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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 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

NuSTAR SPECTROSCOPY OF MULTI-COMPONENT X-RAY REFLECTION FROM NGC 1068

We report on high-energy X-ray observations of the Compton-thick Seyfert 2 galaxy NGC 1068 with NuSTAR, which provide the best constraints to date on its >10 keV spectral shape. The NuSTAR data are consistent with those from past and current instruments to within cross-calibration uncertainties, and we find no strong continuum or line variability over the past two decades, which is in line with its X-ray classification as a reflection-dominated Compton-thick active galactic nucleus. The combined NuSTAR, Chandra, XMM-Newton, and Swift BAT spectral data set offers new insights into the complex secondary emission seen instead of the completely obscured transmitted nuclear continuum. The critical combination of the high signal-to-noise NuSTAR data and the decomposition of the nuclear and extranuclear emission with Chandra allow us to break several model degeneracies and greatly aid physical interpretation. When modeled as a monolithic (i.e., a single N{sub H}) reflector, none of the common Compton reflection models are able to match the neutral fluorescence lines and broad spectral shape of the Compton reflection hump without requiring unrealistic physical parameters (e.g., large Fe overabundances, inconsistent viewing angles, or poor fits to the spatially resolved spectra). A multi-component reflector with three distinct column densities (e.g., with best-fit values of N{sub H} of 1.4 × 10{sup 23}, 5.0 × 10{sup 24}, and 10{sup 25} cm{sup −2}) provides a more reasonable fit to the spectral lines and Compton hump, with near-solar Fe abundances. In this model, the higher N{sub H} component provides the bulk of the flux to the Compton hump, while the lower N{sub H} component produces much of the line emission, effectively decoupling two key features of Compton reflection. We find that ≈30% of the neutral Fe Kα line flux arises from >2″ (≈140 pc) and is clearly extended, implying that a significant fraction (and perhaps most) of the <10 keV reflected component arises from regions well outside a parsec-scale torus. These results likely have ramifications for the interpretation of Compton-thick spectra from observations with poorer signal-to-noise and/or more distant objects.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

CONSTRAINTS ON THE NEUTRON STAR AND INNER ACCRETION FLOW IN SERPENS X-1 USING NuSTAR

We report on an observation of the neutron star low-mass X-ray binary Serpens X-1, made with NuSTAR. The extraordinary sensitivity afforded by NuSTAR facilitated the detection of a clear, robust, relativistic Fe K emission line from the inner disk. A relativistic profile is required over a single Gaussian line from any charge state of Fe at the 5σ level of confidence, and any two Gaussians of equal width at the same confidence. The Compton back-scattering ''hump'' peaking in the 10-20 keV band is detected for the first time in a neutron star X-ray binary. Fits with relativistically blurred disk reflection models suggest that the disk likely extends close to the innermost stable circular orbit (ISCO) or stellar surface. The best-fit blurred reflection models constrain the gravitational redshift from the stellar surface to be z {sub NS} ≥ 0.16. The data are broadly compatible with the disk extending to the ISCO; in that case, z {sub NS} ≥ 0.22 and R {sub NS} ≤ 12.6 km (assuming M {sub NS} = 1.4 M {sub ☉} and a = 0, where a = cJ/GM {sup 2}). If the star is as large or larger than its ISCO, or if the effective reflecting disk leaks across the ISCO to the surface, the redshift constraints become measurements. We discuss our results in the context of efforts to measure fundamental properties of neutron stars, and models for accretion onto compact objects.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Band gap and refractive index tunability in thallium based layered mixed crystals

Compositional variation of the band gap energy and refractive index of TlMeX{sub 2}-type (Me = Ga or In and X = S or Se) layered mixed crystals have been studied by the transmission and reflection measurements in the wavelength range of 400–1100 nm. The analysis of absorption data of TlGa{sub 1-x}In{sub x}Se{sub 2}, TlGa(S{sub 1−x}Se{sub x}){sub 2}, TlGa{sub 1−x}In{sub x}S{sub 2}, and TlIn(Se{sub 1−x}S{sub x}){sub 2} mixed crystals revealed the presence of both optical indirect and direct transitions. It was found that the energy band gaps of mixed crystals decrease at the replacing of gallium atoms by indium and of sulfur atoms by selenium ones. Through the similar replacing of atoms (smaller atoms by larger ones) in the studied mixed crystals, the refractive index shows the quite opposite behavior.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Unique contributions of chlorophyll and nitrogen to predict crop photosynthetic capacity from leaf spectroscopy

The photosynthetic capacity or the CO 2 -saturated photosynthetic rate (Vmax), chlorophyll, and nitrogen are closely linked leaf traits that determine C 4 crop photosynthesis and yield. Accurate, timely, rapid, and non-destructive approaches to predict leaf photosynthetic traits from hyperspectral reflectance are urgently needed for high-throughput crop monitoring to ensure food and bioenergy security. Therefore, this study thoroughly evaluated the state-of-the-art physically based radiative transfer models (RTMs), data-driven partial least squares regression (PLSR), and generalized PLSR (gPLSR) models to estimate leaf traits from leaf-clip hyperspectral reflectance, which was collected from maize (Zea mays L.) bioenergy plots with diverse genotypes, growth stages, treatments with nitrogen fertilizers, and ozone stresses in three growing seasons. The results show that leaf RTMs considering bidirectional effects can give accurate estimates of chlorophyll content (Pearson correlation r=0.95), while gPLSR enabled retrieval of leaf nitrogen concentration (r=0.85). Using PLSR with field measurements for training, the cross-validation indicates that V max can be well predicted from spectra (r=0.81). Here, the integration of chlorophyll content (strongly related to visible spectra) and nitrogen concentration (linked to shortwave infrared signals) can provide better predictions of V max (r=0.71) than only using either chlorophyll or nitrogen individually. This study highlights that leaf chlorophyll content and nitrogen concentration have key and unique contributions to V max prediction.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Assessing the Effectiveness of Large-Scale Environmental Restoration: Challenges and Opportunities

A recent National Academies consensus report addresses monitoring and assessment of cumulative effects of large-scale and multiple restoration projects within the context of long-term environmental change. Fines and penalties from the Deepwater Horizon oil spill in the Gulf of Mexico (GoM) have supported hundreds of restoration projects at spatial scales not often possible in the past. Here, in this paper, the report committee members and staff provide personal reflections from our time working on the study. We found that gaps in data collection, issues with data accessibility, and a lack of synthesis and analysis are hindering the ability to answer a basic question: What are the impacts of these many restoration efforts on improving ecosystem health and productivity in the GoM at the regional and Gulf wide scale? Restoration efforts are occurring in environments where many trends are changing and exhibiting higher variability than in the past, suggesting that previously successful restoration practices may no longer be adequate to compensate for the effects of environmental changes and variability. Our proposed approach to these challenges includes employing emerging monitoring technologies; using conceptual models; devising an adaptive management framework; rethinking restoration outcome goals; assessing cumulative effects; and undertaking rigorous synthesis and analysis of existing information on long-term environmental trends and restoration efforts. Restoration scientists and practitioners working in the GoM have an unprecedented opportunity to demonstrate large-scale environmental recovery if advances in monitoring, synthesis, assessment, and action are taken quickly. We are cautiously optimistic that, with mid-course adjustments, continued progress toward large-scale environmental recovery is possible.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Agentic traffic intelligence: Augmented human-in-the-loop scenario generation for microscopic traffic simulation

Traditional microscopic traffic simulation generation often relies on static datasets and manual design, limiting its ability to simulate complex conditions easily. This paper presents a novel framework, Agentic Traffic Intelligence, which combines human approval large language models (LLMs), the Real-Twin tool, and multi-agent systems to perform realistic microscopic traffic simulation scenario generation. The proposed framework incorporates human-in-the-loop (HIL) control, retrieval-augmented generation (RAG), and multi-agent control mechanisms. HIL mechanisms are used to guide multiple LLMs focused on attributes for microscopic simulation generation and to improve the interpretability and transparency of LLM execution for users. RAG enhances context extraction by dynamically integrating external knowledge sources for traffic scenario generation foundations. A multi-agent architecture with supervisory control coordinates the interaction of simulation components, including traffic simulators, control logic, and calibration tools. This enables the synthesis of simulation-ready scenarios that reflect dynamic demand profiles and behavior controls. Furthermore, the framework fuses multisource traffic data with unstructured context and supports iterative refinement through interactive user feedback. Validated through microscopic simulation using Simulation of Urban Mobility, the generated scenarios demonstrate high-fidelity network generation with inflow and turn movement and behavioral calibration, offering a robust and efficient tool for stress-testing and optimizing urban mobility systems.

Hierarchical multi-agent control↗
DOE OSTI2022

A method and tool to determine the colorimetric and photobiological properties of light transmitted through glass and other optical materials

Here we describe a framework and provides a computational tool to characterize the color quality and biological potential of light that is transmitted through glass and other optical materials. The IES TM-30 framework and Excel computation tool were adapted to evaluate color quality, with measures from CIE S 026, UL 24480, and the WELL Building Standard v2 added to evaluate biological potential. The user selects a pre-transmittance spectral power distribution (SPD), such as for a CIE D-Series Illuminant, Planckian radiation, electric lamp, or measurement of daylight at a building site. The tool allows a user to populate a database with spectral data for glazing or other optical materials, comprising spectral transmission and the spectral reflectance of both sides. The user creates a unit with one, two, or three panes of glazing or other optical materials, and the tool calculates the composite spectral transmittance accounting for reflections between materials. The tool computes the transmitted SPD, then determines colorimetric and photobiological outputs using the transmitted SPD as the test source. Glass, window, and skylight manufacturers can employ the tool to optimize glazing spectral transmission to achieve intentional colorimetric and photobiological performance with transmitted light. Electric lighting manufacturers, designers, and researchers can use the tool to evaluate the impact of glazing units and other optical materials on the color quality and biological potential of transmitted light.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Generating realistic building electrical load profiles through the Generative Adversarial Network (GAN)

Building electrical load profiles can improve understanding of building energy efficiency, demand flexibility, and building-grid interactions. Current approaches to generating load profiles are time-consuming and not capable of reflecting the dynamic and stochastic behaviors of real buildings; some approaches also trigger data privacy concerns. In this study, we proposed a novel approach for generating realistic electrical load profiles of buildings through the Generative Adversarial Network (GAN), a machine learning technique that is capable of revealing an unknown probability distribution purely from data. The proposed approach has three main steps: (1) normalizing the daily 24-hour load profiles, (2) clustering the daily load profiles with the k-means algorithm, and (3) using GAN to generate daily load profiles for each cluster. The approach was tested with an open-source database – the Building Data Genome Project. We validated the proposed method by comparing the mean, standard deviation, and distribution of key parameters of the generated load profiles with those of the real ones. The KL divergence of the generated and real load profiles are within 0.3 for majority of parameters and clusters. Additionally, results showed the load profiles generated by GAN can capture not only the general trend but also the random variations of the actual electrical loads in buildings. We report the proposed GAN approach can be used to generate building electrical load profiles, verify other load profile generation models, detect changes to load profiles, and more importantly, anonymize smart meter data for sharing, to support research and applications of grid-interactive efficient buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Self-supervised and multi-fidelity learning for extended predictive soil spectroscopy

Infrared spectroscopy is a cost-effective, non-destructive, and environmentally benign technology that is increasingly recognized as an important solution for meeting the global demand for soil data. While both near-infrared (NIR) and mid-infrared (MIR) diffuse reflectance spectroscopy enable rapid estimation of soil properties, they present a significant trade-off: NIR offers superior scalability and lower operational costs, whereas MIR provides higher analytical fidelity by capturing fundamental molecular vibrations. In this study, we propose a self-supervised, multi-fidelity learning framework designed to bridge this gap. Our approach leverages large-scale MIR spectral libraries to learn a compact, transferable latent representation, into which NIR spectra are subsequently aligned for downstream prediction. The workflow consists of pretraining a latent model on a large MIR library, adapting the representation using a smaller paired NIR–MIR dataset, and evaluating generalization on an independent external test set. Across a range of chemical and physical soil properties, we found that MIR-derived embeddings improved prediction accuracy relative to baseline models that used raw MIR inputs. Predictions derived from the spectrum conversion (NIR to MIR) task did not match the performance of the original MIR spectra but were similar or superior to predictive performance of NIR-only models, suggesting the unified spectral latent space can effectively leverage the larger and more diverse MIR dataset for prediction of soil properties not well represented in current NIR libraries.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A hydrogeophysical framework to assess infiltration during a simulated ecosystem-scale flooding experiment

This study presents a framework to quantify changes in soil saturation in response to flooding caused by extreme hydrologic perturbation on coastal ecosystems at the interfaces and transition between terrestrial and aquatic systems. Subsurface heterogeneity limits the use of in situ measurements to quantify subsurface flow during flooding due to the spatial discontinuity in the measured data. While geophysical methods, including time-lapse electrical resistivity imaging (ERI), are increasingly used to monitor soil hydrological processes, their abilities to parameterize flow models have been underutilized. This study combines background ERI, ground penetrating radar (GPR), time-lapse ERI, soil characterization, and a numerical flow model developed using an Advanced Terrestrial Simulator (ATS) code to quantify the infiltration pathway and describe the hydrological dynamics during a simulated flooding experiment. We assessed the use of two conceptual models developed using [1] ERI and GPR data that described the stratigraphic distribution, and time-lapse ERI that mapped permeability contrast, and [2] information from a national soil database for capturing changes in saturation. Combining the ERI and GPR results with soil core data revealed the stratigraphic heterogeneity at the site with a silty clay layer from 1 to 2 m between an overlying loamy topsoil and an underlying saturated silty sand. This silty clay layer could restrict deep infiltration. The time-lapse ERI showed up to a 35% decrease in resistivity, which correlated with soil moisture data (R 2 value > 0.53) and revealed preferential infiltration zones used to inform the flow model. Numerical simulation results from both the geophysics- and soil database-informed models quantified changes in soil saturation with calculated soil moistures that agreed with field data. The geophysics-informed model captured more of the system’s variability, reflective of shallow subsurface heterogeneities. The framework presented will serve as a precursor for a robust ecohydrological model that can describe the impacts of extreme events induced by climate change on coastal ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

SrFe{sub 0.9}Ti{sub 0.1}O{sub 3−δ}: A cluster spin glass

Graphical abstract: - Highlights: • Prepared SrFe{sub 0.9}Ti{sub 0.1}O{sub 3−δ} is in nanocrystalline form. • Arrott plot shows absence of spontaneous magnetization which rules out the possibility of long range ferromagnetic order. • AC susceptibility data is evidence of cluster spin glass. • There is no magnetic Bragg's reflection. • By substituting Ti{sup 4+}, helical antiferromagetic SrFeO{sub 3} is transformed to a cluster spin glass. - Abstract: The magnetic ground state of nanocrystalline SrFe{sub 0.9}Ti{sub 0.1}O{sub 3−δ} system has been investigated from 2 K to 300 K using AC Susceptibilty (ACS), magnetization and neutron diffraction measurements. The cusp in the temperature dependent magnetization and dispersion below T{sub max} (the temperature at which the maximum susceptibility is observed) in real part of ac susceptibility with frequency is observed. Arrott plot shows the lack of M{sub S} (spontaneous magnetization) down to 2 K, and the non-occurrence of magnetic Bragg reflections below the T{sub max} in neutron diffraction measurements, indicates the absence of long range ferro and antiferromagnetic order. The frequency dependence of T{sub max} obeys Vögel–Fulcher model establishing the cluster spin glass behavior in this system.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Selective deuteration of an RNA:RNA complex for structural analysis using small-angle scattering

The structures of RNA:RNA complexes regulate many biological processes. Despite their importance, protein-free RNA:RNA complexes represent a tiny fraction of experimentally determined structures. Here, we describe a joint small-angle X-ray and neutron scattering (SAXS/SANS) approach to structurally interrogate conformational changes in a model RNA:RNA complex. Using SAXS, we measured the solution structures of the individual RNAs and of the overall RNA:RNA complex. With SANS, we demonstrate, as a proof of principle, that isotope labeling and contrast matching (CM) can be combined to probe the bound state structure of an RNA within a selectively deuterated RNA:RNA complex. Furthermore, we show that experimental scattering data can validate and improve predicted AlphaFold 3 RNA:RNA complex structures to reflect its solution structure. In conclusion, our work demonstrates that in silico modeling, SAXS, and CM-SANS can be used in concert to directly analyze conformational changes within RNAs when in complex, enhancing our understanding of RNA structure in functional assemblies.

HIV-1 dimerization initiation site↗
DOE OSTI2025

Stimulated Raman Scattering Microscopy: Real-Time In-Situ Physical and Chemical Characterization of Reverse Osmosis Desalination Membrane Scaling

We introduce a stimulated Raman scattering (SRS) methodology designed for rapid, real-time, and in situ monitoring of RO membrane scaling adapted for bench-scale desalination flow cells. The methodology can provide new insights into membrane scaling dynamics by offering time-resolved reflection imaging of inorganic crystal growth, coupled with chemical identification from Raman spectral data. These capabilities allow for direct local measurement of the membrane surface area covered by different scalants as well as an approximation of the scalant volume using three-dimensional, integrated Raman intensity. The 2D and 3D SRS results obtained from CaSO 4 scaling experiments are compared to and are in reasonable agreement with those provided by confocal microscopy. The real-time physical and chemical characterization capabilities presented here could be extended to study combinations of inorganic, organic, and biological fouling. Overall, the SRS methodology represents an advancement in real-time sensing of membrane fouling that offers the potential for improved operation, lower cost, and more resilient RO membrane systems for sustainable water management.

42 ENGINEERING↗
DOE OSTI2024

Dynamic Modeling and Simulation of a Subcritical Coal-Fired Power Plant under Load-Following Conditions

Dynamic models for power plants that capture realistic general process trends and effects of manipulated variables are needed to improve load-following, while minimizing carbon footprint. In this work, a dynamic modeling approach and simulation results for subcritical coal-fired power plant components are presented. These encompass simulation of the dynamics in the fireside, including the effects of fuel, air combustion, and the dynamics of the entire waterside and power generation sections. This model development enables the simulation and analysis of the important short and long-time scale dynamics of components such as heaters, evaporative loop, and power generation units. Furthermore, additional variables in the power generation section are introduced to improve model accuracy, extending the prediction capability of subcritical power plant models and opening new opportunities for research in operator training, optimization, and advanced model-based controller design that are based on these models. The change in process gain for different ramp rates associated with disturbance signals that affect process variables is also explored and a correlation developed. This provides opportunities to study disturbance rejection control implementation and adaptation for scenarios with such variations in ramp rates. The prediction capabilities of selected components are compared to data available in literature, with the obtained root mean squared error ranges that reflect the model performance and quality of predictions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Comprehensive Quantitative Analysis of Ovarian and Breast Cancer Tumor Peptidomes

Aberrant degradation of proteins is associated with many pathological states, including cancers. Mass spectrometric analysis of tumor peptidomes, the intracellular and intercellular products of protein degradation, has the potential to provide biological insights on proteolytic processing in cancer. However, attempts to use the information on these smaller protein degradation products from tumors for biomarker discovery and cancer biology studies have been fairly limited to date, largely due to the lack of effective approaches for robust peptidomics identification and quantification, and the prevalence of confounding factors and biases associated with sample handling and processing. Herein, we have developed an effective and robust analytical platform for comprehensive analyses of tissue peptidomes, which is suitable for high throughput quantitative studies. The reproducibility and coverage of the platform, as well as the suitability of clinical ovarian tumor and patient-derived breast tumor xenograft samples with post-excision delay of up to 60 min before freezing for peptidomics analysis, have been demonstrated. Moreover, our data also show that the peptidomics profiles can effectively separate breast cancer subtypes, reflecting tumor-associated protease activities. Peptidomics complements results obtainable from conventional bottom-up proteomics, and provides insights not readily obtainable from such approaches.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2024

A Non‐Intrusive Machine Learning Framework for Debiasing Long‐Time Coarse Resolution Climate Simulations and Quantifying Rare Events Statistics

Abstract Due to the rapidly changing climate, the frequency and severity of extreme weather is expected to increase over the coming decades. As fully‐resolved climate simulations remain computationally intractable, policy makers must rely on coarse‐models to quantify risk for extremes. However, coarse models suffer from inherent bias due to the ignored “sub‐grid” scales. We propose a framework to non‐intrusively debias coarse‐resolution climate predictions using neural‐network (NN) correction operators. Previous efforts have attempted to train such operators using loss functions that match statistics. However, this approach falls short with events that have longer return period than that of the training data, since the reference statistics have not converged. Here, the scope is to formulate a learning method that allows for correction of dynamics and quantification of extreme events with longer return period than the training data. The key obstacle is the chaotic nature of the underlying dynamics. To overcome this challenge, we introduce a dynamical systems approach where the correction operator is trained using reference data and a coarse model simulation nudged toward that reference. The method is demonstrated on debiasing an under‐resolved quasi‐geostrophic model and the Energy Exascale Earth System Model (E3SM). For the former, our method enables the quantification of events that have return period two orders longer than the training data. For the latter, when trained on 8 years of ERA5 data, our approach is able to correct the coarse E3SM output to closely reflect the 36‐year ERA5 statistics for all prognostic variables and significantly reduce their spatial biases.

Barthel Sorensen, B.↗
DOE OSTI2025

Applying the FAIR Principles to computational workflows

Recent trends within computational and data sciences show an increasing recognition and adoption of computational workflows as tools for productivity and reproducibility that also democratize access to platforms and processing know-how. As digital objects to be shared, discovered, and reused, computational workflows benefit from the FAIR principles, which stand for Findable, Accessible, Interoperable, and Reusable. The Workflows Community Initiative’s FAIR Workflows Working Group (WCI-FW), a global and open community of researchers and developers working with computational workflows across disciplines and domains, has systematically addressed the application of both FAIR data and software principles to computational workflows. We present recommendations with commentary that reflects our discussions and justifies our choices and adaptations. These are offered to workflow users and authors, workflow management system developers, and providers of workflow services as guidelines for adoption and fodder for discussion. The FAIR recommendations for workflows that we propose in this paper will maximize their value as research assets and facilitate their adoption by the wider community.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Absolute calibration of the OMEGA streaked optical pyrometer for temperature measurements of compressed materials

Experiments in high-energy-density physics often use optical pyrometry to determine temperatures of dynamically compressed materials. In combination with simultaneous shock-velocity and optical-reflectivity measurements using velocity interferometry, these experiments provide accurate equation-of-state data at extreme pressures (P > 1 Mbar) and temperatures (T > 0.5 eV). This paper reports on the absolute calibration of the streaked optical pyrometer (SOP) at the Omega Laser Facility. The wavelength-dependent system response was determined by measuring the optical emission from a National Institute of Standards and Technology–traceable tungsten-filament lamp through various narrowband (40 nm-wide) filters. The integrated signal over the SOP’s ~250-nm operating range is then related to that of a blackbody radiator using the calibrated response. We present a simple closed-form equation for the brightness temperature as a function of streak-camera signal derived from this calibration. As a result, error estimates indicate that brightness temperature can be inferred to a precision of <5%.

36 MATERIALS SCIENCE↗