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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

NETL Plastic Pipes Project (Final Report)

Plastic or composite pipelines have been the bane of the utility locating industry because they are neither conductive nor magnetic which are the properties traditionally used to locate buried utilities. Ground penetrating radar (GPR) is an effective geophysical tool for locating plastic/composite pipelines where resistive cover allows for adequate penetration of radar energy. However, GPR has limited applicability in areas where the soil cover is conductive due to significant clay and/or salt content. This study examines complementary near-surface geophysical methods that are potentially useful for locating buried plastic/composite pipelines, either singly or in combination. Specifically, this modeling study used computational numerical methods to forward model the response of GPR, resistivity, seismic, gravity gradiometry, and photoacoustic/thermoacoustic imaging methods to plastic/composite pipelines for various scenarios including: (1) pipe diameters ranging between 2 in. to 12 in.; (2) burial depths ranging between 3 ft. to 4 ft.; (3) various degrees in contrast in physical properties (i.e., electrical permittivity, elasticity, resistivity, density); and (4) various experimental acquisition choices (e.g., GPR radar and seismic source frequencies, electrode spacing). Numerical modeling performed herein reconfirmed that GPR is the preferred method for detecting/locating plastic pipelines. A caveat for GPR detection is that the material covering the plastic pipe (trench fill material and adjacent soil) must be sufficiently resistive to allow the two-way propagation to the required depth of investigation and back to the surface. GPR was the only method modeled in this study that can be used to directly detect plastic pipelines of 2-in.-diameter and larger when buried 3-ft-deep. GPR data processing and imaging also can determine pipe depth, pipe diameter, trench dimensions, and moisture conditions. Seismic modeling results suggest that direct detection of a 12-in.-diameter plastic pipe at 3-ft.-depth may be possible under favorable conditions; however, the associated signature would be weak (e.g., surface- to S-wave, backscattered surface-waves, and/or forward scattered surface-waves to S-wave). Direct pipe detection under field conditions with noise and strong lateral geologic heterogeneity is doubtful. Numerical modeling also suggests that plastic pipelines can be indirectly located by detecting the trench in which they are buried. GPR, direct current (DC) resistivity, and seismic methods have the potential to locate the pipeline trench if there is sufficient contrast between the trench-wall and trench-fill materials for the physical property being measured by each method (i.e., electrical permittivity for GPR; resistivity for DC resistivity; or density, compressional velocity, or shear velocity for seismic). Modeling also indicated that currently available (commercial) gravity gradiometers would be unable to directly detect/locate plastic pipelines ≤ 8-in.-diameter when buried 3-ft.-deep given the typical instrument noise floor for field surveying as well as the expected density variations due to geologic heterogeneity. The numerical modeling performed in this project did not identify a universal geophysical technology that can locate buried plastic pipelines in all parts of the United States (although GPR is suggested for all areas with resistive cover). However, the project results suggest that a towed land streamer simultaneously acquiring multiple geophysical data types including multi-offset GPR, multi-channel DC resistivity, seismic geophone- and/or distributed acoustic sensing (DAS), and potentially photoacoustic/thermoacoustic data would be an appropriate platform for locating buried plastic pipeline. Moreover, the complementary multiphysics data acquired by a towed land streamer would permit the use of joint and/or cooperative inversion frameworks for a more rigorous and consistent data interpretation.

42 ENGINEERING↗
DOE OSTI2021

Microsecond fingerprint stimulated Raman spectroscopic imaging by ultrafast tuning and spatial-spectral learning

Label-free vibrational imaging by stimulated Raman scattering (SRS) provides unprecedented insight into real-time chemical distributions. Specifically, SRS in the fingerprint region (400–1800 cm -1 ) can resolve multiple chemicals in a complex bio-environment. However, due to the intrinsic weak Raman cross-sections and the lack of ultrafast spectral acquisition schemes with high spectral fidelity, SRS in the fingerprint region is not viable for studying living cells or large-scale tissue samples. Here, we report a fingerprint spectroscopic SRS platform that acquires a distortion-free SRS spectrum at 10 cm -1 spectral resolution within 20 µs using a polygon scanner. Meanwhile, we significantly improve the signal-to-noise ratio by employing a spatial-spectral residual learning network, reaching a level comparable to that with 100 times integration. Collectively, our system enables high-speed vibrational spectroscopic imaging of multiple biomolecules in samples ranging from a single live microbe to a tissue slice.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

A Multi-Objective Bayesian Optimized Human Assessed Multi-Target Generated Spectral Recommender System for Rapid Pareto Discoveries of Material Properties

Optimization for different tasks like material characterization, synthesis, and functional properties for desired applications over multi-dimensional control parameter and function spaces need a rapid strategic search through active learning. However, in all cases prior to optimization, the target material properties are assumed known and fixed, which mostly deviates from real-world scenarios in material synthesis. This can be critical for running expensive experiments on new materials, when the experimental results are fuzzy for any scientific outcomes due to improper target setting, ultimately wasting time and cost. The failure rate and cost are even higher over exploring on multi-target space, where we want to learn the pareto among multiple properties, to jointly optimize during material synthesis for desired applications. To address the challenge, here we introduce the human-operator attempt flexibility in the active learning based automated experiment framework, with generating multiple human assessed targets through a voting-based recommender system during real-time microscope measurements over the large material image space, sequentially learn/update multiple desired targets through a weighting system, and adaptively search in multiple material properties functional space for non-dominated pareto discoveries to maximize the custom structural similarity based acquisition function. We term this a multi-objective Bayesian optimized human assessed multi-target generated spectral recommender systems (MOBO-HAM-SRS). The approach has been demonstrated to peizoresponse force spectroscopy of a ferroelectric thin film, exploring with different kernels and acquisition functions. This work shows an advancement towards human-AI collaborated automated experiments, steering optimization trajectories through human overpowering AI at the early stage when uncertainty is high and AI overpowering human at the later stage with rapid exploration towards optimal goal, following human-assessed multiple targets properties.

Biswas, Arpan↗
DOE OSTI2022

Iron acquisition and mineral transformation by cyanobacteria living in extreme environments

Iron is an essential micronutrient for most living organisms, including cyanobacteria. These microorganisms have been found in Earth's driest polar and non-polar deserts, including the Atacama Desert, Chile. Iron-containing minerals were identified in colonized rock substrates from the Atacama Desert, however, the interactions between microorganisms and iron minerals remain unclear. In the current study, we determined that colonized gypsum rocks collected from the Atacama Desert contained both magnetite and hematite phases. A cyanobacteria isolate was cultured on substrates consisting of gypsum with embedded magnetite nanoparticles. Transmission electron microscopy imaging revealed a significant reduction in the size of magnetite nanoparticles due to their dissolution, which occurred around the microbial biofilms. Concurrently, hematite was detected, likely from the oxidation of the magnetite nanoparticles. Higher cell counts and production of siderophores were observed in cultures with magnetite nanoparticles suggesting that cyanobacteria were actively acquiring iron from the magnetite nanoparticles. Magnetite dissolution and iron acquisition by the cyanobacteria was further confirmed using large bulk magnetite crystals, uncovering a survival strategy of cyanobacteria in these extreme environments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Extracellular filaments revealed by affinity capture cryogenic-electron tomography

Cryogenic-electron tomography (cryo-ET) has provided an unprecedented glimpse into the nanoscale architecture of cells by combining cryogenic preservation of biological structures with electron tomography. Micropatterning of extracellular matrix proteins is increasingly used as a method to prepare adherent cell types for cryo-ET as it promotes optimal positioning of cells and subcellular regions of interest for vitrification, cryo-focused ion beam (cryo-FIB) milling, and data acquisition. Here we demonstrate a micropatterning workflow for capturing minimally adherent cell types, human T cells and Jurkat cells, for cryo-FIB and cryo-ET. Our affinity capture system facilitated the nanoscale imaging of Jurkat cells, revealing extracellular filamentous structures. It improved workflow efficiency by consistently producing grids with a sufficient number of well-positioned cells for an entire cryo-FIB session. Affinity capture can be extended to facilitate high-resolution imaging of other adherent and non-adherent cell types with cryo-ET.

Biochemistry↗
DOE OSTI2021

Influence of soil heterogeneity on soybean plant development and crop yield evaluated using time-series of UAV and ground-based geophysical imagery

Understanding the interactions among agricultural processes, soil, and plants is necessary for optimizing crop yield and productivity. This study focuses on developing effective monitoring and analysis methodologies that estimate key soil and plant properties. These methodologies include data acquisition and processing approaches that use unmanned aerial vehicles (UAVs) and surface geophysical techniques. In particular, we applied these approaches to a soybean farm in Arkansas to characterize the soil–plant coupled spatial and temporal heterogeneity, as well as to identify key environmental factors that influence plant growth and yield. UAV-based multitemporal acquisition of high-resolution RGB (red–green–blue) imagery and direct measurements were used to monitor plant height and photosynthetic activity. We present an algorithm that efficiently exploits the high-resolution UAV images to estimate plant spatial abundance and plant vigor throughout the growing season. Such plant characterization is extremely important for the identification of anomalous areas, providing easily interpretable information that can be used to guide near-real-time farming decisions. Additionally, high-resolution multitemporal surface geophysical measurements of apparent soil electrical conductivity were used to estimate the spatial heterogeneity of soil texture. By integrating the multiscale multitype soil and plant datasets, we identified the spatiotemporal co-variance between soil properties and plant development and yield. Our novel approach for early season monitoring of plant spatial abundance identified areas of low productivity controlled by soil clay content, while temporal analysis of geophysical data showed the impact of soil moisture and irrigation practice (controlled by topography) on plant dynamics. Our study demonstrates the effective coupling of UAV data products with geophysical data to extract critical information for farm management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Deep Learning Super-Resolution X-Ray Computed Tomography Algorithms for Additive Manufacturing

Industrial X-ray computed tomography (XCT) is a nondestructive method for inspection and characterization of additively manufactured (AM) materials and parts. In practice, the resolution of XCT can be limited by factors such as detector binning, restricted field of view for large-scale objects, system blur, motion during scanning, and acquisition settings. These limitations can reduce the detectability of critical flaws such as pores, cracks, and lack of fusion. Super-resolution (SR) techniques offer a promising solution for improving the effective resolution and image quality of XCT reconstructions without the need for expensive hardware upgrades or laborious, time-consuming scans. In particular, deep learning-based SR methods have garnered attention in recent years as powerful tools for reconstructing high-resolution volumes from low-resolution inputs. In this work, a novel deep learning-based SR method is proposed for XCT scans of AM parts, and compared against several existing state-of-the-art (SOTA) methods. The proposed method, Simurgh-SR, is built on the pre-existing Simurgh framework and consists of a 2.5D U-Net trained to map low-quality inputs containing noise and artifacts to high-quality reconstructions characterized by higher flaw contrast, better noise texture, and reduced artifacts. The experimental results demonstrate superior performance of Simurgh-SR in performing 4× SR on real industrial XCT scans of thick 316L components, enhancing the structural similarity score and peak signal-to-noise ratio (>7dB) compared to the LR counterpart while improving the F1-score for flaw detection by more than 2.3× when compared to alternative SOTA SR methods. This improvement enables more accurate and significantly faster characterization of metal AM components. Additionally, Simurgh-SR was trained for both 2X and 4X SR and performs effectively at both levels, enabling the use of a single model for various SR factors.

Rahman, Obaid [ORNL] (ORCID:0000000277810840)↗
DOE OSTI2022

CSEM Fluid Monitoring Methodology Using Real Data Examples

Conference presentation at International Meeting for Applied Geoscience & Energy (IMAGE), Houston, Texas, August 28 – September 1, 2023. Using field data from hydrocarbon and CO 2 applications, we illustrate the importance of a workflow and adaption to the target on hand. Verifying the geophysical acquisition and processing steps with 3D modeling and checking them against a 3D anisotropic log-derived model maintains confidence in the workflow and minimizes the influence on the data. This allows us to predict data validity and to certify the data with respect to the borehole logs.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2024

Utah FORGE: Southwestern Utah Magnetotelluric (MT) Data

This comprehensive magnetotellurics (MT) dataset, which covers southwestern Utah, integrates 600 sites from various surveys, including those from the Utah FORGE, SubTER, and Play Fairway projects, all of which are linked below. The core of this dataset is the use of a 3D finite element (FE) algorithm for inversion imaging, allowing for detailed analysis of the MT data. Provided here are all .edi files, the 3D finite element mesh (with a readme file), graphic slices, node graphics, resistivity graphics, and reports on the acquisition areas and inversion.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Preface to the Special Issue on Signal Processing, Data Acquisition and Front-end Systems for Radiation Detection and Measurements

This special issue of the Journal of Signal Processing Systems focuses on advancement of signal processing and data-acquisition for radiation detection and measurements. The seven articles included cover diverse topics within the scope of radiation detection ranging from understanding of fundamental fission process, instrumentation, and application of radiation interaction (e.g., tomography and imaging). The articles focus on signal processing for fission spectrometer, neutron and gamma-ray computed tomography and artificial intelligence methods, dynamic neutron imaging system, pulse-pileup correction in x-ray spectroscopy and calibration methods in radiation measurement.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2023

Coherent correlation imaging for resolving fluctuating states of matter

Fluctuations and stochastic transitions are ubiquitous in nanometre-scale systems, especially in the presence of disorder. However, their direct observation has so far been impeded by a seemingly fundamental, signal-limited compromise between spatial and temporal resolution. Here we develop coherent correlation imaging (CCI) to overcome this dilemma. Our method begins by classifying recorded camera frames in Fourier space. Contrast and spatial resolution emerge by averaging selectively over same-state frames. Temporal resolution down to the acquisition time of a single frame arises independently from an exceptionally low misclassification rate, which we achieve by combining a correlation-based similarity metric with a modified, iterative hierarchical clustering algorithm. We apply CCI to study previously inaccessible magnetic fluctuations in a highly degenerate magnetic stripe domain state with nanometre-scale resolution. We uncover an intricate network of transitions between more than 30 discrete states. Our spatiotemporal data enable us to reconstruct the pinning energy landscape and to thereby explain the dynamics observed on a microscopic level. CCI massively expands the potential of emerging high-coherence X-ray sources and paves the way for addressing large fundamental questions such as the contribution of pinning and topology in phase transitions and the role of spin and charge order fluctuations in high-temperature superconductivity.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Quantitative probing of the fast particle motion during the solidification of battery electrodes

Synchrotron x-ray tomography represents one of the most powerful imaging techniques that can visualize the three-dimensional microstructural complexity with a high spatial resolution. To date, however, the temporal resolution of this technique has been handicapped by the intrinsic limitations in the data acquisition speed and the lack of a suitable data processing method. The present study aims to fundamentally resolve this challenge by implementing a data analysis method that is based on differentiating discrete tomography reconstruction from a continuous scan reconstruction. With this methodological development, we capture and quantify the particle motion during the solidification of battery electrodes and reveal the statistics of the dynamically evolving motion in the drying process, which has been challenging to resolve. We discover that the particle motion exhibits a strong dependence on its geometric location within a drying electrode. Our results also imply that the final electrode quality can be controlled by balancing the solvent evaporation rate and the particle mobility in the region close to the drying surface.

25 ENERGY STORAGE↗
DOE OSTI2021

Simplified feedback control system for scanning tunneling microscopy

A Scanning Tunneling Microscope (STM) is one of the most important scanning probe tools available to study and manipulate matter at the nanoscale. In a STM, a tip is scanned on top of a surface with a separation of a few Å. Often, the tunneling current between the tip and the sample is maintained constant by modifying the distance between the tip apex and the surface through a feedback mechanism acting on a piezoelectric transducer. This produces very detailed images of the electronic properties of the surface. The feedback mechanism is nearly always made using a digital processing circuit separate from the user computer. Here, we discuss another approach using a computer and data acquisition through the universal serial bus port. We find that it allows successful ultralow noise studies of surfaces at cryogenic temperatures. We show results on different compounds including a type II Weyl semimetal (WTe2), a quasi-two-dimensional dichalcogenide superconductor (2H–NbSe2), a magnetic Weyl semimetal (Co3Sn2S2), and an iron pnictide superconductor (FeSe).

Martín-Vega, Francisco (ORCID:0000000333177833)↗
DOE OSTI2020

Experimental system for studying temperature gradient-driven fracture of oxide nuclear fuel out of reactor

Temperature gradients in ceramic light water reactor (LWR) uranium dioxide (UO 2 ) nuclear fuel pellets generate thermal stresses that cause fractures in the fuel beginning early in the life of fresh fuel. The combination of heating due to fission and forced convective cooling on the exterior of LWR fuel rods generates a temperature profile that is difficult to replicate outside the reactor environment. In the present study, a state-of-the-art experimental set-up using electrical heating to replicate fission heating was built and surrogate fuel materials such as ceria (CeO 2 ) were used to validate the system. Cracking experiments were conducted on these surrogates by inducing reactivity-initiated-accident (RIA) like temperature gradients in the pellets via induction and direct resistance heating. Induction heating was done using copper coils and molybdenum susceptors which heated the surrogates to a threshold temperature that is sufficiently high for the fuel material to conduct current. Thereafter, direct resistance heating was used by a D.C. power supply to introduce volumetric heating to replicate LWR operating conditions analogous to fission heating. The pellets were held against nickel electrodes and mounted on a boron nitride test-stand. All the tests were carried out in a stainless-steel vacuum chamber. Simultaneous real-time dual imaging of the surrogate pellet surface has been implemented using an optical and infrared camera system which will be mounted along axial and perpendicular directions to the pellet surface respectively. A beam-splitter was used to split the incoming radiation from the sample into two halves. While one of the beams is transmitted from the splitter through a bandpass filter to obtain optical images, the other beam is reflected from the splitter to the thermal camera to capture full field temperature gradients of the as fabricated pellet surface during crack initiation and propagation. In the current series of tests, a 2-color pyrometer was used for recording and comparing the surface and centerline temperatures of the surrogate pellets in lieu of the thermal camera. A LabVIEW data acquisition system has been set up for collecting useful data during experiments.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Detector Tilt Considerations in Bragg Coherent Diffraction Imaging: A Simulation Study

This paper addresses the three-dimensional signal distortion and image reconstruction issues in X-ray Bragg coherent diffraction imaging (BCDI) in the event of a general non-orthogonal orientation of the area detector with respect to the diffracted beam. Growing interest in novel BCDI adaptations at fourth-generation synchrotron light sources has necessitated improvisations in the experimental configuration and the subsequent data analysis. One such possibly unavoidable improvisation that is envisioned in this paper is a photon-counting area detector whose face is tilted away from the perpendicular to the Bragg-diffracted beam during the acquisition of the coherent diffraction signal. We describe a likely circumstance in which one would require such a detector configuration, along with the experimental precedent at third-generation synchrotrons. Using physically accurate diffraction simulations from synthetic scatterers in the presence of such tilted detectors, we analyze the general nature of the observed signal distortion qualitatively and quantitatively and provide a prescription to correct for it during image reconstruction. Our simulations and reconstructions are based on an adaptation of the known theory of BCDI sampling geometry, as well as the recently developed projection-based methods of wavefield propagation. Such configurational modifications and their numerical remedies are potentially valuable in realizing unconventional coherent diffraction measurement geometries, eventually paving the way for the integration of BCDI into new material characterization experiments at next-generation light sources.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Accelerating strain phenotyping with desorption electrospray ionization-imaging mass spectrometry and untargeted analysis of intact microbial colonies

Significance Synthetic biology has entered an era in which reading and writing DNA sequences are no longer rate-limiting steps in microbial strain engineering. Indeed, analytical methods measuring the resulting metabolic outcomes of specific gene edits have lagged behind the ability to generate new recombinant strains. Herein, we report a mass spectrometry strategy to accelerate these analytical workflows by directly analyzing metabolites and molecules produced from engineered microorganisms in a multiplexed process. Using untargeted acquisitions and unsupervised analytics, we assess the molecular features that change across discrete strains including primary target species, secondary products, and species outside the engineered fatty acid biosynthesis pathway.

09 BIOMASS FUELS↗
DOE OSTI2021

Phase-weighted slant stacking for surface wave dispersion measurement

SUMMARY Surface wave retrieval from ambient noise records using seismic interferometry techniques has been widely used for multiscale shear wave velocity (Vs) imaging. One key step during Vs imaging is the generation of dispersion spectra and the extraction of a reliable dispersion curve from the retrieved surface waves. However, the sparse array geometry usually affects the ability for high-frequency (>1 Hz) seismic signals’ acquisition. Dispersion measurements are degraded by array response due to sparse sampling and often present smeared dispersion spectra with sidelobe artefacts. Previous studies usually focus on interferograms’ domain (e.g. cross-correlation function) and attempt to enhance coherent signals before dispersion measurement. We propose an alternative technique to explicitly deblur dispersion spectra through use of a phase-weighted slant-stacking algorithm. Numerical examples demonstrate the strength of the proposed technique to attenuate array responses as well as incoherent noise. Three different field examples prove the flexibility and superiority of the proposed technique: the first data set consists of ambient noise records acquired using a nodal seismometer array; the second data set utilizes distributed acoustic sensing (DAS) and a marine fibre-optic cable to acquire a similar ambient noise data set; the last data set is a vibrator-based active-source surface wave data. The enhanced dispersion measurements provide cleaner and higher-resolution spectra without distortions which will assist both human interpreters as well as ML algorithms in efficiently picking curves for subsequent Vs inversion.

58 GEOSCIENCES↗
DOE OSTI2021

High-Resolution Momentum Imaging—From Stern’s Molecular Beam Method to the COLTRIMS Reaction Microscope [Book Chapter]

Multi-particle momentum imaging experiments are now capable of providing detailed information on the properties and the dynamics of quantum systems in Atomic, Molecular and Photon (AMO) physics. Historically, Otto Stern can be considered the pioneer of high-resolution momentum measurements of particles moving in a vacuum and he was the first to obtain sub-atomic unit (a.u.) momentum resolution (Schmidt-Böcking et al. in The precision limits in a single-event quantum measurement of electron momentum and position, these proceedings). A major contribution to modern experimental atomic and molecular physics was his so-called molecular beam method, which Stern developed and employed in his experiments. With this method he discovered several fundamental properties of atoms, molecules and nuclei. As corresponding particle detection techniques were lacking during his time, he was only able to observe the averaged footprints of large particle ensembles. Today it is routinely possible to measure the momenta of single particles, because of the tremendous progress in single particle detection and data acquisition electronics. A “state-of-the-art” COLTRIMS reaction microscope can measure, for example, the momenta of several particles ejected in the same quantum process in coincidence with sub-a.u. momentum resolution. Such setups can be used to visualize the dynamics of quantum reactions and image the entangled motion of electrons inside atoms and molecules. This review will briefly summarize Stern’s work and then present in longer detail the historic steps of the development of the COLTRIMS reaction microscope. Furthermore, some benchmark results are shown which initially paved the way for a broad acceptance of the COLTRIMS approach. Finally, a small selection of milestone work is presented which has been performed during the last two decades.

Jahnke, T.↗