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Results for “Spatial correlation”

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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
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 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↗
DOE OSTI2020

Smart Adhesive Joint with High-Definition Fiber-Optic Sensing for Automotive Applications

Structural health monitoring of fiber-reinforced composite-based joints for automotive applications during their manufacturing and on-demand assessment for its durability in working environments is critically needed. High-definition fiber-optic sensing is an effective method to measure internal strain/stress development using minimally invasive continuous sensors. The sensing fiber diameters are in the same order of magnitude when compared to reinforcement (glass, basalt, or carbon fibers) used in polymer composites. They also offer a unique ability to monitor the evolution of residual stresses after repeated thermal exposure with varying temperatures for automotive components/joints during painting using an electrophoretic painting process. In this paper, a high-definition fiber-optic sensor utilizing Rayleigh scattering is embedded within an adhesive joint between a carbon fiber-reinforced thermoset composite panel and an aluminum panel to measure spatially resolved strain development, residual strain, and thermal expansion properties during the electrophoretic paint process-simulated conditions. The strain measured by the continuous fiber-optic sensor was compared with an alternate technique using thermal digital image correlation. The fiber-optic sensor was able to identify the spatial variation of residual strains for a discontinuous carbon fiber-reinforced composite with varying local fiber orientations and resin content.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

A field–particle correlation analysis of a perpendicular magnetized collisionless shock

Using the field–particle correlation technique, we examine the particle energization in a three-dimensional (one spatial dimension and two velocity dimensions; 1D-2V) continuum Vlasov–Maxwell simulation of a perpendicular magnetized collisionless shock. The combination of the field–particle correlation technique with the high-fidelity representation of the particle distribution function provided by a direct discretization of the Vlasov equation allows us to ascertain the details of the exchange of energy between the electromagnetic fields and the particles in phase space. We identify the velocity-space signatures of shock-drift acceleration of the ions and adiabatic heating of the electrons arising from the perpendicular collisionless shock by constructing a simplified model with the minimum ingredients necessary to produce the observed energization signatures in the self-consistent Vlasov–Maxwell simulation. We are thus able to completely characterize the energy transfer in the perpendicular collisionless shock considered here and provide predictions for the application of the field–particle correlation technique to spacecraft measurements of collisionless shocks.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Spectroscopy of Twisted Bilayer Graphene Correlated Insulators

Here we analytically compute the scanning tunneling microscopy (STM) signatures of integer-filled correlated ground states of the magic angle twisted bilayer graphene (TBG) narrow bands. After experimentally validating the strong-coupling approach at ± 4 electrons/moiré unit cell, we consider the spatial features of the STM signal for 14 different many-body correlated states and assess the possibility of Kekulé distortion (KD) emerging at the graphene lattice scale. Remarkably, we find that coupling the two opposite graphene valleys in the intervalley-coherent (IVC) TBG insulators does not always result in KD. As an example, we show that the Kramers IVC state and its nonchiral U(4) rotations do not exhibit any KD, while the time-reversal-symmetric IVC state does. Our results, obtained over a large range of energies and model parameters, show that the STM signal and Chern number of a state can be used to uniquely determine the nature of the TBG ground state.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Analysis of time-dependent strain heterogeneity in shales using X-ray microscopy and digital volume correlation

In this paper, we report the results of a 4-dimensional (3 spatial dimensions plus time) in-situ creep experiment on a milli-core shale plug from Barnett formation in Texas. An outstanding challenge in shale mechanics is connecting observable characteristics to predictable mechanical and hydraulic behavior. We employ novel imaging and analysis techniques to statistically link sample mineralogy and structure to observed deformation. In particular, we focus on mechanisms of porosity reduction during transient response to step changes in stress, analogous to the aseismic deformation that occurs after an episode of fracturing. Our main goal in this study is to take the first steps to visualize and capture the small-scale deformation mechanisms involved in creep of shales, using imaging techniques. To do so, we conducted a uniaxial creep experiment inside an Xradia Versa 520 micro-CT system for 12 h at room temperature under a constant uniaxial stress of 25 MPa. We employed a Digital Volume Correlation (DVC) method to measure micro-scale deforspmation of the sample by comparing the resulting volumetric images with a high-resolution scan of the sample obtained prior to the creep experiment. The results indicate that the time-dependent deformation in the compliant clay plus kerogen matrix constituents facilitates rigid grain rotation and compaction of intergranular porosity at the boundary between rigid grains and matrix.

58 GEOSCIENCES↗
DOE OSTI2023

Deep Learning on Multimodal Chemical and Whole Slide Imaging Data for Predicting Prostate Cancer Directly from Tissue Images

Prostate cancer is one of the most common cancers globally and is the second most common cancer in the male population in the US. Here we develop a study based on correlating the hematoxylin and eosin (H&E)-stained biopsy data with MALDI mass-spectrometric imaging data of the corresponding tissue to determine the cancerous regions and their unique chemical signatures and variations of the predicted regions with original pathological annotations. We obtain features from high-resolution optical micrographs of whole slide H&E stained data through deep learning and spatially register them with mass spectrometry imaging (MSI) data to correlate the chemical signature with the tissue anatomy of the data. We then use the learned correlation to predict prostate cancer from observed H&E images using trained coregistered MSI data. This multimodal approach can predict cancerous regions with ~80% accuracy, which indicates a correlation between optical H&E features and chemical information found in MSI. Further, we show that such paired multimodal data can be used for training feature extraction networks on H&E data which bypasses the need to acquire expensive MSI data and eliminates the need for manual annotation saving valuable time. Two chemical biomarkers were also found to be predicting the ground truth cancerous regions. This study shows promise in generating improved patient treatment trajectories by predicting prostate cancer directly from readily available H&E-stained biopsy images aided by coregistered MSI data.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Reimagining full wave rf quasilinear theory in a tokamak

The velocity dependent resonant interaction of particles with applied radiofrequency (rf) waves during heating and current drive in the presence of pitch angle scattering collisions gives rise to narrow collisional velocity space boundary layers that dramatically enhance the role of collisions as recently shown by Catto ( J. Plasma Phys. , vol. 86, 2020, 815860302). The behaviour is a generalization of the narrow collisional boundary layer that forms during Landau damping as found by Johnston ( Phys. Fluids , vol. 14, 1971, pp. 2719–2726) and Auerbach ( Phys. Fluids , vol. 20, 1977, pp. 1836–1844). For a wave of parallel wave number ${k_{||}}$ interacting with weakly collisional plasma species of collision frequency $\nu$ and thermal speed ${v_{\textrm{th}}}$ , the effective collision frequency becomes of order $\nu {({k_{||}}{v_{th}}/\nu )^{2/3}} \gg \nu $ . The narrow boundary layers that arise because of the diffusive nature of the collisions allow a physically meaningful wave–particle interaction time to be defined that is the inverse of this effective collision frequency. The collisionality implied by the narrow boundary layer results in changes in the standard quasilinear treatment of applied rf fields in tokamaks while remaining consistent with causality. These changes occur because successive poloidal interactions with the rf are correlated in tokamak geometry and because the resonant velocity space dependent interactions are controlled by the spatial and temporal behaviour of the perturbed full wave fields rather than just the spatially local Landau and Doppler shifted cyclotron wave–particle resonance condition associated with unperturbed motion of the particles. The correlation of successive poloidal circuits of the tokamak leads to the appearance in the quasilinear operator of transit averaged resonance conditions localized in velocity space boundary layers that maintain negative definite entropy production.

Physics↗
DOE OSTI2025

X-ray-diffraction and electrical-transport imaging of superconducting superhydride (La,Y)H 10

Understanding how microscopic structural domains govern macroscopic electronic properties is central to advancing hydride superconductors, yet such correlations remain poorly resolved under pressure. We report the synthesis and characterization of (La 0.9 Y 0.1 )H 10 superhydrides exhibiting coexisting cubic $Fm\bar{3}m$ and $P6_3/mmc$ hexagonal clathrate phases observed over the pressure range from 168 GPa down to 136 GPa. Using synchrotron-based X-ray diffraction imaging at the upgraded Advanced Photon Source, we spatially resolved μm-scale distributions of these phases, revealing structural inhomogeneity across the sample. Four-probe resistance measurements confirmed superconductivity with two distinct transitions: an onset at 244 K associated with the cubic phase and a second near 220 K linked to the hexagonal phase. Notably, resistance profiles collected from multiple current and voltage permutations showed variations in transition width and onset temperature that correlated with the spatial phase distribution. These findings demonstrate a direct connection between local structural domains and superconducting behavior.

Materials science↗
DOE OSTI2024

Uranium and Nickel Partitioning in a Contaminated Riparian Wetland

Uranium (U) and nickel (Ni) released 50 years ago have been immobilized in the Tims Branch wetlands located on the Savannah River Site in the United States. Sediments were collected from seven locations to identify the factors responsible for this attenuation. Ni and U contents in the solids were significantly correlated, suggesting that depositional as opposed to chemical processes contributed to their spatial distribution. Based on sequential extractions, 63 ± 16% of the U was partitioned into the organic fraction, whereas Ni was distributed between several sediment fractions. An inverse pH-organic matter (OM) correlation and positive correlations of OM with total U and organic-bound U/Ni suggest that increased OM preservation and binding to the mineral surfaces were likely responsible for Ni- and especially U-sediment retention (Tims Branch pH = 4.84 ± 0.68). EXAFS analysis indicated the predominance of U(VI) coordinated with clay minerals (~65%), together with ~35% coordinated to either OM (in areas with elevated OM levels) or iron oxides. The desorption-Kd coefficients of U (3972 ± 1370 L/kg) and Ni (30 ± 8 L/kg) indicate that dissolved Ni poses a greater long-term risk than dissolved U for migrating downstream. This study suggests that a delicate balance of geochemical properties controls whether wetlands behave as sinks or sources of contaminants.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗