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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Ion energy distribution in an electron beam ion trap inferred from simulations of the trapped ion cloud

For this work, we have inferred the energy distribution of trapped ions in an electron beam ion trap (EBIT) from simulations of the spatial distribution of Fe 13+ ions and a comparison with measured visible light images of the ion cloud. We simulated the cloud of Fe 13+ ions by computing ion trajectories in the EBIT for different ion energy distributions used to initialize the trajectories. We then performed a least-squares fit to infer the ion energy distribution that best reproduced the measured ion cloud. These best-fit distributions were typically non-Maxwellian. For electron beam energies of 395–475 eV and electron beam currents of 1–9 mA, we find that the average ion energy is in the range of 10–300 eV. We also find that the average ion energy increases with increasing beam current approximately as $\langle$E$\rangle$ ≈ 25 I e eV, where I e is the electron beam current in mA. We have also compared our results to Maxwell-Boltzmann-distribution ion clouds. We find that our best-fit non-thermal distributions have an $\langle$E$\rangle$ that is less than half that of the T from the best-fit Maxwell-Boltzmann distributions ($\langle$E$\rangle$/q)/T=0.41 ± 0.05.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

Improved Infrared Optical Constants from Pressed Pellets: II. Ellipsometric n and k Values for Ammonium Sulfate with Variability Analysis

Infrared spectral imaging is facilitated via the comparison of spectra recorded in situ to a databank of actual or synthetic infrared reflectance spectra. It has recently been shown that reference spectra corresponding to the many different morphological forms of the same chemical can be generated synthetically using the imaginary, k, and real, n, components of the complex refractive index, n~ = n + ik. One method to obtain the n and k vectors is infrared ellipsometry, which measures the changes in amplitude, tan ?, and phase, ?, of polarized light reflected from the sample both as a function of wavenumber and angle of incidence. The method requires specularly reflected light so best results are usually obtained with polished planar samples of large surface area. Due to the difficulties of obtaining such samples, however, we investigate the possibility of pressing powders of neat materials and obtaining the corresponding optical constants from the pellets. In this paper, variability in the sample pellet and preparation method is investigated, as is variability in the fitting procedure for the derived optical constants. The n/k vectors are derived from the measured ellipsometric parameters, tan ? and ?, as they are fit by an oscillator model which yield n(?) and k(?) vectors as a function of wavenumber, ?. Construction of the oscillator model is not automatic and depends on significant input from the analyst as well as the sample’s physical characteristics. For the pellet pressing, the experimental variability was found to be minimized for size-selected powdered samples as gauged by the minimal variance in ? and ? for three different pellets; similarly, the analytical precision for multiple measurements of the same pellet was also quite good, suggesting that a pressed pellet is a viable sample preparation method. The greatest variability came in the analytic fitting procedure with differences up to 20% arising from four different fits to the same data set. The final ellipsometric n/k data are compared to literature values obtained from crystalline (NH4)2SO4 samples as well as single-angle reflectance measurements that also used pressed pellets. Comparison with the previous literature values shows generally good agreement although higher k-values are observed for the independent sets of data derived from pressed pellets. These data are suggested as an improved set of optical constants for (NH4)2SO4.

optical constants, ellipsometry, ammonium sulfate,↗
DOE OSTI2021

Herbig Ae/Be Stars toward the Dark Cloud LDN 1667

We report the discovery of a new emission-line object, named SPH 4-South = (GAIA EDR3 5616553300192230272), toward the dark cloud LDN 1667. This object came to our attention after inspecting public images that show a faint diffuse nebula a few arcseconds south of SPH 4, an emission-line object previously classified as a T Tauri star. We present high-resolution spectra and analyzed JHK photometry of SPH 4 and SPH 4-South and new narrowband and archival broadband images of these objects. A comparison of the spectra of SPH 4 and SPH 4-South with high-resolution ones of DG Cir and R Mon strongly suggests that SPH 4 and SPH 4-South are Herbig Ae/Be stars. The classification of SPH 4-South is further supported by using a k-NN algorithm to its position in an H–K versus J–H color–color diagram. Both stars are detected in the four WISE bands and the WISE colors allow us to classify SPH 4 as a Class I and SPH 4-South as a Class II source. We also show that the faint nebula is most probably associated with SPH 4-South. Using published results on LDN 1667 and the Gaia Early Data Release 3, we conclude that SPH 4 is a member of LDN 1667. The case of SPH 4-South is not clear because the determination of its distance and proper motion could be affected by the nebulosity around the star, although membership of SPH 4-South to LDN 1667 cannot be ruled out.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Thermal Stimulation and Injectivity Testing at Raft River, ID EGS Site

The Raft River geothermal field is the site of an innovative Department of Energy Enhanced Geothermal System (EGS) project to determine the viability of using combined thermal and hydraulic stimulation techniques to improve energy production. Well RRG-9 is currently undergoing a stimulation program using injectate from the US Geothermal Raft River Power Plant and cold water from a cooling tower make-up water well. The stimulation began on 13 June 2013 with injection from the power plant at a temperature of about 39 °C and a pressure of 275 psig. Next, two positive displacement plunger type pumps were used to increase the injection pressure and flow rate for about one month. The highest rate achieved was 258 gpm at a pressure of 741 psig. During this time, fluid from the cooler water well was injected for about 2 weeks at various pressures. Then, the pumps were removed and plant injection resumed on 25 September. Plant injection will continue until the spring of 2014, when a high pressure hydraulic stimulation will be conducted. A series of seismic monitoring stations deployed around the well are providing data on seismic events occurring at the site. Over the past year, 51 microseismic events have been recorded, all less than Magnitude 1. During injection, several diagnostic tests were conducted to gain a better understanding of the well and reservoir. A step-rate test was performed on 22 August to measure the in-situ stress and aid in modeling in-situ fractures. A tracer was injected into the well on 9 September. No tracer was detected in adjacent production wells after several months. A second borehole televiewer survey was conducted for comparison to pre-stimulation images. Here, a third borehole televiewer survey is planned after the high pressure stimulation. Injection test data is evaluated in real time. A modified Hall plot analysis indicates the effective permeability is increasing. The injectivity index supports the results of the modified Hall plot analysis. As the thermal stimulation has continued, the injectivity index has consistently followed an upward trend from 0.1 gpm/psi to 0.53 gpm/psi.

enhanced geothermal systems↗
DOE OSTI2021

Preliminary Evaluations of [ 11 C]Verubulin: Implications for Microtubule Imaging With PET

[ 11 C]Verubulin (a.k.a.[ 11 C]MCP-6827), [ 11 C]HD-800 and [ 11 C]colchicine have been developed for imaging microtubules (MTs) with positron emission tomography (PET). The objective of this work was to conduct an in vivo comparison of [ 11 C]verubulin for MT imaging in mouse and rat brain, as well as an in vitro study with this radiotracer in rodent and human Alzheimer’s Disease tissue. Our preliminary PET imaging studies of [ 11 C]verubulin in rodents revealed contradictory results between mouse and rat brain uptake under pretreatment conditions. In vitro autoradiography with [ 11 C]verubulin showed an unexpected higher uptake in AD patient tissue compared with healthy controls. We also conducted the first comparative in vivo PET imaging study with [ 11 C]verubulin, [ 11 C]HD-800 and [ 11 C]colchicine in a non-human primate. [ 11 C]Verubulin and [ 11 C]HD-800 require pharmacokinetic modeling and quantification studies to understand the role of how these radiotracers bind to MTs before translation to human use.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Substrate-enhanced photothermal nano-imaging of surface polaritons in monolayer graphene

Surface polaritons comprise a wealth of light–matter interactions with deep sub-wavelength scale confinement of electromagnetic modes. However, their nanoscale localized dissipation and thermalization processes are not readily accessible experimentally. Here, we introduce photothermal force microscopy to image surface plasmon polaritons (SPPs) in monolayer graphene through their non-radiative SiO2 substrate dissipation. We demonstrate the real-space SPP imaging via photo-induced atomic force detection, and from comparison with scattering-type scanning near-field optical microscopy imaging attribute the force response to substrate dissipation-enhanced thermal expansion. This work illustrates that nano-optical tip–sample induced dissipative forces facilitate a direct mechanical detection of surface polariton interactions with monolayer sensitivity.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Classification and Localization of Fracture-Hit Events in Low-Frequency Distributed Acoustic Sensing Strain Rate with Convolutional Neural Networks

Summary Distributed acoustic sensing (DAS) has been used in the oil and gas industry as an advanced technology for surveillance and diagnostics. Operators use DAS to monitor hydraulic fracturing activities, examine well stimulation efficacy, and estimate complex fracture system geometries. Particularly, low-frequency DAS can detect geomechanical events such as fracture hits because hydraulic fractures propagate and create strain rate variations in the rock. Analysis of DAS data today is mostly done post-job and subject to interpretation methods. However, the continuous and dense data stream generated live by DAS poses the opportunity for more efficient and accurate real-time data-driven analysis. The objective of this study is to develop a machine learning-based workflow that can identify and locate fracture-hit events in simulated strain rate responses correlated with low-frequency DAS data. In this paper, “fracture hit” refers to a hydraulic fracture originating from a stimulated well intersecting an offset well. We start with building a single fracture propagation model to produce strain rate patterns observed at a hypothetical monitoring well. This model is used to generate two sets of strain rate responses with one set containing fracture-hit events. The labeled synthetic data are then used to train a custom convolutional neural network (CNN) model for identifying the presence of fracture-hit events. The same model is trained again for locating the event with the output layer of the model replaced with linear units. We achieved near-perfect predictions for both event classification and localization. These promising results prove the feasibility of using CNN for real-time event detection from fiber-optic sensing data. Additionally, we use edge detection techniques to recognize fracture-hit event patterns in strain rate images. The fracture-hit location can be identified using recognized pixels in the image. The accuracy of edge detection-based location identification is also plausible, but edge detection is dependent on the assumption of pattern shape and image quality, hence it is less robust compared to CNN models. This comparison further supports the need for CNN applications in image-based real-time fiber-optic sensing event detection.

Engineering↗
DOE OSTI2022

Comparison of Machine Learning and Deep Learning for View Identification from Cardiac Magnetic Resonance Images

Background: Artificial intelligence is increasingly utilized to aid in the interpretation of cardiac magnetic resonance (CMR) studies. One of the first steps is the identification of the imaging plane depicted, which can be achieved by both deep learning (DL) and classical machine learning (ML) techniques without user input. We aimed to compare the accuracy of ML and DL for CMR view classification and to identify potential pitfalls during training and testing of the algorithms. Methods: To train our DL and ML algorithms, we first established datasets by retrospectively selecting 200 CMR cases. The models were trained using two different cohorts (passively and actively curated) and applied data augmentation to enhance training. Once trained, the models were validated on an external dataset, consisting of 20 cases acquired at another center. We then compared accuracy metrics and applied class activation mapping (CAM) to visualize DL model performance. Results: The DL and ML models trained with the passively-curated CMR cohort were 99.1% and 99.3% accurate on the validation set, respectively. However, when tested on the CMR cases with complex anatomy, both models performed poorly. After training and testing our models again on all 200 cases (active cohort), validation on the external dataset resulted in 95% and 90% accuracy, respectively. The CAM analysis depicted heat maps that demonstrated the importance of carefully curating the datasets to be used for training. Conclusions: Both DL and ML models can accurately classify CMR images, but DL outperformed ML when classifying images with complex heart anatomy.

artificial intelligence↗
DOE OSTI2022

Multi-frame, ultrafast, x-ray microscope for imaging shockwave dynamics

Inertial confinement fusion (ICF) holds increasing promise as a potential source of abundant, clean energy, but has been impeded by defects such as micro-voids in the ablator layer of the fuel capsules. It is critical to understand how these micro-voids interact with the laser-driven shock waves that compress the fuel pellet. At the Matter in Extreme Conditions (MEC) instrument at the Linac Coherent Light Source (LCLS), we utilized an x-ray pulse train with ns separation, an x-ray microscope, and an ultrafast x-ray imaging (UXI) detector to image shock wave interactions with micro-voids. To minimize the high- and low-frequency variations of the captured images, we incorporated principal component analysis (PCA) and image alignment for flat-field correction. After applying these techniques we generated phase and attenuation maps from a 2D hydrodynamic radiation code (xRAGE), which were used to simulate XPCI images that we qualitatively compare with experimental images, providing a one-to-one comparison for benchmarking material performance. Moreover, we implement a transport-of-intensity (TIE) based method to obtain the average projected mass density (areal density) of our experimental images, yielding insight into how defect-bearing ablator materials alter microstructural feature evolution, material compression, and shock wave propagation on ICF-relevant time scales.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Quantitative imaging and automated fuel pin identification for passive gamma emission tomography

Compliance of member States to the Treaty on the Non-Proliferation of Nuclear Weapons is monitored through nuclear safeguards. The Passive Gamma Emission Tomography (PGET) system is a novel instrument developed within the framework of the International Atomic Energy Agency (IAEA) project JNT 1510, which included the European Commission, Finland, Hungary and Sweden. The PGET is used for the verification of spent nuclear fuel stored in water pools. Advanced image reconstruction techniques are crucial for obtaining high-quality cross-sectional images of the spent-fuel bundle to allow inspectors of the IAEA to monitor nuclear material and promptly identify its diversion. In this work, we have developed a software suite to accurately reconstruct the spent-fuel cross sectional image, automatically identify present fuel rods, and estimate their activity. Unique image reconstruction challenges are posed by the measurement of spent fuel, due to its high activity and the self-attenuation. While the former is mitigated by detector physical collimation, we implemented a linear forward model to model the detector responses to the fuel rods inside the PGET, to account for the latter. The image reconstruction is performed by solving a regularized linear inverse problem using the fast-iterative shrinkage-thresholding algorithm. We have also implemented the traditional filtered back projection (FBP) method based on the inverse Radon transform for comparison and applied both methods to reconstruct images of simulated mockup fuel assemblies. Higher image resolution and fewer reconstruction artifacts were obtained with the inverse-problem approach, with the mean-square-error reduced by 50%, and the structural-similarity improved by 200%. We then used a convolutional neural network (CNN) to automatically identify the bundle type and extract the pin locations from the images; the estimated activity levels finally being compared with the ground truth. The proposed computational methods accurately estimated the activity levels of the present pins, with an associated uncertainty of approximately 5%.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Image Distinguishability Analysis Testing Through Principal Components and Its Application to Hot Spot Scale Invariance

Hot spots are spatial regions of intense energy localization that govern initiation of secondary high explosives. Studies that characterize or compare simulated hot spots are frequently either qualitatively descriptive or resort to quantitative distribution functions that neglect stochastic variations and spatial correlations—effects that are also neglected in common comparison tests like the Kolmogorov–Smirnov test. To this end, we develop an image distinguishability analysis (IDA) test based on principal component (PC) analysis that makes pixel-by-pixel comparisons between small, for example, O(<10), image data sets. The IDA test makes comparisons through a generalized distance metric in the PC space and a test statistic that is derived to calculate mathematical equation-values. Here, we derive a statistical distribution and criticality criterion to determine whether images are distinguishable from established baselines. We apply the IDA test on images generated from molecular dynamics simulations of hot spots from pore collapse in TATB to assess scale invariance in the complex patterns of hot spots that form in a representative high explosive crystal. The IDA test shows that TATB hot spot spatial temperature fields and their derived temperature histograms exhibit scale-invariant features over specific intervals of shock orientation, strength, and initial pore diameter. However, the IDA test also shows that qualitatively different conclusions regarding invariance can be reached depending on whether the hot spot is treated as a spatially correlated field as opposed to a distribution function that lacks spatial information.

organic↗
DOE OSTI2023

In operando neutron imaging characterizations of all-solid-state batteries

The surge in electric vehicle demand has propagated the extensive application of lithium-ion batteries (LIBs) in recent years. Gaining significant traction due to their promising high-energy density and elevated safety over traditional LIBs, all-solid-state Li batteries (ASLBs) have nonetheless been faced with hurdles relating to battery performance. These include concerns with interfacial compatibility, structural stability, Li dendrite inhibition, and large-scale manufacturing. To tackle these issues, it is necessary to employ advanced characterization methods to comprehend the intrinsic mechanisms within ASLBs. In this article, we advocate for the use of neutron imaging as a nondestructive approach for the operando visualization of ASLBs. We draw comparisons with other operando visualization strategies, underline the benefits of neutron imaging, and discuss its potential applicability in the scrutiny of all-solid-state Li metal batteries and all-solid-state Li-sulfur batteries. Neutron imaging provides valuable insights into the dynamics of Li concentration, reaction mechanisms, and transport constraints in ASLBs. These insights are pivotal in contributing to the evolution of high-performance all-solid-state batteries.

25 ENERGY STORAGE↗
DOE OSTI2025

Thermodynamic Stability and Site‐Specific Distribution of Graphitic and Pyridinic Nitrogen in Graphene Moiré on Ru(0001)

Abstract Graphene‐like materials are of interest for large‐scale hydrogen storage applications due to their lightweight, durable, and scalable properties. Nitrogen‐doping minimizes kinetic limitations in diffusion and recombination on surfaces, however, the role of graphitic nitrogen (GN) and pyridinic nitrogen (PN) is not well understood. Nitrogen‐doped graphene is synthesized on Ru(0001) using chemical vapor deposition (CVD) of pyridine and ion irradiation. Scanning tunneling microscopy (STM), x‐ray photoelectron spectroscopy (XPS), and density functional theory (DFT) are used to identify the structure, location, and thermodynamic stability of nitrogen species within the graphene moiré. CVD of pyridine results in a low nitrogen concentration (<0.1at%), while the post‐growth nitrogen ion irradiation allows us to increase the concentration further. The concentration of GN and PN is controlled by varying the ion dose and annealing temperature. Comparison of measured and simulated STM images of GN and PN yield an excellent agreement, allowing us to confidently establish that GN is preferentially located near the center of the Atop region, while PN is located in the valley region of the graphene moiré. This report explicitly confirms the site assignments and provides a foundation for the site synthesis and analysis of structural and electronic properties that drive the reactivity of N‐doped graphene.

Gedara, Buddhika S. A. [Physical and Computational↗
DOE OSTI2022

Finding Hidden Patterns in High Resolution Wind Flow Model Simulations

Wind flow data is critical in terms of investment decisions and policy making. High resolution data from wind flow model simulations serve as a supplement to the limited resource of original wind flow data collection. Given the large size of data, finding hidden patterns in wind flow model simulations are critical for reducing the dimensionality of the analysis. In this work, we first perform dimension reduction with two autoencoder models: the CNN-based autoencoder (CNN-AE) [1], and hierarchical autoencoder (HIER-AE) [2], and compare their performance with the Principal Component Analysis (PCA). We then investigate the super-resolution of the wind flow data. By training a Generative Adversarial Network (GAN) with 300 epochs, we obtained a trained model with 2× resolution enhancement. We compare the results of GAN with Convolutional Neural Network (CNN), and GAN results show finer structure as expected in the data field images. Also, the kinetic energy spectra comparisons show that GAN outperforms CNN in terms of reproducing the physical properties for high wavenumbers and is critical for analysis where high-wavenumber kinetics play an important role.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

H 2 Pc and pentacene on Cu(110)-(2×1)O: A combined STM and nc-AFM study

Scanning tunneling microscopy (STM) and non-contact atomic force microscopy (nc-AFM) with a CuO tip is used to investigate adsorption of metal-free phthalocyanine (H 2 Pc) and pentacene on the Cu(110)-(2×1)O striped-phase reconstructed surface. We show that the combination of STM and nc-AFM is necessary to reveal the detailed adsorption geometry, necessary for interpreting the observed STM contrast. Comparison of the nc-AFM images with simulations shows that some of the H 2 Pc molecules are deformed out-of-plane on this surface, while pentacene retains its molecular geometry regardless of adsorption site. Our research highlights how the stiffness of CuO tips makes it possible to probe the structure of organic semiconductor / metal interfaces.

77 NANOSCIENCE AND NANOTECHNOLOGY↗