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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Dynamic Nuclear Polarization Enhanced 113 Cd Solid-State Nuclear Magnetic Resonance Spectroscopy Reveals CdSe Nanocrystals with Triangular Two-Dimensional Projections are Terminated by {100} Facets

Surface structure plays an important role in particle growth and determining the chemical and photophysical properties of semiconductor nanocrystals (NCs). Therefore, there is a need for structural tools that can characterize and detect different surface facets. Here, we sought to investigate the structure of {111} CdSe facets by applying dynamic nuclear polarization (DNP) enhanced 113 Cd and 77 Se solid-state nuclear magnetic resonance (SSNMR) spectroscopy to zinc-blende CdSe NCs that exhibit triangular two-dimensional (2D) projections within transmission electron microscope (TEM) images. It was originally hypothesized in the literature that these CdSe NCs were tetrahedral in shape and terminated by facets from the {111} family of lattice planes of the zinc-blende structure. Surprisingly, we observe 113 Cd NMR spectra indicating that the primary facets are from the {100} family of lattice planes. We also obtained DNP-enhanced 113 Cd and 77 Se SSNMR spectra of recently reported right trigonal bipyramidal (rTriBP) CdSe NCs grown by seeded growth. The 113 Cd NMR spectra rTriBP CdSe NCs are consistent with {100} surface facets. TEM images show that rTriBP NCs may also exhibit triangular 2D projections that are similar to the so-called tetrahedral NCs. Based upon these results, we conclude that the so-called tetrahedral NCs are predominantly terminated by {100} surface facets and most likely have the same shape as rTriBP NCs. These results highlight the need for multiple complementary techniques when assigning the shape and surface termination of NCs.

Santhiran, Anuluxan [Ames Laboratory (AMES), Ames,↗
DOE OSTI2020

Characterizing natural fractures and sub-seismic faults for well completion of Marcellus shale in the MSEEL Consortium project, West Virginia, USA

The Middle Devonian Marcellus shale play has emerged as a major world-class hydrocarbon accumulation and represents one of the largest and most prolific shale plays in the world. According to many outcrop studies in the region, natural fractures are well developed in the Marcellus Shale. However, evaluating fractures in the subsurface is often a significant challenge due to a lack of sufficient data. Therefore, in the Marcellus Shale Energy and Environment Laboratory (MSEEL) consortium project, significant efforts have been made to acquire high-quality image logs in the Marcellus laterals. The project provided tremendous opportunities to characterize the natural fractures and sub-seismic faults and to evaluate their impact on well stimulation. In this study, about 70,000 ft of acquired high-resolution logging while drilling (LWD) acoustic images from five long laterals located in Monongalia County, West Virginia, were processed and interpreted. In addition, the study used high-quality micro-resistivity images from a pilot well, allowing the evaluation of natural fractures in the entire Marcellus vertical sequence. Based on the available acoustic images, the natural fractures were classified into three basic categories: high-amplitude fractures, low-amplitude fractures, and faults. Further, larger open fractures can also be determined when a low-amplitude fracture is evident on caliper images. The fractures in the Marcellus usually have a medium to high angle dip; however, multiple fracture sets in terms of strike orientation were clearly observed in all the laterals. The fracture set with a strike at NE-SW (or 60-240 deg) seems to be the predominant one in all the wells. A few other sets, including those with N-S, NWW-SEE, and E-W strikes, were also observed. Several sub-seismic faults, with mostly a low dip angle and a NE-SW strike, have also been seen in two of the laterals. The fracture density is variable across all the laterals, ranging from very low (or none) to very high (up to 5 fractures per ft). The average fracture density for all the laterals is about 1 fracture per 10 ft. In the vertical sequence, the natural fracture development showed a clear preference for shale or shaly facies over carbonate-rich or thin limestone layers. The interpreted fracture and fault data were used as input data for the stimulation design with the purpose of better understanding the fractures’ impact on well stimulation. Production data from the laterals were also used to evaluate the natural fractures’ influence on well performance. The quality image database and the consistent interpretation results for the entire project enabled a systematic approach to characterizing fractures and, more importantly, to evaluating the impact of fractures on well stimulation and production.

03 NATURAL GAS↗
DOE OSTI2020

3D Tomography and Image Processing Using Fast Neutrons Final Report

This document is the final report for project OR16-ML-3DTomography-PD3Jb, “3D Tomography and Image Processing Using Fast Neutrons.” This project was motivated by the availability of three-dimensional (3D) tomographic neutron transmission and induced-reaction data following the development of associated-particle imaging (API) systems with two-dimensional (2D) or “area” neutron detectors such as the Advanced Portable Neutron Imaging System, which is capable of simultaneous measurement of transmission and induced-reaction neutrons.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2

Motion-activated wildlife cameras (or “camera traps”) are frequently used to remotely and noninvasively observe animals. The vast number of images collected from camera trap projects has prompted some biologists to employ machine learning algorithms to automatically recognize species in these images, or at least filter-out images that do not contain animals. These approaches are often limited by model transferability, as a model trained to recognize species from one location might not work as well for the same species in different locations. Furthermore, these methods often require advanced computational skills, making them inaccessible to many biologists. We used 3 million camera trap images from 18 studies in 10 states across the United States of America to train two deep neural networks, one that recognizes 58 species, the “species model,” and one that determines if an image is empty or if it contains an animal, the “empty-animal model.” Our species model and empty-animal model had accuracies of 96.8% and 97.3%, respectively. Furthermore, the models performed well on some out-of-sample datasets, as the species model had 91% accuracy on species from Canada (accuracy range 36%–91% across all out-of-sample datasets) and the empty-animal model achieved an accuracy of 91%–94% on out-of-sample datasets from different continents. Our software addresses some of the limitations of using machine learning to classify images from camera traps. By including many species from several locations, our species model is potentially applicable to many camera trap studies in North America. We also found that our empty-animal model can facilitate removal of images without animals globally. We provide the trained models in an R package (MLWIC2: Machine Learning for Wildlife Image Classification in R), which contains Shiny Applications that allow scientists with minimal programming experience to use trained models and train new models in six neural network architectures with varying depths.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Adaptive machine learning for time-varying systems: low dimensional latent space tuning

Machine learning (ML) tools such as encoder-decoder convolutional neural networks (CNN) can represent incredibly complex nonlinear functions which map between combinations of images and scalars. For example, CNNs can be used to map combinations of accelerator parameters and images which are 2D projections of the 6D phase space distributions of charged particle beams as they are transported between various particle accelerator locations. Despite their strengths, applying ML to time-varying systems, or systems with shifting distributions, is an open problem, especially for large systems for which collecting new data for re-training is impractical or interrupts operations. Particle accelerators are one example of large time-varying systems for which collecting detailed training data requires lengthy dedicated beam measurements which may no longer be available during regular operations. We present a novel method of adaptive ML for time-varying systems. Our approach is to map very high (N ≈ 100k) dimensional inputs (a combination of scalar parameters and images) into the low dimensional (N ≈ 2) latent space at the output of the encoder section of an encoder-decoder CNN. We then actively tune the low dimensional latent space-based representation of complex system dynamics by the addition of an adaptively tuned feedback vector directly before the decoder sections builds back up to our image-based high-dimensional phase space density representations. This method allows us to learn correlations within and to quickly tune the characteristics of incredibly large parameter space systems and to track their evolution in real time based on feedback without massive new data sets for re-training. We demonstrate that our method can accurately predict and track the phase space of charged particle beams at various locations in a particle accelerator by adaptively adjusting in real-time while the unknown input beam distribution of the accelerator is changing in shape, charge, and offset and while the RF system of the accelerator itself is also changing in an unpredictable way. For FACET-II we demonstrate that such an approach has the potential to use transverse deflecting cavity and energy spread spectrum beam measurements to accurately predict 2D projections of the 6D phase space of the electron beam at the plasma wakefield acceleration interaction point where such diagnostics are unavailable.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Tomographic optical emission spectroscopy of an atmospheric pressure plasma jet and surface ionization waves on planar and structured surfaces

Here in this paper, an approach for 3D plasma structure diagnostics using tomographic optical emission spectroscopy (Tomo-OES) of a nanosecond pulsed atmospheric pressure plasma jet (APPJ) is presented. In contrast to the well-known Abel inversion, Tomo-OES does not require cylindrical symmetry to recover 3D distributions of plasma light emission. Instead, many 2D angular projections are measured with intensified cameras and the multiplicative algebraic reconstruction technique is used to recover the 3D distribution of light emission. This approach solves the line-of-sight integration problem inherent to optical diagnostics, allowing recovery of localized OES information within the plasma that can be used to better infer plasma parameters within complex plasma structures. Here, Tomo-OES was applied to investigate an APPJ operated with helium in ambient air and impinging on planar and structured dielectric surfaces. Surface charging caused the guided streamer from the APPJ to transition to a surface ionization wave (SIW) that propagated along the surface. The SIW experienced variable geometrical and electrical material properties as it propagated, leading to 3D configurations that were non-symmetric and spatially complex. Light emission from He, N$_2^+$, and N2 were imaged at ten angular projections and the respective time-resolved 3D emission distributions in the plasma were then reconstructed. The spatial resolution of each tomographic reconstruction was 7.4 µm and the temporal resolution was 5 ns, sufficient to observe the guided streamer and the effects of the structured surface on the SIW. Emission from He showed the core of the jet and emission from N$_2^+$ and N2 indicated effects of entrainment of ambient air. Penning ionization of N2 created a ring or outer layer of N$_2^+$ that spatially converged to form the 'plasma bullet' or spatially diverged across a surface as part of a SIW. The SIW entered trenches of size 150 µm, leading to decreases in plasma light emission in regions above the trenches. The plasma light emission was higher in some regions with trenches, possibly due to effects of field enhancement.

plasma interactions with complex surfaces↗
DOE OSTI2023

Hyperspectral Light Sheet Raman Imaging of Leaf Metabolism (Final Report)

This final technical report details the accomplishment and findings of a project that developed technology related to imaging metabolic processes in plants. The report gives technical details of the design, construction and characterization of a Raman microscope system capable of identifying chemical species using the wavelength shifts of scattered excitation light. Some of the unique aspects of the design include: use of a Bessel beam excitation source, which allow long excitation lines that are resistant to scattering in complex media such as plants; excitation at 785 nm to avoid fluorescence emission that can occur in photosynthetic organisms; and a flat optical design that makes construction and alignment significantly simpler. This project also used stable isotope labeling to look at photorespiratory flux in the leaves of three different types of plants that have different photosynthesis pathways. Gas chromatography–mass spectrometry was used to look at which metabolites and amino acids had altered oxygen, carbon and hydrogen uptake under various conditions of environmental oxygen. This generated a rich data set that is being further analyzed to understand metabolic pathways in plants. This project also used the natural fluorescence in sorghum and rice to conduct an extensive experiment across different developmental stages. Spectral differences were identified at most of the developmental stages and these can be attributed to differential pigment and pigment-protein complexes present in different relative abundances as the plant develops.

09 BIOMASS FUELS↗
DOE OSTI2023

Imaging the electron charge density in monolayer MoS 2 at the Ångstrom scale

Four-dimensional scanning transmission electron microscopy (4D-STEM) has recently gained widespread attention for its ability to image atomic electric fields with sub-Ångstrom spatial resolution. These electric field maps represent the integrated effect of the nucleus, core electrons and valence electrons, and separating their contributions is non-trivial. In this paper, we utilized simultaneously acquired 4D-STEM center of mass (CoM) images and annular dark field (ADF) images to determine the projected electron charge density in monolayer MoS 2 . We evaluate the contributions of both the core electrons and the valence electrons to the derived electron charge density; however, due to blurring by the probe shape, the valence electron contribution forms a nearly featureless background while most of the spatial modulation comes from the core electrons. Our findings highlight the importance of probe shape in interpreting charge densities derived from 4D-STEM and the need for smaller electron probes.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Smart quantum statistical imaging beyond the Abbe-Rayleigh criterion

The wave nature of light imposes limits on the resolution of optical imaging systems. For over a century, the Abbe-Rayleigh criterion has been utilized to assess the spatial resolution limits of imaging instruments. Recently, there has been interest in using spatial projective measurements to enhance the resolution of imaging systems. Unfortunately, these schemes require a priori information regarding the coherence properties of “unknown” light beams and impose stringent alignment conditions. Here, we introduce a smart quantum camera for superresolving imaging that exploits the self-learning features of artificial intelligence to identify the statistical fluctuations of unknown mixtures of light sources at each pixel. This is achieved through a universal quantum model that enables the design of artificial neural networks for the identification of photon fluctuations. Our protocol overcomes limitations of existing superresolution schemes based on spatial mode projections, and consequently provides alternative methods for microscopy, remote sensing, and astronomy.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Mapping Glacier Basal Sliding Applying Machine Learning

During the RESOLVE project (“High-resolution imaging in subsurface geophysics: development of a multi-instrument platform for interdisciplinary research”), continuous surface displacement and seismic array observations were obtained on Glacier d’Argentière in the French Alps for 35 days in May 2018. The data set is used to perform a detailed study of targeted processes within the highly dynamic cryospheric environment. In particular, the physical processes controlling glacial basal motion are poorly understood and remain challenging to observe directly. Especially in the Alpine region for temperate based glaciers where the ice rapidly responds to changing climatic conditions and thus, processes are strongly intermittent in time and heterogeneous in space. Spatially dense seismic and Global Positioning System (GPS) measurements are analyzed applying machine learning to gain insight into the processes controlling glacial motions of Glacier d’Argentière. Using multiple bandpass-filtered copies of the continuous seismic waveforms, we compute energy-based features, develop a matched field beamforming catalog and include meteorological observations. Features describing the data are analyzed with a gradient boosting decision tree model to directly estimate the GPS displacements from the seismic noise. We posit that features of the seismic noise provide direct access to the dominant parameters that drive displacement on the highly variable and unsteady surface of the glacier. The machine learning model infers daily fluctuations and longer term trends. The results show on-ice displacement rates are strongly modulated by activity at the base of the glacier. The techniques presented provide a new approach to study glacial basal sliding and discover its full complexity.

58 GEOSCIENCES↗
DOE OSTI2021

Programs and Code for Geothermal Exploration Artificial Intelligence

The scripts below are used to run the Geothermal Exploration Artificial Intelligence developed within the "Detection of Potential Geothermal Exploration Sites from Hyperspectral Images via Deep Learning" project. It includes all scripts for pre-processing and processing, including: - Land Surface Temperature K-Means classifier - Labeling AI using Self Organizing Maps (SOM) - Post-processing for Permanent Scatterer InSAR (PSInSAR) analysis with SOM - Mineral marker summarizing - Artificial Intelligence (AI) Data splitting: creates data set from a single raster file - Artificial Intelligence Model: creates AI from a single data set, after splitting in Train, Validation and Test subsets - AI Mapper: creates a classification map based on a raster file

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Elementary Particle Interactions (Final Technical Report)

Neutrinos provide a promising window to probe a wide range of fundamental physics. The past two decades have brought several remarkable neutrino-related discoveries indicating that one may answer the most sought after question of our matter dominated universe from within the neutrino sector. The global neutrino physics program is currently focused on studying neutrino oscillations, and to understand the implications of this phenomena on the evolution of the universe. Addressing all of these open questions and completing the story of neutrino oscillations requires technological advances in neutrino detection. Liquid argon time projection chambers (LArTPCs) are imaging detectors that offer fine-grained tracking, calorimetry and scalability, and are increasingly favored for the next generation of large neutrino detectors. Through leading technical and scientific roles on MicroBooNE, Short-Baseline Near Detector (SBND) and the Deep Underground Neutrino Experiment (DUNE) LArTPC neutrino experiments, the research program supported through this funding has significantly advanced the understanding of neutrino properties along with technical development of LArTPCs for both short- and long-baseline neutrino oscillations physics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Rare Neutrino Interaction and $\pi^0$ Production Cross Sections with MicroBooNE

MicroBooNE is a Liquid Argon Time Projection Chamber, able to image neutrino interactions with excellent spatial and timing resolution, enabling the identification of complex final states resulting from neutrino-nucleus interactions. This poster will provide an overview of measurements for rare final states, such as and η production. These processes both provide unique sensitivities to the interplay between nucleon-level cross-section physics and nuclear-level physics, as well as account for sources of background in proton decay experiments. Furthermore, this poster showcases MicroBooNE’s measurements of total and first-order differential cross-sections on argon for muon neutrino interactions producing neutral pions in the final-state. These interactions will dominate the event rates observed at forthcoming high-precision neutrino experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Calibrating the DUNE LArTPC detectors for precision physics

The Deep Underground Neutrino Experiment (DUNE) is an international collaboration focused on studying neutrino oscillation over a long baseline (1300 km). DUNE will make use of a near detector and $\mathcal{O}$(GeV) neutrino beam originating at Fermilab in Batavia, IL, and a far detector operating 1.5 km underground at the Sanford Underground Research Facility in Lead, South Dakota. The near and far detectors will use the LArTPC (Liquid Argon Time Projection Chamber) technology to image neutrino interactions. In order to make precise physics measurements at DUNE, such as the amount of CP violation in the neutrino sector, it is essential to be able to accurately reconstruct particle energies and other kinematic quantities; this in turn necessitates an extensive calibration program for DUNE s LArTPC detectors. In this presentation, we describe the requirements for calibrating the DUNE detectors, emphasizing the challenges of massive multi-kiloton LArTPC detectors which are to operate for multiple years deep underground. A preliminary DUNE detector calibration program, including use of both dedicated calibration hardware and cosmogenic/beam-induced calibration sources, is presented. First results on detector calibration at the ProtoDUNE-SP prototype detector located at CERN, and associated impact on calibrations at the DUNE far detector, are also emphasized.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

A Generative Model for Realistic Galaxy Cluster X-Ray Morphologies

Abstract The X-ray morphologies of clusters of galaxies display significant variations, reflecting their dynamical histories and the nonlinear dependence of X-ray emissivity on the density of the intracluster gas. Qualitative and quantitative assessments of X-ray morphology have long been considered a proxy for determining whether clusters are dynamically active or “relaxed.” Conversely, the use of circularly or elliptically symmetric models for cluster emission can be complicated by the variety of complex features realized in nature, spanning scales from megaparsecs down to the resolution limit of current X-ray observatories. In this work, we use mock X-ray images from simulated clusters from The Three Hundred project to define a basis set of cluster image features. We take advantage of the clusters’ approximate self-similarity to minimize the differences between images before encoding the remaining diversity through a distribution of high-order polynomial coefficients. Principal component analysis then provides an orthogonal basis for this distribution, corresponding to natural perturbations from an average model. This representation allows novel, realistically complex X-ray cluster images to be easily generated, and we provide code to do so. The approach provides a simple way to generate training data for cluster image analysis algorithms and could be straightforwardly adapted to generate clusters displaying specific types of features or selected by physical characteristics available in the original simulations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

System and method for sub micron additive manufacturing

An apparatus is disclosed for performing an additive manufacturing operation to form a structure by processing a photopolymer resist material. The apparatus may incorporate a laser for generating a laser beam, and a tunable mask for receiving the laser beam which has an optically dispersive element. The mask splits the laser beam into a plurality of emergent beams each having a subplurality of beamlets of varying or identical intensity, with each beamlet emerging from a unique subsection of illuminated regions of the mask. A collimator collimates at least one of the emergent beams to form a collimated beam. One or more focusing elements focuses the collimated beam into a focused beam which is projected as a focused image plane on or within the resist material. The focused beam simultaneously illuminates a layer of the resist material to process an entire layer in a parallel fashion.

Saha, Sourabh Kumar↗
DOE OSTI2022

Method and system for unsynchronized structured lighting

A system and method to capture the surface geometry a three-dimensional object in a scene using unsynchronized structured lighting is disclosed. The method and system includes a pattern projector configured and arranged to project a sequence of image patterns onto the scene at a pattern frame rate, a camera configured and arranged to capture a sequence of unsynchronized image patterns of the scene at an image capture rate, and a processor configured and arranged to synthesize a sequence of synchronized image frames from the unsynchronized image patterns of the scene. Each of the synchronized image frames corresponds to one image pattern of the sequence of image patterns.

Taubin, Gabriel↗