SEARCH · Engineering Papers
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.
Conclusions: Elements other than detector array can heavily impact performance; Must consider a variety of alterations to an imaging array to determine an optimal system.
Lattice defects, e.g., dislocations and grain boundaries, critically impact the properties of crystalline battery cathode materials. A longstanding challenge is to probe the meso–scale heterogeneity and evolution of lattice defects with sensitivity to atomic-scale details. Herein, we tackle this issue with a unique combination of X-ray nanoprobe diffractive imaging and advanced machine learning techniques. The domains with different lattice defect configuration within a single-crystalline LiCoO2 cathode particle are faithfully revealed using our approach. We further visualize the rearrangement of grain boundaries and local crystallinity upon mild thermal annealing. Furthermore, these results pave a direct way to the understanding of crystalline battery materials’ response under external stimuli with high fidelity, which provides valuable empirical guidance to defect-engineering strategies for improving the cathode materials against aggressive battery operation.
Wigner predicted that when the Coulomb interactions between electrons become much stronger than their kinetic energy, electrons crystallize into a closely packed lattice. A variety of two-dimensional systems have shown evidence for Wigner crystals (WCs). However, a spontaneously formed classical or quantum WC has never been directly visualized. Neither the identification of the WC symmetry nor direct investigation of its melting has been accomplished. Here we use high-resolution scanning tunnelling microscopy measurements to directly image a magnetic-field-induced electron WC in Bernal-stacked bilayer graphene and examine its structural properties as a function of electron density, magnetic field and temperature. At high fields and the lowest temperature, we observe a triangular lattice electron WC in the lowest Landau level. The WC possesses the expected lattice constant and is robust between filling factor ν ≈ 0.13 and ν ≈ 0.38 except near fillings where it competes with fractional quantum Hall states. Increasing the density or temperature results in the melting of the WC into a liquid phase that is isotropic but has a modulated structure characterized by the Bragg wavevector of the WC. At low magnetic fields, the WC unexpectedly transitions into an anisotropic stripe phase, which has been commonly anticipated to form in higher Landau levels. Furthermore, analysis of individual lattice sites shows signatures that may be related to the quantum zero-point motion of electrons in the WC lattice.
The computed tomography (CT) facilities and the Multi-Sensor Core Logger (MSCL) at the National Energy Technology Laboratory (NETL) in Morgantown, West Virginia were used to characterize core of the Bedwell 33-52-1-1H well (API 25-091-21920), a wildcat field in Sheridan County, Montana. The primary impetus of this work is to provide a unique dataset to researchers at universities, national laboratories, geological surveys, and other locations for additional analyses. The resultant datasets are presented in this report and can be accessed from NETL's Energy Data eXchange (EDX) online system using the following link: https://edx.netl.doe.gov/dataset/bedwell-33-52-1-1h-well. The equipment and techniques used to characterize the full core were non-destructive, enabling future examinations and analyses to be performed on these cores. However, none of the equipment used was suitable for direct visualization of the pore space in fine-grained structures such as those found in shales; fractures, discontinuities, and millimeter scale features were readily detectable with the methods tested. Imaging with the NETL medical CT scanner was performed on the entire core. Qualitative analysis of the medical CT images, coupled with X-ray fluorescence (XRF), P-wave, and magnetic susceptibility measurements from the MSCL were useful in identifying zones of interest for more detailed analysis. A selection of samples (Table 1) were cored, sliced, and powdered for additional characterization analyses. These analyses did involve destructive subsampling of the full core at discrete sections, and the results are presented here in the context of the larger core description. Selected sections of the core were removed from the full core for detailed analyses using the following destructive techniques: bulk inductively coupled plasma-optical emission spectroscopy (ICP-OES), isolation of carbon and sulfur, Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), and higher resolution CT scan with NETL’s micro-CT scanning systems. The ability to quickly identify key areas for more detailed study with higher resolution will save time and resources in future studies. The combination of methods used provides a multi-scale analysis of the core; the resulting macro and micro descriptions are relevant to many subsurface energy related examinations traditionally performed at NETL.
The Annual Site Environmental Report (ASER) from a U.S. Department of Energy (DOE) Site summarizes environmental data; environmental management performance; compliance with applicable DOE, federal, state, and local regulations; and restoration and surveillance monitoring programs. The Savannah River Site (SRS) produces ASERs that include results of monitoring mercury levels in fish samples obtained in the vicinity of SRS. Mercury is a toxic trace metal and the methylated organic form is a neurotoxin that bioaccumulates in the food web, endangering humans and other organisms. In United States, power plants are the largest source of mercury emissions. Research Objective: To determine the patterns of mercury levels in fish samples reported in the SRS environmental monitoring reports. We developed box plot visualizations to make sense of the data as well as identify fish species, dates and location with outlier mercury content. The integration and visual representation of multi-year datasets can support decision making processes on environmental compliance and environmental sustainability conducted at U.S. DOE Sites. We are constructing datasets from textual, image and multivariable data sources in the multi-year ASERs of the U.S. DOE Savannah River Site (SRS https://www.srs.gov/general/pubs/ERsum/index.html). The Fish Surveillance for mercury presents a case study for the intersection of food, energy and water systems. The dataset of 864 records (2008 and 2009) of mercury levels includes water location of fish, fish species, collection date and mercury content of fish sample. The patterns of mercury levels in the fish species as well as locations provide knowledge for further investigations. A research area for future studies is the composition of microbial communities in fish guts of varying levels of mercury.
We generated synthetic equiaxed grain structures using computer graphics software to explore the relationship between various grain size determination methods and true three-dimensional (3D) grain diameters. Mirroring grain measurement techniques, the synthetic 3D grain structures are imaged as 2D micrographs which are measured to yield 1D grain size parameters. Synthetic grain structures provide data at a mass scale and permit exploration of both polished and fractured surface micrographs, revealing one-to-one correspondence between exposed 2D grain cross-sections and individual 3D grains. Analysis of this correspondence yielded a procedure to approximate 3D equiaxed grain size and volume distributions based on the mode of the 2D fractograph grain size distribution. The 3D approximation procedure is shown to be less susceptible to different imaging conditions that affect small, undiscernible grains compared to the standard planimetric and linear intercept methods, which by design also tend to underestimate the 3D grain diameter. The procedure requires larger sample sizes to lower variance and a deeper analysis which could become more practical with machine learning (ML) models for grain boundary segmentation, which synthetic grain structures can help train. This work lays the foundation for analyzing other grain distributions such as columnar and composite grains in similar depth.
We utilize operando X-ray computed tomography, coupled with real-time electrochemical analysis, to reveal the underlying failure mechanisms of membrane electrode assemblies (MEAs) for electrochemical CO 2 reduction (eCO 2 R). Through operando imaging, we can obtain unprecedented insights into the dynamic behavior of the MEA under different operating conditions, revealing critical changes in interface interactions, phase distribution, and structural integrity over time. Our findings identify phenomena giving rise to the transition from CO 2 R to the hydrogen evolution reaction (HER), as evidenced by shifts in cathode potential and CO 2 R selectivity. The formation of inhomogeneous precipitates at the gas diffusion electrode disrupts the CO 2 supply and reduces the active sites for eCO 2 R, resulting in a shift toward H2 production during low current density operation. Additionally, under high current density conditions, rapid water crossover up to the microporous layer/gas diffusion layer promotes the transition from CO 2 R to HER, further shifting cell potential toward anodic direction. Oscillating voltage conditions reveal the dissolution and regrowth of precipitates, providing direct visualization of the competing selectivity of CO 2 R and HER. This work offers new insight into the degradation mechanisms of MEAs, with implications for the design of more durable CO 2 R systems.
Since the discovery of two-dimensional electron gas at the LaAlO 3 /SrTiO 3 interface, its intriguing physical properties have garnered significant interests for device applications. Yet, understanding its response to electrical stimuli remains incomplete. Our in-situ transmission electron microscopy analysis of a LaAlO 3 /SrTiO 3 two-dimensional electron gas device under electrical bias reveals key insights. Inline electron holography visualized the field-induced modulation of two-dimensional electron gas at the interface, while electron energy loss spectroscopy showed negligible electromigration of oxygen vacancies. Instead, atom-resolved imaging indicated that electric fields trigger polar distortion in the LaAlO 3 layer, affecting two-dimensional electron gas modulation. This study refutes the previously hypothesized role of oxygen vacancies, underscoring the lattice flexibility of LaAlO 3 and its varied polar distortions under electric fields as central to two-dimensional electron gas dynamics. These findings open pathways for advanced oxide nanoelectronics, exploiting the interplay of polar and nonpolar distortions in LaAlO 3 .
Imaging of optical phenomena at the sub-nanometer scale can offer fundamental insights into the electronic or vibrational states in atomic-scale defects, molecules, and nanoparticles, which are important in quantum information, heterogeneous catalysis, optoelectronics, and structural biology. Several techniques have surpassed the traditional Abbe diffraction limit and attained spatial resolutions down to a few nanometers, but sub-nanometer scale optics has remained elusive. Here, we propose an approach that combines spectrally specific photoabsorption with sub-nanometer scale resolution transmission electron microscopy (TEM) of photoexcited electrons. We first estimate the signal level and conditions required for imaging nanoscale optical phenomena in core-shell quantum dots (QDs) like CdS/CdTe. Furthermore, we show the possibility of imaging photoexcited states of atomic-scale defects in a monolayer hexagonal boron nitride (h-BN) using ab initio and high resolution (HR)TEM simulations. The ability to directly visualize photoexcited states at the sub-nanometer scale opens opportunities to study properties of individual quantum dots and atomic defects.
In support of analyst requests for Mobile Guardian Transport studies, researchers at Sandia National Laboratories have expanded data types for the Slycat ensemble-analysis and visualization tool to include 3D surface meshes. This new capability represents a significant advance in our ability to perform detailed comparative analysis of simulation results. Analyzing mesh data rather than images provides greater flexibility for post-processing exploratory analysis.
We present a new observation of satellite galaxies around seven Milky Way (MW)–like galaxies located outside of the Local Group (LG) using Subaru/Hyper Suprime-Cam imaging data to statistically address the missing satellite problem. We select satellite galaxy candidates using magnitude, surface brightness, Sérsic index, axial ratio, FWHM, and surface brightness fluctuation cuts, followed by visual screening of false positives such as optical ghosts of bright stars. We identify 51 secure dwarf satellite galaxies within the virial radius of nine host galaxies, two of which are drawn from the pilot observation presented in Paper I. We find that the average luminosity function of the satellite galaxies is consistent with that of the MW satellites, although the luminosity function of each host galaxy varies significantly. We observe an indication that more massive hosts tend to have a larger number of satellites. Physical properties of the satellites such as the size–luminosity relation are also consistent with the MW satellites. However, the spatial distribution is different; we find that the satellite galaxies outside of the LG show no sign of concentration or alignment, while that of the MW satellites is more concentrated around the host and exhibits a significant alignment. As we focus on relatively massive satellites with M V < –10, we do not expect that the observational incompleteness can be responsible here. This trend might represent a peculiarity of the MW satellites, and further work is needed to understand its origin.
Fundamental chemistry in heterogeneous catalysis is increasingly explored using operando techniques in order to address the pressure gap between ultrahigh vacuum studies and practical operating pressures. Because most operando experiments focus on the surface and surface-bound species, there is a knowledge gap of the near-surface gas phase and the fundamental information the properties of this region convey about catalytic mechanisms. In this work, we demonstrate in situ visualization and measurement of gas-phase species and temperature distributions in operando catalysis experiments using complementary near-surface optical and mass spectrometry techniques. The partial oxidation of methanol over a silver catalyst demonstrates the value of these diagnostic techniques at 600 Torr (800 mbar) pressure and temperatures from 150 to 410 °C. Planar laser-induced fluorescence provides two-dimensional images of the formaldehyde product distribution that show the development of the boundary layer above the catalyst under different flow conditions. Raman scattering imaging provides measurements of a wide range of major species, such as methanol, oxygen, nitrogen, formaldehyde, and water vapor. Near-surface molecular beam mass spectrometry enables simultaneous detection of all species using a gas sampling probe. Detection of gas-phase free radicals, such as CH 3 and CH 3 O, and of minor products, such as acetaldehyde, dimethyl ether, and methyl formate, provides insights into catalytic mechanisms of the partial oxidation of methanol. The combination of these techniques provides a detailed picture of the coupling between the gas phase and surface in heterogeneous catalysis and enables parametric studies under different operating conditions, which will enhance our ability to constrain microkinetic models of heterogeneous catalysis.
SUMMARY Microseismic observations during unconventional reservoir stimulation are typically seen as a proxy for clusters of hydraulic fractures and the extent of the stimulated reservoir. Such straightforward interpretation is often misleading and fails to provide a physically reasonable image of the fracturing process. This paper demonstrates the application of a physics-based machine learning algorithm which enables a rapid and accurate fracture mapping from the microseismic data. Our training and validation data set relies on a history-matched geomechanical modelling workflow implemented in GEOS software for the Hydraulic Fracturing Test Site 1 (HFTS-1) project. For this study we augmented the simulated fracture growth through geostatistical modelling of induced seismicity, so that the synthetic microseismic catalogue matches the main statistical properties of the field observations. We formulated the problem of mapping the actual fracture in the clutter of events to parallel common video segmentation workflows: several past video frames (microseismic density snapshots) are passed through a deep convolutional network to classify whether a given voxel is associated with a fracture or intact rock. We found that for accurate fracture mapping, the network’s input and architecture must be augmented to incorporate the fluid injection parameters (pressure, rate, concentration of proppant, and location of the perforation within the cluster). The error rate for the network reached as little as 10 per cent of the fracture area, while a conventional microseismic interpretation approach yielded ∼300 per cent. Our approach also yields must faster predictions than conventional methods (minutes instead of weeks), and could enable engineers to make rapid decisions regarding engineering parameters (pumping rate, viscosity) in real time during stimulation.
Abstract Domain switching is crucial for achieving desired functions in ferroic materials that are used in various applications. Fast control of domains at sub‐nanosecond timescales remains a challenge despite its potential for high‐speed operation in random‐access memories, photonic, and nanoelectronic devices. Here, ultrafast laser excitation is shown to transiently melt and reconfigure ferroelectric stripe domains in multiferroic bismuth ferrite on a timescale faster than 100 picoseconds. This dynamic behavior is visualized by picosecond‐ and nanometer‐resolved X‐ray diffraction and time‐resolved X‐ray diffuse scattering. The disordering of stripe domains is attributed to the screening of depolarization fields by photogenerated carriers resulting in the formation of charged domain walls, as supported by phase‐field simulations. Furthermore, the recovery of disordered domains exhibits subdiffusive growth on nanosecond timescales, with a non‐equilibrium domain velocity reaching up to 10 m s −1 . These findings present a new approach to image and manipulate ferroelectric domains on sub‐nanosecond timescales, which can be further extended into other complex photoferroic systems to modulate their electronic, optical, and magnetic properties beyond gigahertz frequencies. This approach could pave the way for high‐speed ferroelectric data storage and computing, and, more broadly, defines new approaches for visualizing the non‐equilibrium dynamics of heterogeneous and disordered materials.
Abstract A combined positron emission particle tracking (PEPT) and X-ray computed tomography (CT) technique is presented, and its utility is demonstrated through investigation of flow in a pipe with twisted tape swirl insert with varying flow conditions (diameter-based Reynolds numbers 16,300–63,300). A description of this technique is given, as well as data handling practices used to relate geometric information captured by CT to fluid flow data gathered via PEPT. It is found that the CT component is readily capable of capturing the stainless steel insert geometry in this present system, but the use of combined plastic and metal materials leads to artifacts in imaging of the plastic surface. Nonetheless, CT data are related to PEPT flow measurements, and average velocity fields are calculated via a pseudo-framing and interpolation scheme and used to visualize and interrogate key flow phenomena within the system. Radial velocity profiles of the mean flow characteristics are seen to collapse to a nearly common form across all flow conditions considered. Helical vortices are seen propagating through the flow field, generated by bypass flow around the gap between the insert and pipe wall, with additional coherent secondary flow structures seen in the higher Reynolds number cases. These findings enhance the understanding of the mixing mechanisms in these swirl flows and encourage the continued development of PEPT-CT methodologies for 3D flow measurements in optically inaccessible systems.
The exponential increase in data produced over the last two decades has revolutionized the way we collect, store, process, analyze, model, and interpret information to improve profitability. Manufacturing is no exception. How- ever, Smart Manufacturing, the digital practice, organization, workforce, and infrastructure transformation for collection and deployment of data and models at scale and at all levels of manufacturing, is a complex, costly, and labor-intensive journey that is still seeing slow adoption. The Clean Energy Smart Manufacturing Innovation Institute (CESMII), a national Manufacturing USA public-private partnership sponsored by the Department of Energy, is addressing this scaled use of data and modeling in manufacturing. CESMII has focused on how to col- lect and use operating data for numerous applications that improve productivity, precision, and performance of manufacturing operations from factory floor to supply chain using process simulation, predictive analytics, mon- itoring and control, and real-time optimization. Because contextualized data are key, CESMII has developed the Smart Manufacturing Innovation Platform (SMIP) to lower the barriers to the data that are needed to accelerate data-based model building, improve data visualization, and more quickly gain insights. Reusable, standards-based ways of doing data collection, ingestion, and contextualization are particularly important for scaling access and use of data. The SMIP uses a standards-based definition and construct for reusable information models called an SM Profile. When an SM Profile is used in conjunction with the SMIP, the SMIP ensures the availability of contextualized, operational data for model building. The present work demonstrates Smart Manufacturing and the application of the SMIP for building several data-centered models for the operation and control of an ex- perimental electrochemical reactor that reduces carbon dioxide (CO 2 ) gas to valuable liquid and gas chemicals, such as alcohols, olefins, and syngas. We describe how the SMIP plays a central role in more effective model building and we demonstrate how the electochemical reactor can be controlled and optimized for the desired products. Use of the SMIP involves the transmission of real-time sensor measurements to a cloud resource so that the operating data are available to all model building experts. The data collection and transmission process is fully automated to greatly reduce the need for manual manipulation of the data. Data-driven machine learning models are used for advanced real-time state estimation, real-time optimization, and model-based feedback control for the reactor. The application models are implemented as a system to monitor the data flow and control the electrochemical reactor with a single visualization interface. SM Profiles are used to demonstrate reusability of the information models for the reactor and the instrumentation. The application packages, algorithms, and user interfaces developed are cast as Docker images in a library to facilitate reusability of the application models.
The lithium-ion battery has demonstrated tremendous economic and social impacts. Upon battery operation under different conditions, lithium changes its chemical state and physical formation, leading to undesired side reactions, e.g., lithium dendrite growth that degrades the cell performance and causes safety concerns. In situ detection and visualization of lithium metal in functional batteries could offer insights with both scientific and industrial significance but remain a frontier challenge. In this work, we demonstrate in situ three-dimensional imaging of lithium whisker using a grating-interferometry-based tricontrast X-ray microtomography method. Our approach explicitly reveals the micromorphology of the electrochemically developed porous lithium whisker with micrometer-level spatial resolution while offering sensitivity to the nanoporosity that is smaller than the nominal resolution limit. Our result reveals valuable structural information on the lithium whisker that is otherwise inaccessible. This method is also readily applicable to a broad range of battery research, including all-solid-state batteries.
Complete theoretical understanding of the most complex superconductors requires a detailed knowledge of the symmetry of the superconducting energy-gap Δ$^α_k$, for all momenta k on the Fermi surface of every band α. While there are a variety of techniques for determining |Δ$^α_k$|, no general method existed to measure the signed values of Δ$^α_k$. Recently, however, a technique based on phase-resolved visualization of superconducting quasiparticle interference (QPI) patterns, centered on a single non-magnetic impurity atom, was introduced. In principle, energy-resolved and phase-resolved Fourier analysis of these images identifies wavevectors connecting all k-space regions where Δ$^α_k$ has the same or opposite sign. But use of a single isolated impurity atom, from whose precise location the spatial phase of the scattering interference pattern must be measured, is technically difficult. Here we introduce a generalization of this approach for use with multiple impurity atoms, and demonstrate its validity by comparing the Δ$^α_k$ it generates to the Δ$^α_k$ determined from single-atom scattering in FeSe where s ± energy-gap symmetry is established. Finally, to exemplify utility, we use the multi-atom technique on LiFeAs and find scattering interference between the hole-like and electron-like pockets as predicted for Δ$^α_k$ of opposite sign.