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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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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Simultaneous measurement of organic scintillator response to carbon and proton recoils

Organic scintillators are widely used for neutron detection in both basic nuclear physics and applications. While the proton light yield of organic scintillators has been extensively studied, measurements of the light yield from neutron interactions with carbon nuclei are scarce. Demonstrate a new approach for the simultaneous measurement of the proton and carbon light yield of organic scintillators. Provide new carbon light yield data for the EJ-309 liquid and EJ-204 plastic organic scintillators. A 33-MeV 2 H + beam from the 88-Inch Cyclotron at Lawrence Berkeley National Laboratory was impinged upon a 3-mm-thick Be target to produce a high-flux, broad-spectrum neutron beam. The double time-of-flight technique was extended to simultaneously measure the proton and carbon light yields of the organic scintillators, wherein the light output associated with the recoil particle was determined using np and nC elastic scattering kinematics. The proton and carbon light yield relations of the EJ-309 liquid and EJ-204 plastic organic scintillators were measured over a recoil energy range of approximately 0.3 to 1 MeV and 2 to 5 MeV, respectively, for EJ-309, and 0.2 to 0.5 MeV and 1 to 4 MeV, respectively, for EJ-204. Furthermore, these data provide new insight into the ionization quenching effect in organic scintillators and key input for simulation of the response of organic scintillators for both basic science and a broad range of applications.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Paths Forward for Nuclear Energy: Using a Nationwide Post-Stratified Hierarchical Model to Facilitate Matching of New Nuclear Technologies to Receptive Host Communities

This project was a collaboration between the University of Oklahoma (OU) and the University of Michigan (UMich). The overall objective of the project was to address a critical problem facing the siting of nuclear facilities, where there is no mechanism for incorporating public attitudes at a state and local level into decision-making. Utilizing recent advances in data modeling over local spatial scales, the research team created a state and county level map of public attitudes towards nuclear energy. Current estimates for public acceptance of nuclear technologies primarily exists on a national level, and sub-national estimates of public opinion about reactor siting, research facilities, transport routes, or storage and disposal facilities are very scarce. Model results from this project provide a systematic basis for technology developers to evaluate public acceptance across alternative siting options as a critical component for weighing potential benefit-cost tradeoffs for alternative paths to deploy new nuclear facilities. The project utilized data from the largest database of nuclear attitudes in the US, coupled with Census and elections-related data as well as data on social vulnerability and proximity to current nuclear facilities. In addition to modeling public support, the team also developed an interactive map to showcase results, using an online tool called Public and Local Attitudes about Nuclear Energy Technologies (PLANET). The tool allows technology developers and stakeholders to interact with the model results and better understand variation in support for nuclear facility siting on a state and county level. The interactive nature of the tool also allows users to analyze results in a targeted way, by comparing levels of support across different states and/or counties as desired.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRSDate not supplied

Mapping National Forest Aboveground Biomass in Mexico By Integrating GEDI and Landsat Times Series Data

Mexico is one of the countries with great potential for the UN's Reducing Emissions from Deforestation and Forest Degradation (REDD+) program, a key nature-based solution for the forest sector. To monitor carbon stock changes, there is a growing demand for unbiased Monitoring Reporting Verification (MRV) systems to facilitate effective forest management and climate change mitigation strategies. Remote sensing-based national aboveground biomass density (AGBD) estimation over Mexico is scarce and often limited to one-time static mapping, leading to spatiotemporal inconsistency in inputs. As an effort under NASA's Carbon Monitoring System (CMS) program, we have developed a remote sensing-based approach to create consistent historical AGBD maps of Mexico using multi-stream remote sensing data, including spaceborne lidar GEDI and long-term Landsat time series, as well as topographic information. We employ the continuous change detection and classification (CCDC) algorithm for temporal modeling of Landsat surface reflectance, followed by the inference of forest AGBD using a random forest machine learning algorithm with the temporal information of land surface dynamics extracted by the CCDC as input. GEDI provides unprecedented forest structure and AGBD sampling datasets for model training and validation practices. In this presentation, we share the progress made in developing a spatially explicit mapping of historical AGBD changes associated with land surface changes and post-disturbance landscapes.

Taejin Park↗
DOE OSTI2020

Evidence of Alpha Radiolysis in the Formation of a Californium Nitrate Complex

Well-characterized complexes of transplutonium elements are scarce because of the experimental challenges of working with these elements and the rarity of the isotopes. This leads to a lack of structural and spectroscopic data needed to understand the nature of chemical bonds in these compounds. In this work, the synthesis of Cf(DOPO q ) 2 (NO 3 )(py) (DOPO q =2,4,6,8-tetra-tert-butyl-1-oxo-1H-phenoxazin-9-olate; py=pyridine) is reported, in which the nitrate anion is hypothesized to form through the α-radiolysis-induced reaction of pyridine and/or the ligand. Here, computational analysis of the electronic structure of the complex reveals that the Cf III –ligand interactions are largely ionic.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRSDate not supplied

Climate Fingerprinting Sounder Product (ClimFiSP) Skin Temperature Trends Analysis

Climate fingerprinting Sounder Product (ClimFiSP) has been developed at NASA Langley Research Center which includes daily skin temperature, surface emissivity, air temperature, H2O, trace gases, and cloud properties on 0.5x0.5 grid. Those properties are derived from IR hyper-spectral radiance measured by AIRS on Aqua and CrIS on SNPP and JPSS series. Global skin temperature trends have been derived using more than 20 years of monthly mean skin temperature data from ClimFiSP. The ClimFiSP algorithm use the spectral fingerprinting methodology that allows a low latency procession of more than two decades long satellite data record. The computational cost can be reduced by more than two orders of magnitude as compared with traditional Level-Level2-Level3 retrieval algorithms. In this presentation the global skin temperature trend from ClimFiSP will be compared with skin temperature trend derived from CLIMCAPS, ERA5, GISTEMP, HadCRUT5, and IASI data products, and the results show that pattern of ClimFiSP global skin temperature trends overall matches well with other datasets. The zonally averaged skin temperature anomaly will also be validated using those datasets. It is expected that ClimFiSP surface temperature data can serve as an important complement for surface-based estimates, especially in the regions where the spatial coverage of the surface-based observations is scarce.

Liqiao Lei↗
DOE OSTI2025

Text Mining for Process–Structure–Properties Relationships in Metals

With the advent of large language models (LLMs), the vast unstructured text within millions of academic papers is increasingly accessible for materials discovery—although significant challenges remain. While LLMs offer promising few- and zero-shot learning capabilities, particularly valuable in the materials domain where expert annotations are scarce, general-purpose LLMs often fail to address key materials-specific queries without further adaptation. To bridge this gap, fine-tuning LLMs on human-labeled data is essential for effective structured knowledge extraction (Liu in The Importance of Human-Labeled Data in the Era of LLMs, 2023). Here, in this study, we introduce a novel annotation schema designed to extract generic process–structure–properties relationships from scientific literature. We demonstrate the utility of this approach using a dataset of 128 abstracts, with annotations drawn from two distinct domains: high-temperature materials (Domain I) and uncertainty quantification in simulating materials microstructure (Domain II). Initially, we developed a conditional random field (CRF) model based on MatBERT—a domain-specific BERT variant—and evaluated its performance on Domain I. Subsequently, we compared this model with a fine-tuned LLM (GPT-4o from OpenAI) under identical conditions. Our results indicate that fine-tuning LLMs can significantly improve entity extraction performance over the BERT-CRF baseline on Domain I. However, when additional examples from Domain II were incorporated, the performance of the BERT-CRF model became comparable to that of the GPT-4o model. These findings underscore the potential of our schema for structured knowledge extraction and highlight the complementary strengths of both modeling approaches.

Materials science↗
DOE OSTI2025

Self-Supervised T-GCN for Detection of Disturbance and Propagation in Power Grid

Urban power systems increasingly rely on dense sensing to monitor grid reliability, yet disturbance labels are scarce and events are rare. We present a self-supervised spatio-temporal method that detects, localizes, and characterizes grid frequency disturbances across urban areas using only unlabeled data. Our approach trains a tiny Temporal Graph Convolutional Network (T-GCN) to forecast per-site frequency residuals (deviation from 60 Hz). The sensor graph is constructed directly from signals using pre-event Pearson correlation with a cross-correlation lag penalty without geocoding. At inference, node-level anomalies are the model's forecast errors; region-level alarms arise from connected components of high-score nodes. We estimate disturbance propagation by computing per-node arrival times (first persistent exceedance), then fit a planar or time-of-arrival model to obtain direction, speed, and an epicenter proxy. With only three real events collected at decisecond resolution across U.S. cities, we evaluate the T-GCN and report time-to-detect, footprint size, and propagation consistency. We further show that short-window embeddings from the T-GCN's hidden states enable few-shot event-vs-background recognition via a simple prototypical classifier. Despite minimal data and no labels, our system yields fast, spatially coherent detection and interpretable propagation maps, offering a practical, lightweight pathway to city-scale grid resilience analytics.

Niu, Haoran [ORNL] (ORCID:0000000155228297)↗
DOE OSTI2024

Chemistry Informed Machine Learning-Based Heat Capacity Prediction of Solid Mixed Oxides

Knowing heat capacity is crucial for modeling temperature changes with the absorption and release of heat and for calculating the thermal energy storage capacity of oxide mixtures with energy applications. The current prediction methods (ab initio simulations, computational thermodynamics, and the Neumann–Kopp rule) are computationally expensive, not fully generalizable, or inaccurate. Machine learning has the potential of being fast, accurate, and generalizable, but it has been scarcely used to predict mixture properties, particularly for mixed oxides. Here, we demonstrate a method for the generalizable prediction of heat capacity of solid oxide pseudobinary mixtures using heat capacity data obtained from computational thermodynamics and descriptors from ab initio databases. Further, models trained through this workflow achieved an error (mean absolute error of 0.43 J mol –1 K –1 ) lower than the uncertainty in differential scanning calorimetry measurements, and the workflow can be extended to predict other properties derived from the Gibbs free energy and for higher-order oxide mixtures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Subdecadal Holocene Warm-Season Temperature Variability in Central Europe Recorded by Biochemical Varves

Paleoclimate data provide important information about the character of natural climate variability. However, records with sufficient length and resolution to resolve high-frequency (decadal-scale) variability across the Holocene are scarce. We present a 10,800-year reconstruction of spring and summer temperature at three-year resolution based on biochemical varves from Lake $\dot{Z}abi$$\acute{n}skie$, Poland. The reconstruction is based on Ca/Ti ratio, which are significantly correlated with instrumental spring and summer temperature spanning 240 years. Major climate events of the Holocene period are represented in the reconstruction, including the Holocene Thermal Maximum, 8.2 ka Event, Medieval Climate Anomaly, and Little Ice Age. A low-frequency 8,000-year decreasing trend in warm-season temperatures is driven by declining summer insolation. Temperature variability is highest during the early Holocene, likely related to warmer and drier conditions. The rate of warming during the past 90 years is extremely unusual, if not unprecedented for the Holocene, based on our reconstruction.

58 GEOSCIENCES↗
DOE OSTI2020

Implementation of early psychosis services in Latin America: A scoping review

Abstract Aim The evidence of the effectiveness and cost‐effectiveness of early intervention for psychosis (EIP) services has motivated their implementation worldwide. However, complex interventions of such EIP services require local adaptations to successfully match population needs and cultural differences. Latin America is a heterogenous region where EIP services are progressively being adopted. Our aim is to map such initiatives in the region with a focus on implementation outcomes. Methods A scoping review following the Preferred Reporting Items for Systematic review and Meta‐Analysis extension for Scoping Reviews guidelines was conducted. International and regional databases were searched for publications describing EIP programmes in the region. Besides mapping the services, we described implementation outcomes based on the Standards for Reporting Implementation Studies Checklist. Results Ten articles describing seven EIP initiatives from the region were found. Four countries were represented: Argentina, Brazil, Chile and Mexico. The implementation outcomes reporting was heterogenous, although it was possible to ascertain EIP services are feasible and adequate for the region's context. Also, there is some evidence of effectiveness in terms of reducing hospitalizations and improving symptoms. Information about fidelity measures was scarce and there was no information about costs or cost‐effectiveness. Conclusions Only a small proportion of Latin American countries have adopted EIP services. Although these programmes seem to be feasible and effective, data on other implementation outcomes, such as fidelity, cultural appropriateness, cost‐effectiveness and affordability are not available. This might in part explain why this effective approach has not been yet scaled‐up at nationwide levels.

Aceituno, David↗
DOE OSTI2020

A Remote Sensing Technique to Upscale Methane Emission Flux in a Subtropical Peatland

Abstract Quantification of methane (CH 4 ) gas emission from peat is critical to understand CH 4 budget from natural wetlands under a climate warming scenario. Previous studies have focused on prediction and mapping of CH 4 emission flux using process‐based models, while application of statistical‐empirical models for upscaling spatially sparse in situ measurements is scarce. In this study, we developed an empirical remote sensing upscaling approach to estimate CH 4 emission flux in the Everglades using limited in situ point‐based CH 4 emission flux measurements and Landsat data during 2013–2018. We spatially and temporally linked in situ data with Landsat surface reflectance based on temporally composite data sets and developed an object‐based machine learning framework to model and map CH 4 emission flux. An ensemble analysis of two machine learning models, k ‐Nearest Neighbor ( k ‐NN) and Support Vector Machine (SVM), shows that the upscaling approach is promising for predicting CH 4 emission flux with a R 2 of 0.65 and 0.87 based on a fivefold cross‐validation for a dry season and wet season estimation, respectively. We generated emission flux map products that successfully revealed the spatial and temporal heterogeneity of CH 4 emission within the dominant freshwater marsh ecosystem in the Everglades. We conclude that Landsat is promising for upscaling and monitoring CH 4 emission flux and reducing the uncertainty in emission estimates from wetlands.

Zhang, Caiyun↗
NASA NTRS2023

Eastern Indigo Snake (Drymarchon couperi) Shelter Site Use in Peninsular Florida and Implications for Habitat Conservation

Shelters are critical for many species as protection from predators and extreme temperatures. Successful conservation of reptiles requires understanding both shelter site requirements and availability. The Eastern Indigo Snake (EIS) is endemic to the southeastern United States and is federally listed. Recovery has focused on maximizing unfragmented landscapes, with less attention on fine-scale features such as shelter sites. In the northern EIS range, Gopher Tortoise (Gopherus polyphemus) burrows are used extensively for shelter. Although EIS in peninsular Florida often shelter in tortoise burrows, they also use other shelters where tortoise burrows are scarce or absent. Solely focusing EIS survey and management efforts where Gopher Tortoises are present may overlook occupied habitats and misallocate resources. We investigated the importance of different shelter sites in central Florida using data from radio-tracked EIS. We modeled the use of shelter categories as a function of sex, season, and habitat using Bayesian multinomial generalized linear models. Results showed that EIS in peninsular Florida used Gopher Tortoise burrows across all seasons and habitats. Tortoise burrow use was highest in xeric habitats and lowest in mesic habitats where burrows are most and least abundant, respectively. There was less variability in shelter site use in disturbed habitats and flatwoods. Cool season tortoise burrow use across sexes and habitats in our study was much lower than in southern Georgia. Our results indicate that EIS are less dependent on Gopher Tortoise burrows in peninsular Florida and that suitable habitats with few or no tortoise burrows could still provide conservation value for EIS.

Bayesian hierarchical modeling↗
NASA NTRS1994

Turbulent Heat Transfer Characteristics in the Shear Layer of a Separated Flow

Experiments were performed to study the evolution of the heat transfer structure in a separated free shear layer region of an incompressible separated turbulent boundary layer flow behind a backward-facing step. While there is an abundance of velocity field measurements of separated flows, heat transfer measurements are rather scarce, thus limiting assessment of the heat transfer physics and its accurate modeling. The purpose of the paper is twofold: to improve an understanding of effects of flow separation on heat transfer characteristics, and to provide data for turbulence modeling and computation. The boundary layer upstream of the step was turbulent and fully developed. A constant temperature surface boundary condition was imposed upstream and downstream of the step for the heat transfer study. An internal mixing-layer like flow forms and grows from the step lip within the original boundary layer. The turbulent structure of the flow evolving downstream, however, does not switch immediately to that of a mixing layer over the entire shear layer thickness. Measurements of mean and fluctuating velocity and temperature fields indicate that the internal layer spreads gradually in the transverse direction while the outer part of the original boundary layer is effectively unperturbed. The results in this paper have not been previously reported.

Jovic, S.↗
NASA NTRS2023

A Meta-Analytic Approach to Investigating the Relationship Between Human-Automation Trust and Attention Allocation

Trust and attention allocation are pivotal determinants in human-automation interaction. However, there are scarce empirical findings regarding the relationship between trust and attention allocation. Observations from our previous work suggested there may be a negative correlation between trust in automation and eye movement towards automation, though no formal analysis of these data had been conducted to quantify this relationship. The present meta-analysis examined the relationship between three dimensions of trust in automation (performance, process, and purpose) and visual attention allocation to the automation. Specifically, we applied Cumming’s (2014) meta-analysis technique to combine evidence across three experiments. Results indicated a negative correlation between trust in automation and visual sampling of the automated system monitoring task for performance-based trust, but not for process- or purpose-based trust. These findings suggest that operators scanned the automation’s behavior less frequently when indicating higher performance-based trust towards the automation.

Human-Autonomy Teaming (HAT)↗
DOE OSTI2026

A spatiotemporally explicit and scalable indicator of intact lands across the conterminous United States, 1986–2023

Globally, ecologically intact areas are increasingly scarce. Agricultural expansion into previously uncultivated areas drives the loss of intact lands that might otherwise exhibit high levels of ecological integrity. Thus, the absence of cultivation can be an indicator of intact lands as measured from remote sensing data and thematic maps. Our objective for this study was to develop and compare tractable approaches based on remotely sensed satellite data to map spatial patterns of potentially intact lands across the conterminous U.S. (CONUS). Using annual cultivation probabilities derived from satellite observations, we classified and mapped potentially intact lands across CONUS from 1986 to 2023 at 30 m resolution. We created three maps, first by applying a constant cultivation probability threshold across CONUS, second by varying the threshold state-by-state to maximize state-level overall accuracies, and third by equalizing the state-level user's and producer's accuracies to minimize classification bias. Validation against 800,000+ independent ground samples resulted in CONUS-level overall accuracies ≥85% for the roughly 660 million ha of potentially intact land. Map accuracy varied with the proportion of potentially intact lands across regions, with the Pacific-Mountain and Great Plains regions exhibiting the highest accuracies, while Eastern CONUS exhibited a greater mix of potentially intact and non-intact lands and more moderate map accuracies. These novel maps and approaches can be adapted to different spatiotemporal extents to support conservation and production decisions ranging from species and ecosystems protection to reducing land conversion and climate mitigation.

agriculture↗
DOE OSTI2025

The search for high-entropy fuel-cell catalysts using disorder descriptors

The transition to a hydrogen economy depends on efficient, affordable catalysts for fuel cells. Platinum—the industry standard for fuel-cell electrodes—is costly and scarce, highlighting the need for practical alternatives. High-entropy alloys offer vast compositional diversity and tunable properties that can mitigate these issues, yet their chemical complexity and configurational disorder have hindered rational discovery. Here, we introduce a data-driven framework that couples machine learning with first-principles disorder descriptors—including the entropy forming ability, disordered enthalpy-entropy descriptor, and electronic-structure similarity metrics to platinum—to predict alloy synthesizability and catalytic performance. These descriptors are applied for the first time in the context of fuel-cell catalyst discovery. The workflow rapidly screens more than 20 000 compositions and identifies several platinum-free candidates that are economically viable, readily scalable, and exhibit promising predicted activity. These results demonstrate that disorder descriptors are reliably predicted by machine learning models and can be effectively integrated into materials-discovery pipelines, accelerating innovation across complex compositional spaces.

fuel-cell catalysts↗
NASA NTRS1999

Experimental Results of LightSAR Mision Planning Using a Market-Based System

The allocation of scarce spacecraft resources to multiple users has always been a difficult process. This difficulty arises from the fact that there are never enough resources to meet the stated requirements of the scientific investigators who compete to acquire their desired data sets.

LightSAR Mission Planning Market-based systems↗
DOE OSTI2025

Scattering insights into shear-induced scission of rod-like micelles

Understanding the scission of rod-like micelles under mechanical forces is crucial for optimizing their stability and behavior in industrial applications. This study investigates how micelle length, flexibility, and external forces interact, offering insights into the design of micellar systems in processes influenced by mechanical stress. Although significant, direct experimental observations of flow-induced micellar scission using scattering techniques remain scarce. Small angle neutron scattering (SANS) is used to explore the shear response of aqueous cetyltrimethylammonium bromide (CTAB) solutions with sodium nitrate. Rheological tests show shear thinning with no shear banding, ensuring a uniform flow field for reliable interpretation of scattering data. As shear rate increases, the scattering spectra show angular distortion, which is analyzed using spherical harmonic decomposition to characterize flow-induced scission and micelle orientation under shear. Two analysis steps are used: a model-independent spectral eigendecomposition reveals a decrease in micellar length, while regression analysis quantifies the evolution of the length distribution and mean length with shear rate. Additionally, micelle alignment increases with shear, quantified by the orientational distribution function. In conclusion, these findings provide experimental evidence for flow-induced alignment and scission, offering a new framework for understanding shear-induced phenomena in micellar systems.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗