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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Uncovering the Materials Paradigm at the Nanoscale: The Role of Next Generation Imaging and Characterization

The behavior of many energy materials and devices is very often limited by inhomogeneously distributed nanoscale defects. This is the case throughout the entire lifecycle of the solar cell, from the distribution of elements and defects during solar cell growth as well as the charge-collection and recombination during operation, to degradation and failure mechanisms due to impurity diffusion, crack formation, and irradiation- and heat-induced cell damage. This has been known for a while in the field of crystalline silicon, but inhomogeneities are far more abundant and smaller in scale for the case of polycrystalline materials. We showed that the high penetration of hard X-rays combined with the high sensitivity to elemental distribution, structure, and spatial resolution offers a unique avenue for highly correlative studies at the nanoscale. We presented results on CdTe and Cu(In,Ga)Se2 where carrier collection is directly correlated to the compositional and structural properties of the material under a large variety of synthesis and operating conditions. The segregation of copper at the grain boundaries of both solar absorbers was discussed in detail as well as the defects impact to carrier collection efficiency. Furthermore, the kinetics of copper segregation during growth and processing was presented and discussed.

Bertoni, Mariana↗
DOE OSTI2021

Multimodal X-ray Microscopy: The Kinetics of Cu in CdTe Absorbers

The behavior of solar cells is very often limited by inhomogeneously distributed nanoscale defects. This is the case throughout the entire lifecycle of the solar cell, from the distribution of elements and defects during solar cell growth as well as the charge-collection and recombination during operation, to degradation and failure mechanisms due to impurity diffusion, crack formation, and irradiation- and heat-induced cell damage This has been known for a while in the field of crystalline silicon, but inhomogeneities are far more abundant in polycrystalline materials, and are the limiting factor in thin-film solar cells where grain sizes are often on the order of the diffusion length. We showed that the high penetration of hard X-rays combined with the high sensitivity to elemental distribution, structure, and spatial resolution offers a unique avenue for highly correlative studies at the nanoscale. We presented results on Cu-doped CdTe, where carrier collection is directly correlated to the kinetics of Cu inside the absorber and the particular Cu-phases at the ZnTe/CdTe interface. We complemented the results with modeling using PVRD-FASP and PyCDTS, where the X-ray beam induced profile can be reproduced by the equilibrium defect concentrations proposed by the solvers.

Bertoni, Mariana↗
DOE OSTI2022

An influence of manufacturing tolerances on Pin-Cell k-infinity of MOX fuel using data from the FUBILA experiment program

An influence of manufacturing tolerances (MTs) was evaluated on the pin-cell k-infinity of MOX fuel through the random sampling of CASMO5 calculations. The data from the FUBILA experiment program was used as the manufacturing parameters. For the uncertainties of element/isotope mass fractions, their covariance matrices were calculated by the generalized least square method. The total k-infinity uncertainty was 120-250 pcm (percent mille). From the breakdown of k-infinity uncertainty per materials, the MTs of fuel pellet had a dominant influence. The individual influences were also evaluated for element/isotope mass fractions, an inner/outer diameter, and a density. Those of element/isotope mass fractions were less than a few dozen pcm. Since the perturbations of fuel pellet diameter and density caused large variations to the total amount of heavy metals and effect of spatial self-shielding, they had a negative correlation with the k-infinity perturbation. On the AG3 (Al-Mg alloy) over-cladding, the inner/outer diameter perturbations had both negative and positive correlations. The negative one was due to decrease in amount of light water and the positive one was due to increase in amount of AG3. Since the effect of neutron slowing-down by light water had a dominant influence, the influence of the former negative correlation was larger than that of the latter positive correlation. It is, therefore, concluded that the consideration of the MTs that had a large influence on the effect of neutron slowing-down is important for precise quantification of the pin-cell k-infinity uncertainty of MOX fuel. (author)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

EXCLUSIVE NEUTRAL PION ELECTROPRODUCTION CROSS SECTION MEASUREMENTSWITHANEUTRALPARTICLE SPECTROMETER

Deep Virtual Compton Scattering (DVCS), the exclusive electron-proton scattering process ep ¿e'p'¿, provides access to generalized parton distributions (GPDs), which correlate information about the longitudinal momentum and transverse spatial structure of quarks inside the nucleon. Experiment E12-13-010 in Hall C at Jefferson Lab was designed to take high-precision measurements of the DVCS cross section over an extended kinematic range using the newly commissioned Neutral Particle Spectrometer (NPS). The NPS features a high-resolution electromagnetic calorimeter and a streaming data acquisition system optimized for operation at high luminosities. This thesis presents the detector and analysis work carried out to support the NPS DVCS program. In particular, it focuses on the hardware design, calibration, and performance of the calorimeter. A development of a waveform reconstruction analysis of the calorimeter signals enabled improved extraction of pulse amplitudes and times. The waveform analysis was also extended to operate in a multithreaded environment, substantially reducing processing time for large datasets. Analysis of exclusive neutral pion electroproduction events in the calorimeter gives a strong validation of the calorimeter’s performance and resolution. Together these developments establish a foundation for future analyses and extraction of the DVCS cross section and its use in constraining the GPDs.

Kerver, Mitchell [Old Dominion Univ., Norfolk, VA ↗
DOE OSTI2018

A Modeling Study of Direct and Indirect N 2 O Emissions From a Representative Catchment in the U.S. Corn Belt

Indirect nitrous oxide (N 2 O) emissions from drainage ditches and headwater streams are poorly constrained. Few studies have monitored stream N 2 O emissions and fewer modeling studies have been conducted to simulate stream N 2 O emissions. Here, we determined direct and indirect N 2 O emission modules and a corresponding calibration module for use in the Soil and Water Assessment Tool (SWAT) model, and implemented the expanded SWAT model (termed SWAT-N 2 O) to a representative fourth-stream-order catchment (210 km 2 ) and six first-order stream catchments (0.22–1.83 km 2 ) in southeastern Minnesota. We simulated the spatial and temporal fluctuations of the indirect emissions from streams, identified emission “hot spots” and “hot moments,” and diagnosed the correlations between direct and indirect emissions. We demonstrated that zero-order streams and first-order streams could contribute 0.034–0.066 and 0.011 nmol N 2 O m –2 s –1 (expressed on the basis of unit catchment area) to the total surface emissions, respectively. Emissions from zero-order and first-order streams equal 24–41% of direct emissions from soil, which may explain the emission gap between calculations using top-down and bottom-up methods. Clear spatial patterns were identified for both direct and indirect emissions and their spatial variations were negatively correlated. Our results indicate that the IPCC N 2 O emission factor for streams in the Corn Belt should be increased by 3.2–5.7 times. Increasing precipitation and streamflow in the Corn Belt may potentially increase frequencies of soil anoxic conditions and nitrate leaching to streams, and subsequently increase N 2 O emissions from both soils and streams.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Implications of increased spatial and trophic overlap between juvenile Pacific salmon and Sablefish in the northern California Current

Abstract Objective The study was designed to assess long-term variability in the distribution of juvenile Pacific salmon Oncorhynchus spp. and Sablefish Anoplopoma fimbria. The study also evaluated whether Sablefish and Pacific salmon shared food resources and looked to characterize Sablefish during an understudied period of their life cycle. Methods To meet the objectives, the study used data from 26 years of surface trawls conducted in Oregon and Washington coastal waters (1998–2023). Spatial–temporal models were used to measure changes in abundance and distribution of Pacific salmon and Sablefish along with covariates of ocean temperature. The study evaluated trophic characteristics of Pacific salmon and Sablefish from 2020 for differences. The temporal variation in size and diets of Sablefish were also analyzed, along with energy density of fish caught in 2020. Result The spatial–temporal model demonstrated that there has been a nearshore expansion of juvenile Sablefish over the past 26 years that was correlated with increased ocean temperature. The nearshore expansion of Sablefish resulted in increased spatial and trophic overlap with juvenile Pacific salmon. While feeding in nearshore waters, juvenile Sablefish demonstrated competitive feeding advantages over juvenile Pacific salmon during a critical phase of salmonid early marine life history. Juvenile Sablefish exhibited significant ontogenetic diet and energetic shifts, and even the smallest (68–80 mm fork length) were piscivorous. Conclusions If juvenile Sablefish numbers continue to increase relative to Pacific salmon, they could exert more competitive pressure, especially if food resources become limited. Pacific salmon may experience adverse effects from competition, regardless of whether or not juvenile Sablefish, which have recently expanded into nearshore waters, successfully recruit to the adult population.

Daly, Elizabeth A. (ORCID:0000000195334457)↗
DOE OSTI2020

Correlation Function Approach for Diffusion in Confined Geometries

This paper describes a formalism for extracting spatially varying transport coefficients from simulations of a molecular fluid in a nano channel. This approach is applied to self-diffusion of a Lennard-Jones fluid confined between two parallel surfaces. A numerical grid is laid over the domain confining the fluid, and fluid properties are projected onto the grid cells. The time correlation functions between properties in different grid cells are calculated and can be used as the basis for a fitting procedure for extracting spatially varying diffusion coefficients from the simulation. Results for the Lennard-Jones system show that transport behavior varies sharply near the liquid-solid boundary and that the changes depend on the details of the liquid-solid interaction. A quantitative difference between the reduced and detailed models is discussed. It is found that the difference could be associated with assumptions about the form of the transport equations at molecular scales in lieu of problems with the method itself. The study suggests that this approach to fitting molecular simulations to continuum equations may guide the development of appropriate coarse-grained equations to model transport phenomena at nanometer scales.

Nanoscale flow, Transport, molecular simulation↗
DOE OSTI2020

Reducing uncertainties in satellite estimates of aerosol–cloud interactions over the subtropical ocean by integrating vertically resolved aerosol observations

Satellite quantification of aerosol effects on clouds relies on aerosol optical depth (AOD) as a proxy for aerosol concentration or cloud condensation nuclei (CCN). However, the lack of error characterization of satellite-based results hampers their use for the evaluation and improvement of global climate models. We show that the use of AOD for assessing aerosol–cloud interactions (ACIs) is inadequate over vast oceanic areas in the subtropics. Instead, we postulate that a more physical approach that consists of matching vertically resolved aerosol data from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite at the cloud-layer height with Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua cloud retrievals reduces uncertainties in satellite-based ACI estimates. Combined aerosol extinction coefficients (σ) below cloud top (σBC) from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and cloud droplet number concentrations (N d ) from MODIS Aqua yield high correlations across a broad range of σBC values, with σBC quartile correlations ≥0.78. In contrast, CALIOP-based AOD yields correlations with MODIS Nd of 0.54–0.62 for the two lower AOD quartiles. Moreover, σBC explains 41% of the spatial variance in MODIS N d , whereas AOD only explains 17%, primarily caused by the lack of spatial covariability in the eastern Pacific. Compared with σBC, near-surface σ weakly correlates in space with MODIS N d , accounting for a 16% variance. It is concluded that the linear regression calculated from ln(N d )–ln(σBC) (the standard method for quantifying ACIs) is more physically meaningful than that derived from the Nd–AOD pair.

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

MemFriend: Understanding Memory Performance with Spatial-Temporal Affinity

In HPC applications, memory access behavior is one of the main factors affecting performance. Improving an application’s memory access behavior involves optimizing data layout and/or restructuring code, and requires studying spatial-temporal data locality. Existing data locality analyses focus on single-location metrics and are restricted to evaluating temporal locality. We introduce spatial-temporal affinity metrics that quantify temporal access proximity, forward access correlation, and nearby access correlation between pairs of memory locations. We describe methods for distinguishing between potential vs. realized affinity and for reasoning about affinity at multiple resolutions (3D, 2D, 1D). Finally, we construct spatial-temporal affinity signatures that classify memory behavior and that be used to reason about changes in software (data relayout, code refactoring) or hardware (caching, prefetching). We describe methods for signature visualization, interpretation, and quantitative comparison of signatures. We evaluate our methodology using applications with variants that contrast data structures, data layouts and algorithms. We show that spatial-temporal affinity analysis provides novel insights and enables predictive reasoning about application performance when contrasted with reuse distance analysis.

Suriyakumar, Yasodhadevi↗
DOE OSTI2026

Endophyte‐induced systemic spatial reprogramming of metabolism in Populus trichocarpa roots under drought

Beneficial, facultative endophytes help plants thrive in challenging environments by altering their host's metabolism, but how these cellular scale metabolic changes propagate to the systems biology scale is unknown. In this work, we employed a high-resolution chemical imaging approach to map metabolic changes at the Populus trichocarpa root-zone and cell-type levels combined with machine learning (ML) models to identify root metabolites and exudates that have predictive power over treatment class. We found that a nine-strain consortium of beneficial endophytes differentially altered the metabolome of droughted root tissues in a manner specific to cell type and root zone, with endophyte abundance showing a clear correlation to individual metabolites. Our study demonstrates that integrating spatial metabolomics with ML can reveal localized metabolic patterns linked to root–microbe interactions and generate novel hypotheses about underlying biological mechanisms.

Drought↗
DOE OSTI2024

Predicting responses to climate change using a joint species, spatially dependent physiologically guided abundance model

Abstract Predicting the effects of warming temperatures on the abundance and distribution of organisms under future climate scenarios often requires extrapolating species–environment correlations to climatic conditions not currently experienced by a species, which can result in unrealistic predictions. For poikilotherms, incorporating species' thermal physiology to inform extrapolations under novel thermal conditions can result in more realistic predictions. Furthermore, models that incorporate species and spatial dependencies may improve predictions by capturing correlations present in ecological data that are not accounted for by predictor variables. Here, we present a joint species, spatially dependent physiologically guided abundance (jsPGA) model for predicting multispecies responses to climate warming. The jsPGA model uses a basis function approach to capture both species and spatial dependencies. We apply the jsPGA model to predict the response of eight fish species to projected climate warming in thousands of lakes in Minnesota, USA. By the end of the century, the cold‐adapted species was predicted to have high probabilities of extirpation across its current range—with 10% of lakes currently inhabited by this species having an extirpation probability >0.90. The remaining species had varying levels of predicted changes in abundance, reflecting differences in their thermal physiology. Though the model did not identify many strong species dependencies, the variation in estimated spatial dependence across species suggested that accounting for both dependencies was important for predicting the abundance of these fishes. The jsPGA model provides a new tool for predicting changes in the abundance, distribution, and extirpation probability of poikilotherms under novel thermal conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Predicting photovoltaic soiling losses using environmental parameters: An update

This study presents an investigation on the correlations between soiling losses and environmental parameters at 41 locations in the United States, with the aim of analyzing the possibility of predicting soiling losses at a site even when soiling data are not available. The results of this work, which considers the largest pool of soiling data points systematically investigated so far, confirm that a single-variable regression based on particulate matter concentration returns the best correlations with soiling, with adjusted coefficients of determination up to 70%, corresponding to RMSE as low as 0.9%. Among the various particulate matter datasets investigated, a gridded Environment Protection Agency dataset is for the first time found to return correlations similar to those obtained by interpolating particulate matter monitoring station data. We discuss in detail the different interpolation techniques used to process the particulate matter concentrations because they can greatly impact the correlations. Specifically, the correlation coefficients between soiling and particulate matter range between 70% and less than 20%, depending on the interpolation methods and monitoring distance. Spatial interpolation methods based on inverse distance weighting are found to return better correlations than a nearest neighbor or a simple average approach, especially when large distances are considered. Similarly, the effects of different rain thresholds used to calculate the length of the dry periods are examined. An enhanced two-variable regression is found to achieve higher-quality correlations, with adjusted R2 of 90% (RMSE = 0.55%), also suggesting that high and low soiling locations might be differentiated depending on fixed particulate matter or rainfall thresholds.

14 SOLAR ENERGY↗
DOE OSTI2022

Organic matter distribution in the icy environments of Taylor Valley, Antarctica

Glaciers can accumulate and release organic matter affecting the structure and function of associated terrestrial and aquatic ecosystems. Here we analyzed 18 ice cores collected from six locations in Taylor Valley (McMurdo Dry Valleys), Antarctica to determine the spatial abundance and quality of organic matter, and the spatial distribution of bacterial density and community structure from the terminus of the Taylor Glacier to the coast (McMurdo Sound). Our results showed that dissolved and particulate organic carbon (DOC and POC) concentrations in the ice core samples increased from the Taylor Glacier to McMurdo Sound, a pattern also shown by bacterial cell density. Fluorescence Excitation Emission Matrices Spectroscopy (EEMs) and multivariate parallel factor (PARAFAC) modeling identified one humic-like (C1) and one protein-like (C2) component in ice cores whose fluorescent intensities all increased from the Polar Plateau to the coast. The fluorescence index showed that the bioavailability of dissolved organic matter (DOM) also decreased from the Polar Plateau to the coast. Partial least squares path modeling analysis revealed that bacterial abundance was the main positive biotic factor influencing both the quantity and quality of organic matter. Marine aerosol influenced the spatial distribution of DOC more than katabatic winds in the ice cores. Certain bacterial taxa showed significant correlations with DOC and POC concentrations. Collectively, our results show the tight connectivity among organic matter spatial distribution, bacterial abundance and meteorology in the McMurdo Dry Valley ecosystem.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Correlative SIP-FISH-Raman-SEM-NanoSIMS links identity, morphology, biochemistry, and physiology of environmental microbes

Microscopic and spectroscopic techniques are commonly applied to study microbial cells but are typically used on separate samples, resulting in population-level datasets that are integrated across different cells with little spatial resolution. To address this shortcoming, we developed a workflow that correlates several microscopic and spectroscopic techniques to generate an in depth analysis of individual cells. By combining stable isotope probing (SIP), fluorescence in situ hybridization (FISH), scanning electron microscopy (SEM), confocal Raman microspectroscopy (Raman), and nano-scale secondary ion mass spectrometry (NanoSIMS), we illustrate how individual cells can be thoroughly interrogated to obtain information about their taxonomic identity, structure, physiology, and metabolic activity. Analysis of an artificial community demonstrated that our correlative approach was able to resolve the activity of single cells using heavy water SIP in conjunction with Raman and/or NanoSIMS and establish their taxonomy and morphology using FISH and SEM. We then applied this workflow to a sample of yet uncultured multicellular magnetotactic bacteria. In addition to establishing their identity and activity, backscatter electron microscopy (BSE), NanoSIMS, and energy-dispersive X-ray spectroscopy (EDS) were employed to characterize the magnetosomes within the cells. By integrating these techniques, we demonstrate a cohesive approach to thoroughly study environmental microbes at single cell resolution.

Schaible, George↗
DOE OSTI2019

Direct Nanoscale Characterization of Deep Levels in AgCuInGaSe 2 Using Electron Energy–Loss Spectroscopy in the Scanning Transmission Electron Microscope

A new experimental framework for the characterization of defects in semiconductors is demonstrated. Through the direct, energy-resolved correlation of three analytical techniques spanning six orders of magnitude in spatial resolution, a critical mid-bandgap electronic trap level (E V + 0.56 eV) within Ag 0.2 Cu 0.8 In 1–x Ga x Se 2 is traced to its nanoscale physical location and chemical source. This is achieved through a stepwise, site-specific correlated characterization workflow consisting of device-scale (≈1 mm 2 ) deep level transient spectroscopy (DLTS) to survey the traps present, scanning probe–based DLTS (scanning-DLTS) for mesoscale-resolved (hundreds of nanometers) mapping of the target trap state’s spatial distribution, and scanning transmission electron microscope based electron energy-loss spectroscopy (STEM-EELS) and X-ray energy-dispersive spectroscopy for nanoscale energy-, structure, and chemical-resolved investigation of the defect source. Furthermore, this first demonstration of the direct observation of sub-bandgap defect levels via STEM-EELS, combined with the DLTS methods, provides strong evidence that the longsuspected Cu In/Ga substitutional defects are indeed the most likely source of the E V + 0.56 eV trap state and serves as a key example of this approach for the fundamental identification of defects within semiconductors, in general.

14 SOLAR ENERGY↗
DOE OSTI2016

Crystal plasticity investigation of the microstructural factors influencing dislocation channeling in a model irradiated bcc material

We use a continuum crystal plasticity framework to study the effect of microstructure and mesoscopic factors on dislocation channeling and flow localization in an irradiated model bcc alloy. For simulated dislocation channeling characteristics we correlate the dislocation and defect densities in the substructure, local Schmid factor, and stress triaxiality, in terms of their temporal and spatial evolution. A metric is introduced to assess the propensity for localization and is correlated to the grain-level Schmid factor. We also found that localization generally takes place in grains with a local Schmid factor in the range 0.42 or higher. Surface slip step heights are computed at free surfaces and compared to relevant experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2024

High-resolution mapping of nitrogen oxide emissions in large US cities from TROPOMI retrievals of tropospheric nitrogen dioxide columns

Satellite-derived spatiotemporal patterns of nitrogen oxide (NOx) emissions can improve accuracy of emission inventories to better support air quality and climate research and policy studies. In this study, we develop a new method by coupling the chemical transport Model-Independent SATellite-derived Emission estimation Algorithm for Mixed-sources (MISATEAM) with a divergence method to map high-resolution NO x emissions across US cities using TROPOspheric Monitoring Instrument (TROPOMI) tropospheric nitrogen dioxide (NO 2 ) retrievals. The accuracy of the coupled method is validated through application to synthetic NO 2 observations from the NASA-Unified Weather Research and Forecasting (NU-WRF) model, with a horizontal spatial resolution of 4 km × 4 km for 33 large and mid-size US cities. Validation reveals excellent agreement between inferred and NU-WRF-provided emission magnitudes (R= 0.99, normalized mean bias, NMB = —0.01) and a consistent spatial pattern when comparing emissions for individual grid cells (R = 0.88 ± 0.06). We then develop a TROPOMI-based database reporting annual emissions for 39 US cities at a horizontal spatial resolution of 0.05° × 0.05° from 2018 to 2021. This database demonstrates a strong correlation (R= 0.90) with the National Emission Inventory (NEI) but reveals some bias (NMB = —0.24). There are noticeable differences in the spatial patterns of emissions in some cities. Our analysis suggests that uncertainties in TROPOMI-based emissions and potential misallocation of emissions and/or missing sources in bottom-up emission inventories both contribute to these differences.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Label-free nanoscopy of cell metabolism by ultrasensitive reweighted visible stimulated Raman scattering

Nanoscopic imaging of cell metabolism is hindered by the incompatibility of small metabolites with fluorescent dyes and the limited resolution of imaging mass spectrometry. We present ultrasensitive reweighted visible stimulated Raman scattering (URV-SRS), a label-free vibrational nanoscopy technique that enables multiplexed detection of metabolic nanostructures within cells. We developed an extensively chirped spectral focusing visible SRS microscope that achieves a detection limit of 4,000 molecules and introduced a self-supervised learning-based denoiser to robustly suppress non-independent SRS noise by over 7.2 dB. The instrumentation-based signal enhancement and computation-based noise suppression synergistically improved the detection sensitivity by 50 times over near-infrared SRS. Leveraging this enhanced sensitivity, we further pushed the resolution to nanoscopic levels by introducing Fourier reweighting to amplify sub-100 nm spatial frequencies previously overwhelmed by noise. Validated by Fourier ring correlation, URV-SRS achieves a lateral resolution of 86 nm in cellular imaging. Here, we applied URV-SRS to elucidate the reprogramming of metabolic nanostructures associated with virus replication in Vero E6 host cells and to compositionally delineate subcellular fatty acid synthesis in engineered Escherichia coli, demonstrating its capability towards nanoscopic spatial metabolomics.

59 BASIC BIOLOGICAL SCIENCES↗