photoemission, atom probe tomography, and electron microscopy. While the roadmap delves into specific areas within the broad interdisciplinary field of materials science, the provided examples elucidate key concepts applicable to a wider range of topics. The discussed instances offer insights into addressing the multifaceted challenges encountered in contemporary materials research.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Analytics-at-scale of Sensor Data for Digital Monitoring in Nuclear Plants (3 rd Annual Report)

Nuclear power plants collect and store large volumes of heterogeneous data from various components and systems. With recent advances in machine learning (ML) techniques, these data can be leveraged to develop diagnostic and short-term forecasting models to better predict future equipment condition. Maintenance operations can then be planned in advance whenever degraded performance is predicted, thus resulting in fewer unplanned outages and the optimization of maintenance activities. This enables lower maintenance costs and improves the overall economics of nuclear power. This report primarily focuses on developing a short-term forecasting process that leverages a feature selection process to distill large volumes of heterogeneous data and predict specific equipment parameters. A variety of feature selection methods, including Shapley Additive Explanations (SHAP) and variance inflation factor (VIF), were used to select the optimal features as inputs for three ML methods: long short-term memory (LSTM) networks, support vector regression (SVR), and random forest (RF). Each combination of model and input features was used to predict a pump bearing temperature both 1 and 24 hours in advance, based on actual plant system data. The optimal inputs for the LSTM and SVR were selected using the SHAP values, while the optimal input for the RF consisted solely of the response variable itself. Each model produced similar 1-hour-ahead predictions, with root mean square errors (RMSEs) of roughly 0.006. For the 24-hour-ahead predictions, differences could be seen between LSTM, SVR, and RF, as reflected by model performances of 0.036 ± 0.014, 0.0026 ± 0, and 0.063 ± 0.004 RMSE, respectively. As big data and continuous online monitoring become more widely available, the proposed feature selection process can be used for many applications beyond the prediction of process parameters within nuclear infrastructure. This report summarizes the Fiscal Year 2021 research progress encompassing the (1) data cleaning and feature selection necessary for ML applications; (2) development of short-term forecasting models to predict future plant process parameters for both single and multiple time steps ahead; and (3) validation of the feature selection methods and short-term forecasting models given new data from different systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

AmeriFlux FLUXNET-1F US-ORv Olentangy River Wetland Research Park

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-ORv Olentangy River Wetland Research Park. This is the FLUXNET version of the carbon flux data for the site US-ORv Olentangy River Wetland Research Park produced by applying the standard ONEFlux (1F) software. Site Description - The ORWRP site is a 21-ha large-scale, long-term wetland campus facility that is owned by Ohio State University. It is designed to provide teaching, research, and service related to wetland and river science and ecological engineering. The site has been developed in several phases: Phase 1 (1992 - 1994) - Construction of two 2.5-acre deepwater marshes and a river water delivery system began, with pumps installed on the floodplain to bring water from the Olentangy River. In May 1994, one wetland was planted with marsh vegetation, while the other remained as an unplanted control; Phase 2 (1994 - 1999) - Development of a research and teaching infrastructure took place with the construction of boardwalks, a pavilion, and a compound. The creation of the 7-acre naturally flooded oxbow was also included; Phase 3 (2000 - 2003) - As a research building was created, three additional wetlands were created in the vicinity of the building, including a stormwater wetland that receives runoff from the roof of the building; Phase 4 - The current phase involves research access to the Olentangy River.

Bohrer, Gil↗
DOE OSTI2026

AmeriFlux FLUXNET-1F US-RC3 WSU Lind Dryland Research Station

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-RC3 WSU Lind Dryland Research Station. This is the FLUXNET version of the carbon flux data for the site US-RC3 WSU Lind Dryland Research Station produced by applying the standard ONEFlux (1F) software. Site Description - RC3 operated from 2011-2016 at the Washington State University Lind Dryland Experimental Station, as part of a cluster of 5 towers (RC1 to RC5) operated for the Regional Approaches to Climate Change (REACCH) USDA-supported research project. Lind is in the low precipitation region of the Columbia Plateau dryland cropping region. The area is characterized as a stable crop-fallow agroecological zone, with crops typically grown every second year to allow soil water to accumulate during intervening fallow years. Winter wheat was grown August 2012-August 2013, and August 2014-July 2015. The growing seasons of 2012 and 2014 were fallow years. Soils at Lind are predominantly silt loam texture Mollisols in the Shano and Ritzville series. Site topography is flat.

Chi, Jinshu [The Hong Kong University of Science a↗
DOE OSTI2021

Shedding Light on Primary Donors in Photosynthetic Reaction Centers

Chlorophylls (Chl)s exist in a variety of flavors and are ubiquitous in both the energy and electron transfer processes of photosynthesis. The functions they perform often occur on the ultrafast (fs–ns) time scale and until recently, these have been difficult to measure in real time. Further, the complexity of the binding pockets and the resulting protein-matrix effects that alter the respective electronic properties have rendered theoretical modeling of these states difficult. Recent advances in experimental methodology, computational modeling, and emergence of new reaction center (RC) structures have renewed interest in these processes and allowed researchers to elucidate previously ambiguous functions of Chls and related pheophytins. This is complemented by a wealth of experimental data obtained from decades of prior research. Studying the electronic properties of Chl molecules has advanced our understanding of both the nature of the primary charge separation and subsequent electron transfer processes of RCs. In this review, we examine the structures of primary electron donors in Type I and Type II RCs in relation to the vast body of spectroscopic research that has been performed on them to date. Further, we present density functional theory calculations on each oxidized primary donor to study both their electronic properties and our ability to model experimental spectroscopic data. This allows us to directly compare the electronic properties of hetero- and homodimeric RCs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

American Heart Association Protein Atlas Portal for Accelerated Drug Discovery

The American Heart Association's Protein Atlas Data Portal is a cutting-edge, open-source atlas of the structural determinants of the binding of drugs to (all of) their target proteins as an unbiased means to accelerate drug discovery. The portal will provide researchers with access to comprehensive data that can help increase the selection of effective drugs and cut the time-to-market of new drugs by half.

drug targets↗
DOE OSTI2023

Improving the Quality of Geothermal Data Through Data Standards and Pipelines Within the Geothermal Data Repository: Preprint

For machine learning outputs to be applicable to real world problems, high quality data are needed to ensure high quality results. With the more recent emphasis on machine learning in geothermal, there is an increasing need for greater focus on the quality of the data available for use in these projects. For example, Geothermal Operational Optimization Using Machine Learning (GOOML) utilized large quantities of geothermal power plant operational data to inform power plant operational configurations to maximize power generation. High quality datasets result from dependable sensors or devices collecting data, high frequency of measurements, sufficient data points, adequate metadata, reliable storage of data, and sufficient data curation. Another component that contributes to high quality data is reusability, which can be enhanced through data standardization. Data Standardization creates consistency in formatting and contents of like datasets, lessening preprocessing requirements and ensuring adequate information provided by a given dataset. The Geothermal Data Repository (GDR) aims to help improve data quality through automated data standardization for high-value datasets through the implementation of data pipelines alongside reliable and accessible long-term storage for datasets. As such, the GDR has decided to shift away from recommending the use of Excel-based content models and towards the implementation of automated data pipelines. This takes the burden of data standardization off the user and project team and will increase the availability of standardized geothermal data available through the GDR. A set of recommendations, or a data standard for each data type will exist with each data pipeline in order to advise data collection for maximum usability for future research. This paper serves to describe the GDR's proposed transition towards data standardization through automated data pipelines, to discuss the need for and value of such a shift, and to call for suggestions from the community regarding the most useful data standards and pipelines.

data↗
DOE OSTI2025

Advancing the Frontiers of Deep Learning for Low-Dose 3D Cone-Beam CT Reconstruction

X-ray computed tomography (CT) is an important noninvasive medical imaging modality for studying the structural details of internal organs. Image reconstruction in CT is an inverse problem of recovering an object's internal structure from the absorption profile of X-ray beams (sinogram) measured using a detector. The classical variational approach for CT reconstruction minimizes an energy functional using an appropriate iterative algorithm. Motivated by the success of deep learning (DL), researchers have begun to leverage training data and enhanced computing capabilities in recent years to produce high-fidelity reconstructed images. Nonetheless, much of the academic research in DL algorithms for CT has focused primarily on the two-dimensional setting (with simplified forward operators and noise model) for proofs-of-concept, and a comprehensive benchmarking of various classical and data-driven CT reconstruction approaches has not beenundertaken. The key objective of our CT reconstruction grand challenge was to promote methodological advancements for both classical and DL-based approaches for clinical CT with a reasonably accurately simulated 3D CT forward operator and noise model. We have utilized the publicly available LIDC-IDRI dataset and simulated sinograms and FDK images corresponding to two dose levels (clinical- and low-dose, constituting two tracks of the challenge) starting from the normal-dose images as the ground truth. In this paper, we summarize the motivation, context, and results of our challenge, and highlight the future research directions in DL for clinical CT.

X-ray tomography↗
DOE OSTI2025

Q1-2025 Solar Photovoltaic System Cost Benchmarks

The U.S. Department of Energy’s solar office and its national laboratory partners analyze cost data for U.S. solar photovoltaic systems to develop cost benchmarks to measure progress toward goals and guide research and development programs. The cost data is available in an Excel spreadsheet model that can be downloaded here.

14 SOLAR ENERGY↗
DOE OSTI2026

Bridging Cloud and Edge Computing at NREL Using CONNECT: Cloud Optimized Networking for Next-Gen Edge Computing Technologies [Slides]

CONNECT is an innovative on-premise hardware and software solution that integrates edge and cloud computing infrastructure at NREL. Built on the AWS Greengrass middleware and leveraging the MQTT protocol, CONNECT enables real-time data streaming from IoT devices and gateways to both cloud and local services, empowering researchers to rapidly capture, analyze, and act upon edge-generated data while leveraging cloud capabilities. The platform addresses research infrastructure challenges by providing a pre-approved platform which is already configured with the correct networking and cybersecurity baselines thus eliminating procurement delays and enabling on-demand availability. CONNECT's hybrid architecture efficiently manages burstable workloads, allowing research teams to dynamically scale computational capacity, handle peak data loads, and reduce operational bottlenecks. Advanced capabilities include built-in GPU support for executing machine learning models which enables low-latency inference at the edge from models trained in the cloud. This architecture supports real-time analytics and filtering, providing a mechanism to allow only transmitting and processing high-value data. Cloud-based configuration management permits engineers to manage on-premise systems remotely, optimizing operational efficiency. By bridging edge and cloud computing, CONNECT provides NREL researchers with a flexible, scalable platform that accelerates scientific discovery while maintaining robust security and performance standards.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

LungMAP Portal Ecosystem: Systems-level Exploration of the Lung

An improved understanding of the human lung necessitates advanced systems models informed by an ever-increasing repertoire of molecular omics, cellular, imaging, and pathological datasets. To centralize and standardize information across broad lung research efforts we expanded the LungMAP.net website into a new gateway portal. This portal connects a broad spectrum of research networks, bulk and single-cell multi-omics data and a diverse collection of image data that span mammalian lung development, and disease. The data are standardized across species and technologies using harmonized data and metadata models that leverage recent advances including those from the Human Cell Atlas, diverse ontologies, and the LungMAP CellCards initiative. To cultivate future discoveries, we have aggregated a diverse collection of single-cell atlases for multiple species (human, rhesus, mouse), to enable consistent queries across technologies, cohorts, age, disease, and drug treatment. These atlases are provided as independent and integrated queryable datasets, with an emphasis on dynamic visualization, figure generation, re-analysis, cell-type curation, and automated reference-based classification of user-provided single-cell genomics datasets (Azimuth). Finally, as this resource grows, we intend to increase the breadth of available interactive interfaces, supported data types, data portals and datasets from LungMAP and external research efforts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

GaAs X-Ray Diodes (LDRD Annual Report FY2019)

Ongoing work at the Z Machine on the development of warm x-ray sources in support of national security missions requires fast x-ray diagnostics with sensitivities significantly higher than what is commercially available. Our team represents a collaborative effort between MESA and Z to fabricate GaAs x-ray detectors that meet this need. The delivered detectors have now been fielded in several Z shot series and are providing hard x-ray data to physicists at Z. In addition to improved time response and hard x-ray sensitivity compared to commercial detectors, the devices fabricated at MESA show much more consistent device-to-device signal levels. This improved repeatability gives researchers at Z new quantitative data for source development efforts.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Thermal modeling of directed energy deposition additive manufacturing using graph theory

Purpose: The purpose of this paper is to develop, apply and validate a mesh-free graph theory–based approach for rapid thermal modeling of the directed energy deposition (DED) additive manufacturing (AM) process. Design/methodology/approach: Here, the authors develop a novel mesh-free graph theory–based approach to predict the thermal history of the DED process. Subsequently, the authors validated the graph theory predicted temperature trends using experimental temperature data for DED of titanium alloy parts (Ti-6Al-4V). Temperature trends were tracked by embedding thermocouples in the substrate. The DED process was simulated using the graph theory approach, and the thermal history predictions were validated based on the data from the thermocouples. Findings: The temperature trends predicted by the graph theory approach have mean absolute percentage error of approximately 11% and root mean square error of 23°C when compared to the experimental data. Moreover, the graph theory simulation was obtained within 4 min using desktop computing resources, which is less than the build time of 25 min. By comparison, a finite element–based model required 136 min to converge to similar level of error. Research limitations/implications: This study uses data from fixed thermocouples when printing thin-wall DED parts. In the future, the authors will incorporate infrared thermal camera data from large parts. Practical implications: The DED process is particularly valuable for near-net shape manufacturing, repair and remanufacturing applications. However, DED parts are often afflicted with flaws, such as cracking and distortion. In DED, flaw formation is largely governed by the intensity and spatial distribution of heat in the part during the process, often referred to as the thermal history. Accordingly, fast and accurate thermal models to predict the thermal history are necessary to understand and preclude flaw formation. Originality/value: This paper presents a new mesh-free computational thermal modeling approach based on graph theory (network science) and applies it to DED. The approach eschews the tedious and computationally demanding meshing aspect of finite element modeling and allows rapid simulation of the thermal history in additive manufacturing. Although the graph theory has been applied to thermal modeling of laser powder bed fusion (LPBF), there are distinct phenomenological differences between DED and LPBF that necessitate substantial modifications to the graph theory approach.

42 ENGINEERING↗
DOE OSTI2025

PubChemLite Plus Collision Cross Section (CCS) Values for Enhanced Interpretation of Nontarget Environmental Data

Finding relevant chemicals in the vast (known) chemical space is a major challenge for environmental and exposomics studies leveraging nontarget high resolution mass spectrometry (NT-HRMS) methods. Chemical databases now contain hundreds of millions of chemicals, yet many are not relevant. This article details an extensive collaborative, open science effort to provide a dynamic collection of chemicals for environmental, metabolomics, and exposomics research, along with supporting information about their relevance to assist researchers in the interpretation of candidate hits. The PubChemLite for Exposomics collection is compiled from ten annotation categories within PubChem, enhanced with patent, literature and annotation counts, predicted partition coefficient (logP) values, as well as predicted collision cross section (CCS) values using CCSbase. Monthly versions are archived on Zenodo under a CC-BY license, supporting reproducible research, and a new interface has been developed, including historical trends of patent and literature data, for researchers to browse the collection. This article details how PubChemLite can support researchers in environmental and exposomics studies, describes efforts to increase the availability of experimental CCS values, and explores known limitations and potential for future developments. The data and code behind these efforts are openly available.

PubChem↗