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At least 1,063 records · Page 59

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DOE OSTI2020

Probing local electrochemistry via mechanical cyclic voltammetry curves

Understanding the mechanical response of an electrode during electrochemical cycling and its correlation to the device electrochemical performance is crucial to improving the performance of insertion-type energy storage devices, electrochemical actuators, water purification, ion separation and neuromorphic computing applications. Here, we visualized the electro-chemo-mechanical coupling behaviors during charge storage of anhydrous and hydrated WO 3 electrodes via in situ atomic force microscopy (AFM) and developed the concept of mechanical cyclic voltammetry (mCV) curves. The relationship between electrochemical current and strain was investigated with simplified models and the results revealed that the proton insertion/deinsertion process could be described through potential-dependent electro-chemo-mechanical coupling coefficients which might indicate changes in insertion processes during electrode cycling. The mCV mapping results highlight the local heterogeneity and show that the charging processes varied across the electrode. These local variations could be further correlated to local morphology, crystal orientations or chemical compositions with proper electrode designs.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Direct observation of elemental fluctuation and oxygen octahedral distortion-dependent charge distribution in high entropy oxides

The enhanced compositional flexibility to incorporate multiple-principal cations in high entropy oxides (HEOs) offers the opportunity to expand boundaries for accessible compositions and unconventional properties in oxides. Attractive functionalities have been reported in some bulk HEOs, which are attributed to the long-range compositional homogeneity, lattice distortion, and local chemical bonding characteristics in materials. However, the intricate details of local composition fluctuation, metal-oxygen bond distortion and covalency are difficult to visualize experimentally, especially on the atomic scale. Here, we study the atomic structure-chemical bonding-property correlations in a series of perovskite-HEOs utilizing the recently developed four-dimensional scanning transmission electron microscopy techniques which enables to determine the structure, chemical bonding, electric field, and charge density on the atomic scale. The existence of compositional fluctuations along with significant composition-dependent distortion of metal-oxygen bonds is observed. Consequently, distinct variations of metal-oxygen bonding covalency are shown by the real-space charge-density distribution maps with sub-ångström resolution. The observed atomic features not only provide a realistic picture of the local physico-chemistry of chemically complex HEOs but can also be directly correlated to their distinctive magneto-electronic properties.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Biomimetic tissue phantoms for neurosurgical near-infrared fluorescence imaging

Neurosurgical fluorescence imaging is a well-established clinical approach with a growing range of indications for use. However, this technology lacks effective phantom-based tools for development, performance testing, and clinician training. Our primary aim was to develop and evaluate 3D-printed phantoms capable of optically and morphologically simulating neurovasculature under fluorescence angiography. Volumetric digital maps of the circle of Willis with basilar and posterior communicator artery aneurysms, along with surrounding cerebral tissue, were generated. Phantoms were fabricated with a stereolithography printer using custom photopolymer composites, then visualized under white light and near-infrared fluorescence imaging. Feature sizes of printed components were found to be within 13% of digital models. Phantoms exhibited realistic optical properties and convincingly recapitulated fluorescence angiography scenes. Methods identified in this study can facilitate the development of realistic phantoms as powerful new tools for fluorescence imaging.

3D modeling↗

NASA NTRS1981

Assessment of Superflux relative to remote sensing

The state-of-the-art advancements in remote sensor technology due to the Superflux program are examined. Three major individual sensor technologies benefitted from the program: laser fluorosensors, optical-range scanners, and passive microwave sensors. Under Superflux, convincing evidence was obtained that the airborne oceanographic lidar fluorosensor can map chlorophyll, i.e., is linear, over a wide range from less than 0.5 to 5.0 mg/cu m. The lidar oceanographic probe dual-excitation concept for addressing phytoplankton color group composition was also demonstrated convincingly. Algorithm development, real time capabilities, and multisensor integration are also addressed.

Campbell, J. W.↗

NASA NTRS1990

Synthetic-aperture-radar imaging of the ocean surface using the slightly-rough facet model and a full surface-wave spectrum

A new model of synthetic-aperture-radar (SAR) imaging of ocean waves is described. The model is based on mapping individual, slightly-rough surface facets through the SAR processor into the image and responses of the facets in the image domain are added together coherently to give the composite image. A windowing technique allows both the orbital motion and the phase velocity of the long waves to be included. It is determined that the azimuthal cut-off is due to a smearing of the response of the facets in the image induced by the random orbital motion of the intermediate large-scale waves and that the focus adjustment that gives the greatest image contrast is half the phase velocity of the dominant long wave. The optimal processing technique, however, may consist of spatially offsetting the multiple looks on the image domain to compensate the propagation of long waves during the integration time of the SAR.

West, James C.↗

NASA NTRS1995

Silicon Detector Studies with an Interferometric Thickness Mapper

Cosmic ray isotopic composition studies aboard satellites are normally based on energy detection measurements which require a precise knowledge of matter thickness particle penetration. A laser- interferometer system has been developed to precisely map the thick- ness variations of large-area silicon detectors. Design, operation, and the data processing to derive thickness maps is described.

Cosmic Rays Silicon Detectors Interferometry Satel↗

NASA NTRS2016

The Mapping X-Ray Fluorescence Spectrometer (MAPX)

MapX will provide elemental imaging at ≤100 micron spatial resolution over 2.5 X 2.5 centimeter areas, yielding elemental chemistry at or below the scale length where many relict physical, chemical, and biological features can be imaged and interpreted in ancient rocks. MapX is a full-frame spectroscopic imager positioned on soil or regolith with touch sensors. During an analysis, an X-ray source (tube or radioisotope) bombards the sample surface with X-rays or αlpha-particles / gamma rays, resulting in sample X-ray Fluorescence (XRF). Fluoresced X-rays pass through an X-ray lens (X-ray μ-Pore Optic, "MPO") that projects a spatially resolved image of the X-rays onto a CCD. The CCD is operated in single photon counting mode so that the positions and energies of individual photons are retained. In a single analysis, several thousand frames are stored and processed. A MapX experiment provides elemental maps having a spatial resolution of ≤100 micron and quantitative XRF spectra from Regions of Interest (ROI) 2 centimers ≤ x ≤ 100 micron. ROI are compared with known rock and mineral compositions to extrapolate the data to rock types and putative mineralogies. The MapX geometry is being refined with ray-tracing simulations and with synchrotron experiments at SLAC. Source requirements are being determined through Monte Carlo modeling and experiment using XMIMSIM [1], GEANT4 [2] and PyMca [3] and a dedicated XRF test fixture. A flow-down of requirements for both tube and radioisotope sources is being developed from these experiments. In addition to Mars lander and rover missions, MapX could be used for landed science on other airless bodies (Phobos/Deimos, Comet nucleus, asteroids, the Earth's moon, and the icy satellites of the outer planets, including Europa.

Blake, David↗

NASA NTRSDate not supplied

Applications of NASA’s LANCE Earth Observations for Ecosystem Assessments

As one of NASA's open and free data systems, NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE) provides a wide range of near real-time, low latency and expedited data products from NASA and other earth science satellite missions to support users in their understanding of the value and distribution of ecosystem services. LANCE data can support the System of Environmental Economic Accounting-Ecosystem Accounting (SEEA-EA) framework by both facilitating the creation of ecosystem spatial extent and condition accounts, as well as the quantification and mapping of ecosystem services, much more quickly than routine data processing allows. During the past 13 years, LANCE near real-time satellite data products (e.g., surface reflectance, albedo, vegetation height, thermal anomalies, soil moisture, snow cover etc.) have been used to produce ecosystem-related indicators such as vegetation indexes, biomass, land cover maps, fire, and flood products. These ecosystem-related indicators can be integrated into ecosystem services models, tools, and platforms to monitor land cover and land use changes in ecosystem composition in support of land and natural resource management. When using ecosystem services accounts in decision-making, low latency is essential because having up-to-date information can be critical to support decisions being made. With adequate spatial resolution, long time series length, and low latency data products, LANCE would promote the sustainable use of land and natural resources.

Tian Yao↗

DOE OSTI2026

Synchrotron resolved microscale mineralogy and elemental composition of individual plinthic and manganiferous nodules from agricultural soils

Iron and manganese oxides are strong sorbents and elemental scavengers in the environment that can form macroscopic nodules in soils. Despite their importance in the geochemical cycling of nutrients and contaminants, nodules are rarely characterized. When analyzed, they are often combined into bulk samples, obscuring individual characteristics. This study investigates the composition and mineralogy of individual plinthic and manganiferous nodules in soil from cropping systems in the Farming Systems Project in Beltsville, Maryland. Elemental composition indicates the accumulation of contaminants and nutrients, including copper in all nodules and high levels of phosphorus in select nodules. Sequential extractions determine that highly crystalline minerals comprise the nodules, locking away these nutrients and contaminants. Bulk X-ray diffraction (XRD) demonstrated distinct mineralogies in the plinthic and manganiferous nodules, with the former comprised of quartz, goethite, and kaolinite, and the latter consisting of quartz, Mn 2 O 3 , and hematite. Synchrotron and laboratory-based X-ray fluorescence (XRF) mapping gives critical insight into the elemental distribution within the nodules and elucidates spatial elemental associations, including the coupling of manganese and iron in the manganiferous nodules. XRF maps paired with µXRD provide microscale spatial resolution in mineralogy, which resolved several phases not apparent in bulk XRD, manganese’s association with goethite, and minerals indicative of redox transformations, including magnetite and maghemite. These novel results provide critical insight into the microscale mineralogy and individual elemental composition of plinthic and manganiferous nodules, as well as how they alter bulk soil geochemistry in ways largely unaccounted for in research on soil systems.

36 MATERIALS SCIENCE↗

NASA NTRS2020

Land-cover Change in the Caucasus Mountains Since 1987 Based on the Topographic Correction of Multi-temporal Landsat Composites

Mountainous regions are changing rapidly across the world due to both land-use change and climate change. Given the importance of mountainous regions for ecosystem services and endemic biodiversity, monitoring these changes is essential. Satellite data provide a great resource to map land-cover change in mountainous regions, however mapping is especially challenging there because topographic complexity affects reflectance. The so called ‘topographic effect’ has been successfully corrected for in case studies of small areas, but a comparison of large-area classifications and land-cover change analyses with and without topographic correction is missing. Here, we performed a long-term land-cover change assessment for a large mountainous region, i.e., the Caucasus Mountains with topographic correction. Our two goals were 1) to examine the effect of topographic correction on land-cover classification for a large mountainous region, and 2) to assess land-cover changes since 1987 across the Caucasus based on the full Landsat archive. Both the complex topography and the history of land-use changes, especially after the collapse of the Soviet Union in 1991, make the Caucasus Mountains an ideal study area to understand topographic effects on large-area land-cover mapping for the last three decades. First, we compared a non-topographically-corrected Landsat classification for 2015 with a classification that was topographically-corrected with an enhanced C-correction for the same year and assessed the accuracy of both. Second, we derived topographically-corrected Landsat classifications for six dates to assess changes in cropland and forest from 1987 to 2015, based on class probabilities and post-classification comparisons. In regard to our first goal, topographic correction improved the overall accuracy of the classification only by 2% (from 79 to 81%), but disagreement rates were as high as 100% in mountainous regions, especially among forest types. In regard to our second goal, we found that cropland loss was the most prevalent change process since 1987. Cropland loss was particularly widespread in Georgia and Armenia until 2000, and in Azerbaijan until 2005. The North Caucasus (the Russian Federation) had more stable cropland over time, most likely due to different land reforms after the collapse of the Soviet Union, and the prevalence of flat landscapes and very fertile soils, which make cultivation easier than in the South Caucasus. Rates of forest change throughout the Caucasus Mountains were surprisingly low, with forest loss and forest gain was most likely due to cropland abandonment and less grazing pressure. Our results highlight both the importance and the feasibility of topographic correction for accurate large-area land-cover classifications in steep terrain.

Large-area mapping↗

NASA NTRS2015

On the Feasibility of Monitoring Carbon Monoxide in the Lower Troposphere from a Constellation of Northern Hemisphere Geostationary Satellites (PART 1)

By the end of the current decade, there are plans to deploy several geostationary Earth orbit (GEO) satellite missions for atmospheric composition over North America, East Asia and Europe with additional missions proposed. Together, these present the possibility of a constellation of geostationary platforms to achieve continuous time-resolved high-density observations over continental domains for mapping pollutant sources and variability at diurnal and local scales. In this paper, we use a novel approach to sample a very high global resolution model (GEOS-5 at 7 km horizontal resolution) to produce a dataset of synthetic carbon monoxide pollution observations representative of those potentially obtainable from a GEO satellite constellation with predicted measurement sensitivities based on current remote sensing capabilities. Part 1 of this study focuses on the production of simulated synthetic measurements for air quality OSSEs (Observing System Simulation Experiments). We simulate carbon monoxide nadir retrievals using a technique that provides realistic measurements with very low computational cost. We discuss the sampling methodology: the projection of footprints and areas of regard for geostationary geometries over each of the North America, East Asia and Europe regions; the regression method to simulate measurement sensitivity; and the measurement error simulation. A detailed analysis of the simulated observation sensitivity is performed, and limitations of the method are discussed. We also describe impacts from clouds, showing that the efficiency of an instrument making atmospheric composition measurements on a geostationary platform is dependent on the dominant weather regime over a given region and the pixel size resolution. These results demonstrate the viability of the "instrument simulator" step for an OSSE to assess the performance of a constellation of geostationary satellites for air quality measurements.

GEOS-5↗

DOE OSTI2022

Development of green-colour-emitting pyrotechnics as a core for 3D temperature imaging sensors inside coal boilers

Efficiency in the control operation of the boilers for coal and coal with biomass can be further improved if the flue gas temperature distribution can be better characterized. This is very difficult in these harsh environmental systems, where spatially resolved measurements are nearly impossible with solid-state sensors. Here, in this work, we evaluate the development of pyrotechnic compounds that would serve as the basis for a novel optical mapping of the temperature inside coal boilers. For this purpose, various green-colour-emitting pyrotechnics using BaCl 2 · 2H 2 O and Ba(NO 3 ) 2 as the green light source were prepared, as this colour offers a distinct signal from the combustion-based background in the boiler. These pyrotechnics were characterized using thermogravimetric analysis (TGA) and X-ray diffraction (XRD) and tested using a flat-flame burner. Furthermore, the composition was varied to evaluate the effect of different metal fuels such as Sn, Co, and Mg, as well as various binders such as ethylcellulose, shellac, parlon, and PVC on green light emission. The emission intensity and the apparent ignition temperature were strongly dependent on the metal type, with Mg showing higher intensities. On the other hand, the effect of the binder showed that the ignition behaviour, emission intensity, and spectral purity were influenced by the nature and exothermicity of the binder. The addition of other potential green light-producing materials, such as boric acid, increased the intensity of emission by 17% for a BaCl 2 · 2H 2 O-based composition. This study identified prospective compositions with intense and bright green-colour emissions that have high spectral purities.

42 ENGINEERING↗

NASA NTRS1977

Thermal inertia mapping - A new view of the earth

The thermal response of a substance to a time-varying surface power input is determined by its thermal inertia. Remote sensing (e.g., from satellites) can be utilized to measure this property, which is related to surface composition or to near-surface soil moisture. An algorithm is developed which relates thermal inertia to remote measurements of surface temperature and reflectance. Application to geosynchronous satellite data illustrates the contrast between irrigated and desert areas in the region north of the Gulf of California. The effect of local weather conditions (latent and sensible heat transfer to the atmosphere) must be estimated before precise values for thermal inertia can be specified.

Price, J. C.↗

DOE OSTI2024

Neural network kinetics for exploring diffusion multiplicity and chemical ordering in compositionally complex materials

Diffusion involving atom transport from one location to another governs many important processes and behaviors such as precipitation and phase nucleation. The inherent chemical complexity in compositionally complex materials poses challenges for modeling atomic diffusion and the resulting formation of chemically ordered structures. Here, we introduce a neural network kinetics (NNK) scheme that predicts and simulates diffusion-induced chemical and structural evolution in complex concentrated chemical environments. The framework is grounded on efficient on-lattice structure and chemistry representation combined with artificial neural networks, enabling precise prediction of all path-dependent migration barriers and individual atom jumps. To demonstrate the method, we study the temperature-dependent local chemical ordering in a refractory NbMoTa alloy and reveal a critical temperature at which the B2 order reaches a maximum. The atomic jump randomness map exhibits the highest diffusion heterogeneity (multiplicity) in the vicinity of this characteristic temperature, which is closely related to chemical ordering and B2 structure formation. The scalable NNK framework provides a promising new avenue to exploring diffusion-related properties in the vast compositional space within which extraordinary properties are hidden.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Modeling Single and Two-Phase Transport in Thin Porous Layers Using a Composite Continuum-Pore Network Formulation

In this work, a composite continuum-pore network formulation is presented to model single and two-phase transport thin porous layers, such as gas diffusion layers in polymer electrolyte fuel cells (PEFCs) and active electrodes in redox flow batteries (RFBs). The formulation can be integrated into CFD codes, thus combining the ease of implementation of continuum-based modeling and the computational power of pore-network modeling. The composite model includes a control volume (CV) mesh at the layer scale, which embeds a cubic pore network [1,2]. The pore-network model is used to determine analytically local anisotropic effective transport properties (local effective diffusivity and permeability), which are mapped onto the CV mesh to simulate transport in the porous transport layer [3,4]. Good agreement is found between the predicted global effective transport properties (global effective diffusivity and permeability) under dry and wet conditions and previous experimental data reported in the literature for Toray TGP-H series carbon paper. Water saturation distributions are also compared with results obtained using X-ray computed tomography [4]. [1] P.A. García-Salaberri, I.V. Zenyuk, J.T. Gostick, A.Z. Weber, Modeling Gas Diffusion Layer in Polymer Electrolyte Fuel Cells Using a Continuum-Based Pore-Network Formulation, ECS Trans. 97 (2020) 615. [2] P.A. García-Salaberri, Modeling diffusion and convection in thin porous transport layers using a composite continuum-network model: Application to gas diffusion layers in polymer electrolyte fuel cells, Int J. Heat Mass Transf. (2020), submitted. [3] P.A. García-Salaberri, J.T. Gostick, G. Hwang, A.Z. Weber, M. Vera, Effective diffusivity in partially-saturated carbon-fiber gas diffusion layers: Effect of local saturation and application to macroscopic continuum models, J. Power Sources 296 (2015) 440–453. [4] P.A. García-Salaberri, G. Hwang, M. Vera, A.Z. Weber, J.T. Gostick, Effective diffusivity in partially-saturated carbon-fiber gas diffusion layers: Effect of through-plane saturation distribution, Int. J. Heat Mass Transf. 86 (2015) 319–333.

Garcia-Salaberri, Pablo↗

DOE OSTI2021

A generalized machine learning workflow to visualize mechanical discontinuity

Accurate detection and mapping of mechanical discontinuity in materials has widespread industrial and research applications. Herein, we developed a generalized machine-learning framework for visualizing single mechanical discontinuity embedded in material of any composition, velocity, density, porosity, and size with limited data. The proposed visualization of discontinuity requires accurate estimations of the length, location, and orientation of the embedded discontinuity by processing multipoint wave-transmission measurements. k-Wave simulator is used to create a large dataset of elastic waveforms recorded during multi-point wave-transmission measurements through materials containing single mechanical discontinuity. k-Wave simulator considers the wave attenuation, dispersion, and mode conversion in wave motion. Discrete wavelet transform (DWT) and statistical feature extraction are essential for data preprocessing prior to the data-driven model development. DWT also minimizes the effect of noise. Using hyper-parameter tuning and cross validation, gradient boosting regression can visualize the mechanical discontinuity with an accuracy of 0.85, in terms of coefficient of determination. A double-layered neural network-based regression has better performance with an accuracy of 0.95. Use of convolutional neural network converts the predictive task from a waveform processing to an image processing problem. Convolutional neural network achieved a generalization performance of 0.91. The proposed generalized workflow requires robust simulation of wave propagation, signal processing, feature engineering, and model evaluation. Sensors closest to the source and those located opposite the source are the most significant for the desired visualization. Notably, the sensors closest to the source capture the non-linear associations, whereas the sensor on the border opposite to the source capture the linear associations between the measured waveforms and the properties of the mechanical discontinuity.

42 ENGINEERING↗

DOE OSTI2025

Probing the Mechanism of Selective Phosphate Adsorption from Wastewater Using Aqueous and Synchrotron X-ray Characterization

Ion exchange shows promise for recovering phosphate from wastewater as value-added products, but requires high phosphate selectivity to compete with conventional treatment. Hybrid anion exchange (HAIX) resins, which contain non-selective basic functional groups and selective iron oxide nanoparticles (FeOnp), can effectively remove phosphate from wastewater. However, knowledge gaps remain regarding the mechanisms of phosphate selectivity and influence of competing ions, hindering needed efforts to model adsorption dynamics and design scalable adsorption processes for varying wastewaters. To address these gaps, we integrated aqueous-phase adsorption analysis with solid-phase, synchrotron-based X-ray characterization; this integration facilitated elucidation of the distribution and speciation of iron, phosphate, and competing anions on HAIX resins. We compared a quaternary ammonium-functionalized HAIX resin (SBA) to a tertiary amine version (WBA) to determine the role of functional groups. X-ray radiography revealed differences in FeOnp speciation (goethite vs. ferrihydrite) and distribution (peripheral vs. homogeneous) between the resins, resulting in varied phosphate affinity and intraparticle diffusion resistance. Using micro-X-ray fluorescence (μ-XRF) and micro-X-ray absorption near-edge structure (μ-XANES) spectroscopy, we identified differences in where and how phosphate binds across resin types and wastewaters. Across wastewater compositions, FeOnp sites in WBA contribute more to phosphate adsorption than in SBA, possibly due to variations in Fe distribution and speciation. Phosphate adsorption densities calculated from quantitative μ-XRF maps matched those from aqueous analysis, demonstrating the effectiveness of this integrated approach. Altogether, results demonstrate the use of synchrotron-based X-ray characterization for investigating adsorption mechanisms and advance HAIX as a phosphate recovery technology from complex wastewaters.

Nutrient recovery↗

DOE OSTI2024

Revealing the hidden structure of disordered materials by parameterizing their local structural manifold

Abstract Durable interest in developing a framework for the detailed structure of glassy materials has produced numerous structural descriptors that trade off between general applicability and interpretability. However, none approach the combination of simplicity and wide-ranging predictive power of the lattice-grain-defect framework for crystalline materials. Working from the hypothesis that the local atomic environments of a glassy material are constrained by enthalpy minimization to a low-dimensional manifold in atomic coordinate space, we develop a generalized distance function, the Gaussian Integral Inner Product (GIIP) distance, in connection with agglomerative clustering and diffusion maps, to parameterize that manifold. Applying this approach to a two-dimensional model crystal and a three-dimensional binary model metallic glass results in parameters interpretable as coordination number, composition, volumetric strain, and local symmetry. In particular, we show that a more slowly quenched glass has a higher degree of local tetrahedral symmetry at the expense of cyclic symmetry. While these descriptors require post-hoc interpretation, they minimize bias rooted in crystalline materials science and illuminate a range of structural trends that might otherwise be missed.

36 MATERIALS SCIENCE↗