Search NASA⌕ Search

SEARCH · Search NASA

Results for “resolution process”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

In vitro investigation of protein assembly by combined microscopy and infrared spectroscopy at the nanometer scale

The nanoscale structure and dynamics of proteins on surfaces has been extensively studied using various imaging techniques, such as transmission electron microscopy and atomic force microscopy (AFM) in liquid environments. These powerful imaging techniques, however, can potentially damage or perturb delicate biological material and do not provide chemical information, which prevents a fundamental understanding of the dynamic processes underlying their evolution under physiological conditions. Here, we use a platform developed in our laboratory that enables acquisition of infrared (IR) spectroscopy and AFM images of biological material in physiological liquids with nanometer resolution in a cell closed by atomically thin graphene membranes transparent to IR photons. In this work, we studied the self-assembly process of S-layer proteins at the graphene-aqueous solution interface. The graphene acts also as the membrane separating the solution containing the proteins and Ca 2+ ions from the AFM tip, thus eliminating sample damage and contamination effects. The formation of S-layer protein lattices and their structural evolution was monitored by AFM and by recording the amide I and II IR absorption bands, which reveal the noncovalent interaction between proteins and their response to the environment, including ionic strength and solvation. Our measurement platform opens unique opportunities to study biological material and soft materials in general.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

High-Resolution Regional Atmosphere–Ocean–Wave Coupled Simulations of Hurricane Henri (2021)

To explore the integrated effects of ocean and ocean surface wave related physical processes on TC simulations, a set of three model simulations is performed. * In experiment 'A', the event is modeled using the Weather Research Forecasting (WRF) model alone with prescribed Sea Surface Temperature (SST) at 6-hour intervals. * In experiment ‘AO,’ WRF is coupled with the Finite Volume Community Ocean Model (FVCOM), enabling variable exchange between atmosphere and ocean, but without considering ocean surface wave-related physical processes. * In experiment ‘AOW’, WRF, FVCOM, and Simulating WAves Nearshore (SWAN) exchange variables with each other every hour through the OASIS3-MCT Coupler to allow direct and indirect atmosphere-ocean-wave interactions. * Observational data are also included in this dataset (Dropsonde, HRD-Radar, NDBC_wave). All simulations are initialized at 18:00 UTC on August 19, 2021, within a domain encompassing the western North Atlantic Ocean. The atmospheric domain features a horizontal resolution of 3 km. The ocean domain, which covers a substantial portion of the WRF ocean domain, employs an unstructured triangular grid with resolutions ranging from 3 km near the coast to 9 km in the open ocean, effectively resolving the complex coastline of the U.S. Northeast Coast. Initial and boundary conditions for the atmosphere model are obtained from the 6-hourly 0.25° NCEP (National Centers for Environmental Prediction) Global Forecast System (GFS; NCEP, 2015) data.

17 WIND ENERGY↗
DOE OSTI2026

High-Resolution Regional Atmosphere–Ocean–Wave Coupled Simulations of Hurricane Henri (2021)

To explore the integrated effects of ocean and ocean surface wave related physical processes on TC simulations, a set of three model simulations is performed. * In experiment 'A', the event is modeled using the Weather Research Forecasting (WRF) model alone with prescribed Sea Surface Temperature (SST) at 6-hour intervals. * In experiment ‘AO,’ WRF is coupled with the Finite Volume Community Ocean Model (FVCOM), enabling variable exchange between atmosphere and ocean, but without considering ocean surface wave-related physical processes. * In experiment ‘AOW’, WRF, FVCOM, and Simulating WAves Nearshore (SWAN) exchange variables with each other every hour through the OASIS3-MCT Coupler to allow direct and indirect atmosphere-ocean-wave interactions. * Observational data are also included in this dataset (Dropsonde, HRD-Radar, NDBC_wave). All simulations are initialized at 18:00 UTC on August 19, 2021, within a domain encompassing the western North Atlantic Ocean. The atmospheric domain features a horizontal resolution of 3 km. The ocean domain, which covers a substantial portion of the WRF ocean domain, employs an unstructured triangular grid with resolutions ranging from 3 km near the coast to 9 km in the open ocean, effectively resolving the complex coastline of the U.S. Northeast Coast. Initial and boundary conditions for the atmosphere model are obtained from the 6-hourly 0.25° NCEP (National Centers for Environmental Prediction) Global Forecast System (GFS; NCEP, 2015) data.

17 WIND ENERGY↗
DOE OSTI2026

High-Resolution Regional Atmosphere–Ocean–Wave Coupled Simulations of Hurricane Henri (2021)

To explore the integrated effects of ocean and ocean surface wave related physical processes on tropical cyclone simulations, a set of three model simulations is performed. * In experiment 'A', the event is modeled using the Weather Research Forecasting (WRF) model alone with prescribed Sea Surface Temperature (SST) at 6-hour intervals. * In experiment ‘AO,’ WRF is coupled with the Finite Volume Community Ocean Model (FVCOM), enabling variable exchange between atmosphere and ocean, but without considering ocean surface wave-related physical processes. * In experiment ‘AOW’, WRF, FVCOM, and Simulating WAves Nearshore (SWAN) exchange variables with each other every hour through the OASIS3-MCT Coupler to allow direct and indirect atmosphere-ocean-wave interactions. * Observational data are also included in this dataset (Dropsonde, HRD-Radar, NDBC_wave). All simulations are initialized at 18:00 UTC on August 19, 2021, within a domain encompassing the western North Atlantic Ocean. The atmospheric domain features a horizontal resolution of 3 km. The ocean domain, which covers a substantial portion of the WRF ocean domain, employs an unstructured triangular grid with resolutions ranging from 3 km near the coast to 9 km in the open ocean, effectively resolving the complex coastline of the U.S. Northeast Coast. Initial and boundary conditions for the atmosphere model are obtained from the 6-hourly 0.25° NCEP (National Centers for Environmental Prediction) Global Forecast System (GFS; NCEP, 2015) data. These CSV files are derived from the NetCDF files in the c0 dataset. Unlike the original format, where geographic coordinates were stored in a separate file, each CSV now embeds the corresponding latitude and longitude values alongside the measured or simulated variables.

17 WIND ENERGY↗
DOE OSTI2026

High-Resolution WRF-Based Downscaling of Earth System Model Projections for Energy Applications across CONUS

Evaluating energy resources under future scenarios requires meteorological information that adequately resolves regional-scale variability and is suitable for regional energy system studies. Although Earth system model (ESM) outputs provide essential large-scale context, their coarse resolution and inherent biases limit direct use in energy system applications. This work presents a high-resolution dynamical downscaling framework using the Weather Research and Forecasting (WRF) model to generate energy-relevant regional fields for future scenarios over the contiguous United States (CONUS). The framework first identifies an optimal WRF configuration through sensitivity experiments, then evaluates raw and bias-corrected ESM initial and boundary conditions, with soil moisture and soil temperature bias correction implemented as an integral component of the bias-corrected ESM atmospheric forcing prior to WRF dynamical downscaling to improve land-atmosphere interactions. Simulations performed at 4-km resolution show that uncorrected ESM forcing leads to systematically dry and cold soil states, which propagate into elevated near-surface air temperature and solar irradiance biases, particularly during summer for the period 2000-2014. Incorporating bias-corrected atmospheric forcing together with soil state bias correction substantially reduces these errors and improves the representation of surface energy processes in WRF simulations. The results highlight the importance of bias-aware initialization strategies in high-resolution dynamical downscaling for future energy system analysis and planning.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Prescribing the aerosol effective radiative forcing in the Simple Cloud-Resolving E3SM Atmosphere Model v1

Aerosol effective radiative forcing critically influences climate projections but remains poorly constrained. Using the Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM) v1 configuration, we assess the sensitivity of the radiative forcing due to anthropogenic aerosol changes using a simplified prescribed aerosol scheme (SPA) derived from E3SM v3. Nudged simulations at 3 and 12 km horizontal grid spacings reveal a more negative aerosol forcing than the reference 100 km E3SM v3 whence the SPA properties are derived. The resulting globally averaged aerosol forcing signal is largely due to aerosol–cloud interactions and exhibits little overall resolution sensitivity, while hints of resolution sensitivity appear regionally between the 3 and 12 km runs. While the default SPA scheme overestimates cloud droplet dependence on aerosols, parameterization adjustments in the activation process reconcile forcing estimates with the reference model. Our results demonstrate the ability to use a prescribed aerosol scheme to hold aerosol forcing to a desired strength across resolutions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

PyZebrascope: An Open-Source Platform for Brain-Wide Neural Activity Imaging in Zebrafish

Understanding how neurons interact across the brain to control animal behaviors is one of the central goals in neuroscience. Recent developments in fluorescent microscopy and genetically-encoded calcium indicators led to the establishment of whole-brain imaging methods in zebrafish, which record neural activity across a brain-wide volume with single-cell resolution. Pioneering studies of whole-brain imaging used custom light-sheet microscopes, and their operation relied on commercially developed and maintained software not available globally. Hence it has been challenging to disseminate and develop the technology in the research community. Here, we present PyZebrascope, an open-source Python platform designed for neural activity imaging in zebrafish using light-sheet microscopy. PyZebrascope has intuitive user interfaces and supports essential features for whole-brain imaging, such as two orthogonal excitation beams and eye damage prevention. Its camera module can handle image data throughput of up to 800 MB/s from camera acquisition to file writing while maintaining stable CPU and memory usage. Its modular architecture allows the inclusion of advanced algorithms for microscope control and image processing. As a proof of concept, we implemented a novel automatic algorithm for maximizing the image resolution in the brain by precisely aligning the excitation beams to the image focal plane. PyZebrascope enables whole-brain neural activity imaging in fish behaving in a virtual reality environment. Thus, PyZebrascope will help disseminate and develop light-sheet microscopy techniques in the neuroscience community and advance our understanding of whole-brain neural dynamics during animal behaviors.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Multiscale Characterization of Additive Manufacturing Components with Computed Tomography, 3D X-ray Microscopy, and Deep Learning

Additive manufacturing (AM) facilitates the creation of complex-geometry parts, driving advancements in lightweight aerospace components, high-efficiency engine cooling channels, and customized medical implants. However, ensuring the quality and reliability of AM parts remains challenging due to internal defects, surface irregularities, porosity, and residual trapped powder, which are often inaccessible to traditional inspection methods. Recent developments in X-ray computed tomography (XCT) and 3D X-ray microscopy (XRM), particularly systems equipped with resolution-at-a-distance (RaaD™) capabilities, enable high-resolution, non-destructive evaluation of AM components across multiple scales, from sub-micrometer to macroscopic levels. This paper explores modern XCT and XRM techniques for multiscale characterization of AM parts, focusing on their ability to detect and analyze defects such as porosity, cracks, inclusions, and surface roughness, while offering insights into defect formation mechanisms, material properties, and process-induced variations. The integration of deep learning (DL) frameworks, including Simurgh, DeepRecon, and DeepScout, enhances XCT/XRM workflows by reducing scan times, improving resolution recovery, and enabling accurate defect detection even with limited projection data. These DL-based methods overcome limitations of traditional reconstruction techniques, enabling faster, more reliable characterization of dense materials like Inconel 718 and novel alloys such as AlCe. Applications include process parameter optimization, high-throughput quality control, and multistage AM process evaluation, with DL-enhanced workflows accelerating analysis times from weeks to days. Correlative imaging approaches further validate XCT and XRM data against scanning electron microscopy (SEM) images of physically sectioned samples, confirming the accuracy of DL-based reconstructions and enabling comprehensive defect analysis. While challenges remain in generalizing DL models to diverse materials and imaging conditions, improvements in resolution, noise reduction, and defect detection highlight the transformative potential of these methods. This multiscale and correlative approach enables precise identification and correlation of microstructural features with the overall performance of AM components. By integrating advanced XCT, XRM, and DL techniques, this paper demonstrates a significant leap forward in AM characterization, offering valuable insights into the relationships between processing parameters, microstructure, and part performance, and driving innovations that enhance the quality and reliability of AM products for demanding industrial applications.

Additive manufacturing↗
DOE OSTI2025

Cathode Upcycling for Direct Recycling of Lithium‐Ion Batteries Using a Precipitation Approach

With the increased production of electric vehicles to reduce carbon emissions, the lithium-ion battery market to supply those vehicles has grown dramatically. To enhance battery sustainability and circularity, direct recycling methods aim to recover intact cathode materials. However, end-of-life cathode materials are typically 15–20 years old and often have lower energy density compared to current cathode materials. To address this challenge, a rapid precipitation process is developed to boost energy density by converting low Ni-compositions, LiNi 0.33 Co 0.33 Mn 0.33 O 2 (NMC111), into higher Ni-compositions (NMC622). This process forms a Ni-rich coating on cathode particles that diffuses into the core upon high-temperature relithiation, increasing compositional homogeneity. The upcycling process leverages existing infrastructure, offering low capital cost and minimal additional chemical input. Through ex situ tomographic transmission X-ray microscopy (TXM), 3D Ni:Co elemental mixing is quantified, confirming that elemental content evens at the secondary particle level with a mean NMC622 composition upon relithiation. However, elemental gradients remain in the single crystalline primary particles. Ex situ high-resolution and in situ wide-angle X-ray diffraction reveals concurrent structural changes during the relithiation process. These findings provide key insights into the structural and chemical mechanisms of elemental diffusion to obtain further improvements of increased capacity and retention through compositional conversion of cathode materials.

Cathode Upcycling↗
DOE OSTI2022

A Review of Computational Capabilities and Requirements in High-Resolution Simulation of Nonferrous Pyrometallurgical Furnaces

We report that tremendous progress has been made over the last several decades in improving the pyrometallurgical processing routes of nonferrous metals, such as aluminum, copper, and lead. Advances in the numerical modeling of pyrometallurgical processes has aided in these improvements by providing a better understanding of the complex transport phenomena occurring in modern furnaces. However, there is a need for a comprehensive discussion of the numerical modeling of a primary and a secondary nonferrous pyrometallurgical furnaces. This review provides such a discussion by surveying recent attempts at capturing the physico-chemical phenomena occurring within these furnaces, including gas-phase combustion, melting/smelting, and multiphase heat- and mass-transfer with the molten phases. This work then identifies a complete set of approaches for simulating these types of furnaces and provides recommendations for applying high-resolution numerical tools towards full-furnace simulation. By identifying gaps in the current state of the art, this review offers suggestions for future developments in commercial codebases, outlining the path to fulfilling the promise of CFD-enabled pyrometallurgy.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A staged deep learning approach to spatial refinement in 3D temporal atmospheric transport

High-resolution spatiotemporal simulations effectively capture the complexities of atmospheric plume dispersion in complex terrain. However, their high computational cost makes them impractical for applications requiring rapid responses or iterative processes, such as optimization, uncertainty quantification, or inverse modeling. To address this challenge, this work introduces the Dual-Stage Temporal Three-dimensional UNet Super-resolution (DST3D-UNet-SR) model, a highly efficient deep learning model for plume dispersion predictions. DST3D-UNet-SR is composed of two sequential modules: the temporal module (TM), which predicts the transient evolution of a plume in complex terrain from low-resolution temporal data, and the spatial refinement module (SRM), which subsequently enhances the spatial resolution of the TM predictions. We train DST3D-UNet-SR using a comprehensive dataset derived from high-resolution large eddy simulations (LES) of plume transport. We propose the DST3D-UNet-SR model to significantly accelerate LES of three-dimensional (3D) plume dispersion by three orders of magnitude. Additionally, the model demonstrates the ability to dynamically adapt to evolving conditions through the incorporation of new observational data, substantially improving prediction accuracy in high-concentration regions near the source.

3D temporal sequences↗
DOE OSTI2020

A self-supervised workflow for particle picking in cryo-EM

High-resolution single-particle cryo-EM data analysis relies on accurate particle picking. To facilitate the particle picking process, a self-supervised workflow has been developed. This includes an iterative strategy, which uses a 2D class average to improve training particles, and a progressively improved convolutional neural network for particle picking. To automate the selection of particles, a threshold is defined (%/Res) using the ratio of percentage class distribution and resolution as a cutoff. This workflow has been tested using six publicly available data sets with different particle sizes and shapes, and can automatically pick particles with minimal user input. The picked particles support high-resolution reconstructions at 3.0 Å or better. This workflow is a step towards automated single-particle cryo-EM data analysis at the stage of particle picking. It may be used in conjunction with commonly used single-particle analysis packages such as Relion , cryoSPARC , cisTEM , SPHIRE and EMAN2 .

2D class averages↗
DOE OSTI2020

Unraveling spectral shapes of adventitious carbon on gold using a time-resolved high-resolution X-ray photoelectron spectroscopy and principal component analysis

To extract chemical information reliably, we propose a new data processing method based both on the creation of information vectors and on a vector base change. The originality of this method is the combination of the different core XPS peaks, Auger and/or valence bands in a single vector. We show that the measurement of a sample upon its progressive chemical change, such as accumulation of the adventitious carbon, allows us to create a new vector basis using linear algebra and Principal Component Analysis. In this work, we demonstrate the application of this method using adventitious carbon films on Au foil utilizing Au 4f, C 1s and O 1s spectral envelopes. This method expands the possibilities of XPS measured chemical environment analysis and is an operator-unbiased solution for materials for which the reference XPS spectra are not available. In this demonstration, the emphasis is placed on identifying changes in a C 1s peak assumed to be adventitious carbon and highlight uncertainties associated with calibrating the binding energy scale using a simple peak model to identify a component C 1s assumed to be saturated hydrocarbon in origin.

36 MATERIALS SCIENCE↗
DOE OSTI2021

X-ray computed tomography-based porosity analysis: Algorithms and application for porous woody biomass

The properties of lignocellulosic biomass, such as bulk physical, thermal, and mechanical properties, as well as the mobility of enzymes or catalysts, are largely affected by porosity, pore structures, and pore size distribution. While X-ray computed tomography (CT) has been introduced to effectively produce 3D volumetric images of material microstructure, a quantitative porosity analysis from the 3D images has never been done. This work introduces a first-of-its-kind X-ray CT-based quantitative porosity analysis method and applies the developed toolkit to characterize the internal porosity distribution of loblolly pine. A sample loblolly pine chip was scanned by a nano-resolution X-ray CT system and digitally reconstructed after a sequence of image processing operations. Here, a comprehensive porosity analysis was then performed to quantify the envelope porosity, the spatial distribution of local porosity, and the directional porosity. Solutions to the various challenges in image processing, porosity calculation, and large data handling are provided.

42 ENGINEERING↗
DOE OSTI2024

Persistent global greening over the last four decades using novel long-term vegetation index data with enhanced temporal consistency

Advanced Very High-Resolution Radiometer (AVHRR) satellite observations have provided the longest global daily records from 1980s, but the remaining temporal inconsistency in vegetation index datasets has hindered reliable assessment of vegetation greenness trends. To tackle this, we generated novel global long-term Normalized Difference Vegetation Index (NDVI) and Near-Infrared Reflectance of vegetation (NIRv) datasets derived from AVHRR and Moderate Resolution Imaging Spectroradiometer (MODIS). We addressed residual temporal inconsistency through three-step post processing including cross-sensor calibration among AVHRR sensors, orbital drifting correction for AVHRR sensors, and machine learning-based harmonization between AVHRR and MODIS. After applying each processing step, we confirmed the enhanced temporal consistency in terms of detrended anomaly, trend and interannual variability of NDVI and NIRv at calibration sites. Our refined NDVI and NIRv datasets showed a persistent global greening trend over the last four decades (NDVI: 0.0008 yr -1 ; NIRv: 0.0003 yr -1 ), contrasting with those without the three processing steps that showed rapid greening trends before 2000 (NDVI: 0.0017 yr -1 ; NIRv: 0.0008 yr -1 ) and weakened greening trends after 2000 (NDVI: 0.0004 yr -1 ; NIRv: 0.0001 yr -1 ). These findings highlight the importance of minimizing temporal inconsistency in long-term vegetation index datasets, which can support more reliable trend analysis in global vegetation response to climate changes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Detailed Characterization of an Annealed Reduced Graphene Oxide Catalyst for Selective Peroxide Formation Activity

Recently, electrochemical hydrogen peroxide (H 2 O 2 ) generation from oxygen molecules has been extensively studied. Thus far, the best peroxide activity under alkaline conditions has been reported at the surface of a mild reduced graphene oxide annealed at 600 °C (mrGO-600). However, the detailed material information, such as chemical functionality and structural morphology, is unknown, which results in ambiguous debates on its catalytic active sites. Therefore, to solve this problem, we intensively characterize the structure of mrGO-600 to clarify the origin of its catalytic activity. Various characterizations, including X-ray photoelectron spectroscopy, Raman spectroscopy, infrared spectroscopy, near-edge X-ray absorption fine spectroscopy, and high-resolution transmittance electron microscopy coupled with in situ infrared spectroelecrochemistry, reveal that the annealing process generates not only various hole edge defects that are related to the ring ether group but also numerous point defects that result in a small-sized disconnected graphitic carbon region. These defects are believed to form a unique atomic level configuration in mrGO-600, which enables it to facilitate high peroxide-generated activity from oxygen molecules in an alkaline electrolyte.

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

Spatiotemporal Observation of Quasi-Ballistic Transport of Electrons in Graphene

We report spatiotemporal observations of room-temperature quasi-ballistic electron transport in graphene, which is achieved by utilizing a four-layer van der Waals heterostructure to generate free charge carriers. The heterostructure is formed by sandwiching a MoS 2 and MoSe 2 heterobilayer between two graphene monolayers. Transient absorption measurements reveal that the electrons and holes separated by the type-II interface between MoS 2 and MoSe 2 can transfer to the two graphene layers, respectively. Transient absorption microscopy measurements, with high spatial and temporal resolution, reveal that while the holes in one graphene layer undergo a classical diffusion process with a large diffusion coefficient of 65 cm 2 s –1 and a charge mobility of 5000 cm 2 V –1 s –1 , the electrons in the other graphene layer exhibit a quasi-ballistic transport feature, with a ballistic transport time of 20 ps and a speed of 22 km s –1 , respectively. The different in-plane transport properties confirm that electrons and holes move independently of each other as charge carriers. Furthermore, the optical generation of ballistic charge carriers suggests potential applications for such van der Waals heterostructures as optoelectronic materials.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2020

Autonomous adaptive data acquisition for scanning hyperspectral imaging

Non-invasive and label-free spectral microscopy (spectromicroscopy) techniques can provide quantitative biochemical information complementary to genomic sequencing, transcriptomic profiling, and proteomic analyses. However, spectromicroscopy techniques generate high-dimensional data; acquisition of a single spectral image can range from tens of minutes to hours, depending on the desired spatial resolution and the image size. This substantially limits the timescales of observable transient biological processes. To address this challenge and move spectromicroscopy towards efficient real-time spatiochemical imaging, we developed a grid-less autonomous adaptive sampling method. Our method substantially decreases image acquisition time while increasing sampling density in regions of steeper physico-chemical gradients. When implemented with scanning Fourier Transform infrared spectromicroscopy experiments, this grid-less adaptive sampling approach outperformed standard uniform grid sampling in a two-component chemical model system and in a complex biological sample, Caenorhabditis elegans. We quantitatively and qualitatively assess the efficiency of data acquisition using performance metrics and multivariate infrared spectral analysis, respectively.

47 OTHER INSTRUMENTATION↗