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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Estimating Switchgrass Biomass Yield and Lignocellulose Composition from UAV-Based Indices

Innovative methods for estimating commercial-scale switchgrass yields and feedstock quality are essential to optimize harvest logistics and biorefinery efficiency for sustainable aviation fuel production. This study utilized vegetation indices (VIs) derived from multispectral images to predict biomass yield and lignocellulose concentrations of advanced bioenergy-type switchgrass cultivars (“Liberty” and “Independence”) under two N rates (28 and 56 kg N ha –1 ). Field-scale plots were arranged in a randomized complete block design (RCBD) and replicated three times at Urbana, IL. Multispectral images captured during the 2021–2023 growing seasons were used to extract VIs. The results show that linear and exponential models outperformed partial least square and random forest models, with mid-August imagery providing the best predictions for biomass, cellulose, and hemicellulose. The green normalized difference vegetation index (GNDVI) was the best univariate predictor for biomass yield (R 2 = 0.86), while a multivariate combination of the GNDVI and normalized difference red-edge index (NDRE) enhanced prediction accuracy (R 2 = 0.88). Cellulose was best predicted using the NDRE (R 2 = 0.53), whereas hemicellulose prediction was most effective with a multivariate model combining the GNDVI, NDRE, NDVI, and green ratio vegetation index (GRVI) (R 2 = 0.44). These findings demonstrate the potential of UAV-based VIs for the in-season estimation of biomass yield and cellulose concentration.

09 BIOMASS FUELS↗
DOE OSTI2021

Imaging moiré flat bands in three-dimensional reconstructed WSe 2 /WS 2 superlattices

Moiré superlattices in transition metal dichalcogenide (TMD) heterostructures can host novel correlated quantum phenomena due to the interplay of narrow moiré flat bands and strong, long-range Coulomb interactions. However, microscopic knowledge of the atomically reconstructed moiré superlattice and resulting flat bands is still lacking, which is critical for fundamental understanding and control of the correlated moiré phenomena. Here we quantitatively study the moiré flat bands in three-dimensional (3D) reconstructed WSe 2 /WS 2 moiré superlattices by comparing scanning tunnelling spectroscopy (STS) of high-quality exfoliated TMD heterostructure devices with ab initio simulations of TMD moiré superlattices. A strong 3D buckling reconstruction accompanied by large in-plane strain redistribution is identified in our WSe 2 /WS 2 moiré heterostructures. STS imaging demonstrates that this results in a remarkably narrow and highly localized K-point moiré flat band at the valence band edge of the heterostructure. A series of moiré flat bands are observed at different energies that exhibit varying degrees of localization. Our observations contradict previous simplified theoretical models but agree quantitatively with ab initio simulations that fully capture the 3D structural reconstruction. Furthermore, our results reveal that the strain redistribution and 3D buckling in TMD heterostructures dominate the effective moiré potential and the corresponding moiré flat bands at the Brillouin zone K points.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Validating deep learning inference during chest X-ray classification for COVID-19 screening

Abstract The new coronavirus unleashed a worldwide pandemic in early 2020, and a fatality rate several times that of the flu. As the number of infections soared, and capabilities for testing lagged behind, chest X-ray (CXR) imaging became more relevant in the early diagnosis and treatment planning for patients with suspected or confirmed COVID-19 infection. In a few weeks, proposed new methods for lung screening using deep learning rapidly appeared, while quality assurance discussions lagged behind. This paper proposes a set of protocols to validate deep learning algorithms, including our ROI Hide-and-Seek protocol, which emphasizes or hides key regions of interest from CXR data. Our protocol allows assessing the classification performance for anomaly detection and its correlation to radiological signatures, an important issue overlooked in several deep learning approaches proposed so far. By running a set of systematic tests over CXR representations using public image datasets, we demonstrate the weaknesses of current techniques and offer perspectives on the advantages and limitations of automated radiography analysis when using heterogeneous data sources.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Molecular Reaction Imaging of a Surface Recombination Process Explains Performance Variation Among Smooth MoS 2 Photoelectrodes

Transition metal dichalcogenides (TMD) such as WSe 2 and MoS 2 are highly efficient and stable light absorbers in TMD∣I − ,I 3 − ∣Pt liquid junction solar cells. It is generally accepted that TMD crystals with a large fraction of exposed edge sites exhibit lower power conversion efficiencies (PCEs) than apparently smooth crystals. However, one open question is why does the PCE vary significantly from one crystal to another? Answering this critical question could lead to robust syntheses for high quality and uniform TMD samples. In this work, we apply nanoscale photoelectrochemical microscopy techniques to study n-type TMD nanoflake∣I − ,I 3 − ∣Pt cells. Using a combination of near-diffraction-limited photocurrent mapping and molecular reaction imaging techniques, we reveal a previously hidden surface recombination process: photogenerated holes in hidden p-type domains travel micron-scale distances parallel to the solid/liquid interface and preferentially react with iodide at step-edges. The overall efficiency of the nanoflake, as evidenced from whole nanoflake-level photoelectrochemical measurements, is dictated by the size, efficiency, and location of n- and p-type domains. Furthermore, these results provide a unifying view of efficiency losses in smooth TMD photoelectrodes and open the possibility to design electrode architectures that leverage the long-range lateral charge transport property for photoelectrocatalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Deep-learning based artificial intelligence tool for melt pools and defect segmentation

Accelerating fabrication of additively manufactured components with precise microstructures is important for quality and qualification of built parts, as well as for a fundamental understanding of process improvement. Accomplishing this requires fast and robust characterization of melt pool geometries and structural defects in images. This paper proposes a pragmatic approach based on implementation of deep learning models and self-consistent workflow that enable systematic segmentation of defects and melt pools in optical images. Deep learning is based on an image-to-image translation–conditional generative adversarial neural network architecture. An artificial intelligence (AI) tool based on this deep learning model enables fast and incrementally more accurate predictions of the prevalent geometric features, including melt pool boundaries and printing-induced structural defects. We present statistical analysis of geometric features that is enabled by the AI tool, showing strong spatial correlation of defects and the melt pool boundaries. The correlations of widths and heights of melt pools with dataset processing parameters show the highest sensitivity to thermal influences resulting from laser passes in adjacent and subsequent layer passes. The presented models and tools are demonstrated on the aluminum alloy and datasets produced with different sets of processing parameters. However, they have universal quality and could easily be adapted to different material compositions. The method can be easily generalized to microstructural characterizations other than optical microscopy.

additive manufacturing↗
DOE OSTI2022

Atomic layer epitaxy of kagome magnet Fe 3 Sn 2 and Sn-modulated heterostructures

Magnetic materials with kagome crystal structure exhibit rich physics, such as frustrated magnetism, skyrmion formation, topological flat bands, and Dirac/Weyl points. Until recently, most studies on kagome magnets have been performed on bulk crystals or polycrystalline films. Here, we report the atomic layer molecular beam epitaxy synthesis of high-quality thin films of topological kagome magnet Fe 3 Sn 2 . The structural and magnetic characterization of Fe 3 Sn 2 on epitaxial Pt(111) identifies highly ordered films with c-plane orientation and an in-plane magnetic easy axis. Studies on the local magnetic structure by anomalous Nernst effect imaging reveal in-plane oriented micrometer size domains. Superlattice structures consisting of Fe 3 Sn 2 and Fe 3 Sn are also synthesized by atomic layer molecular beam epitaxy, demonstrating the ability to modulate the sample structure at the atomic level. The realization of high-quality films by atomic layer molecular beam epitaxy opens the door to explore the rich physics of this system and investigate novel spintronic phenomena by interfacing Fe 3 Sn 2 with other materials.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Detection of Defects in Additively Manufactured Metallic Materials with Machine Learning of Pulsed Thermography Images

Additive manufacturing (AM) is an emerging method for cost-efficient fabrication of nuclear reactor parts. AM of metallic structures for nuclear energy applications is currently based on laser powder bed fusion (LPBF) process, which can introduce internal material flaws, such as pores and anisotropy. Integrity of AM structures needs to be evaluated nondestructively because material flaws could lead to premature failures due to exposure to high temperature, radiation and corrosive environment in a nuclear reactor. Quality control (QC) requires nondestructive evaluation (NDE) of actual AM structures. Pulsed thermography is a potentially promising QC technique because it is scalable to arbitrary structure size. However, detection sensitivity of this method is limited by noises. We investigate separation of signal from noise in thermography images using several machine learning (ML) methods, including new spatio-temporal blind source separation (STBSS) and spatio-temporal sparse dictionary learning (STSDL) methods. Performance of the ML methods is benchmarked using thermography data obtained from imaging stainless steel 316L and Inconel 718 specimens produced LPBF method with imprinted calibrated porosity defects. The ML methods are ranked by F-score and execution runtime. The ML methods with higher accuracy require longer run time. However, this runtime is sufficiently short to perform QC within a realistic time frame.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

TDCOSMO: II. Six new time delays in lensed quasars from high-cadence monitoring at the MPIA 2.2m telescope

In this paper we present six new time-delay measurements obtained from $R_c$-band monitoring data acquired at the Max Planck Institute for Astrophysics (MPIA) 2.2 m telescope at La Silla observatory between October 2016 and February 2020. The lensed quasars HE 0047–1756, WG 0214–2105, DES 0407–5006, 2M 1134–2103, PSJ 1606–2333, and DES 2325–5229 were observed almost daily at high signal-to-noise ratio to obtain high-quality light curves where we can record fast and small-amplitude variations of the quasars. We measured time delays between all pairs of multiple images with only one or two seasons of monitoring with the exception of the time delays relative to image D of PSJ 1606–2333. The most precise estimate was obtained for the delay between image A and image B of DES 0407–5006, where $τ_{AB} = –128.4_{–3.8}^{+3.5}$ d (2.8% precision) including systematics due to extrinsic variability in the light curves. For HE 0047–1756, we combined our high-cadence data with measurements from decade-long light curves from previous COSMOGRAIL campaigns, and reach a precision of 0.9 d on the final measurement. The present work demonstrates the feasibility of measuring time delays in lensed quasars in only one or two seasons, provided high signal-to-noise ratio data are obtained at a cadence close to daily.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Real-time single-element-detection structured illumination optical metrology for laser powder bed fusion

Laser powder bed fusion (LPBF) is a type of metal additive manufacturing which could benefit from improved process monitoring to improve quality control. We demonstrate, for the first time to our knowledge, the coaxial monitoring of melt track formation in steel powder with spatial frequency modulation imaging (SPIFI), an enhanced-resolution imaging technique which uses a photodiode to record one-dimensional images. Using a custom live-display software and a high-speed SPIFI geometry, we offset the SPIFI field of view from the fusing beam to monitor different regions of the LPBF melt pool and surrounding area. This demonstrates the potential of SPIFI to monitor spatial features within the melt pool in real-time with increased data efficiency.

Hunter, Scott (ORCID:0009000886150312)↗
DOE OSTI2022

The Simulation and Design of an On-Chip Superconducting Millimetre Filter-Bank Spectrometer

Abstract Superconducting on-chip filter banks provide a scalable, space saving solution to create imaging spectrometers at millimetre and submillimetre wavelengths. We present an easy to realise, lithographed superconducting filter design with a high tolerance to fabrication error. Using a capacitively coupled $$\lambda /2$$ λ / 2 microstrip resonator to define a narrow ( $$\lambda /\Delta \lambda = 300$$ λ / Δ λ = 300 ) spectral pass band, the filtered output of a given spectrometer channel directly connects to a lumped-element kinetic inductance detector. We show the tolerance analysis of our design, demonstrating $$<11\%$$ < 11 % change in filter quality factor to any one realistic fabrication error and a full filter-bank efficiency forecast to be 50% after accounting for fabrication errors and dielectric loss tangent.

Robson, G. (ORCID:0000000315279326)↗
DOE OSTI2024

Toward Ultra‐High‐Quality‐Factor Wireless Masing Magnetic Resonance Sensing

It has recently been shown that a bolus of hyperpolarized nuclear spins can yield stimulated emission signals similar in nature to maser signals, potentially enabling new ways of sensing hyperpolarized contrast media, including most notably [1- 13 C]pyruvate that is under evaluation in over 50 clinical trials for metabolic imaging of cancer. The stimulated NMR signal emissions lasting for minutes do not require radio-frequency excitation, offering unprecedented advantages compared to conventional MR sensing. However, creating nuclear spin maser emission is challenging in practice due to stringent fundamental requirements, making practical in vivo applications hardly possible using conventional passive MR detectors. Here, in this study, we demonstrate the utility of a wireless NMR maser detector, the quality factor of which was enhanced 22-fold (to 1,670) via parametric pumping. This active-feedback technique breaks the intrinsic fundamental limit of NMR detector circuit quality factor. We show the use of parametric pumping to reduce the threshold requirement for inducing nuclear spin masing at 300 MHz resonance frequency in a preclinical MRI scanner. Indeed, stimulated emission from hyperpolarized protons was obtained under highly unfavorable conditions of low magnetic field homogeneity (T 2 * of 3 ms). Greater gains of the quality factor of the MR detector (up to 1 million) were also demonstrated.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Chemical Imaging of Atmospheric Biomass Burning Particles from North American Wildfires

The effects of biomass burning aerosols (BBA) on radiative forcing and cloud formation depend on chemical composition and the internal structures of individual particles within smoke plumes. To improve our understanding of the chemical and physical properties of BBA emitted at different times of the day and their evolution during atmospheric aging, we conducted a study as a part of the Fire Influence on Regional to Global Environments and Air Quality field campaign. Particle samples were collected onboard a research aircraft from smoke plumes from a wildfire in eastern Oregon during late afternoon and nighttime flights on August 28, 2019. A time-resolved aerosol collector was used to collect samples on substrates for offline spectromicroscopic imaging to investigate the single-particle characteristics of BBA particles. Approximately 20,400 individual particles from 10 selected samples were analyzed using computer-controlled scanning electron microscopy coupled with energy-dispersive X-ray microanalysis, revealing their elemental composition, morphology, and viscosity. Elemental microanalysis indicated that aged potassium is likely found in the form of K 2 SO 4 , KNO 3 , and possible K-organic salts. Further chemical speciation and carbon bonding mapping within individual particles were conducted using synchrotron-based scanning transmission X-ray microscopy (STXM) coupled with near edge X-ray absorption fine structure (NEXAFS) spectroscopy. Real-time, water-soluble light absorption measurements were acquired using a particle-into-liquid sampler instrument coupled to a liquid waveguide capillary cell and total organic analyzer. In the late afternoon samples, 65% of the total particle number population consisted entirely of organic components, compared to 46% in the nighttime particles. These differences were attributed to discrepancies in composition at the time of emission and to the daytime condensation and accumulation of photochemically formed secondary organic material on existing BBA particles, a process that halts at night. Microscopy images indicated that particle viscosity was lower in the nighttime particles (<10 1 Pa·s), likely due to increased relative humidity and a higher contribution from hygroscopic inorganic components. The chemical heterogeneity of individual particles was quantified using STXM-derived mixing state parameters. The nature of carbon bonding within individual particles was inferred from the extent of carbon sp 2 hybridization derived from NEXAFS spectra. Average percentages of sp 2 hybridization range between 40% and 60%, with no noticeable differences between late afternoon and nighttime flights. These findings were compared with the online optical properties of both late afternoon and nighttime smoke plumes, providing valuable insights into the complex relationship between chemical composition and optical properties of BBA particles at different times of the day.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Enhanced detection of volatile organic compounds (VOCs) by caffeine modified carbon nanotube junctions

Detection of vapor phase analytes and gas molecules is primarily essential for numerous applications including medical diagnostic, environmental monitoring, chemical threat detection, food quality control, and chemical warfare agents. Here, we developed Caffeine functionalized carbon nanotubes (CNTs) random network based vapor quantum resistive sensors (vQRSs) for volatile organic compounds (VOCs) detection. The localized transmission electron microscopy (TEM) image and energy dispersive X-ray spectroscopy (EDS) confirm migration of caffeine to CNT–CNT junction (CNTj). We have recorded more than one order of magnitude higher sensitivity and unique selectivity towards selective VOCs (e.g., acetone, toluene, ethanol, methanol, etc.) with short response time (few seconds) for caffeine modified CNT junction (Caf-CNTj) vQRSs. Molecular dynamics (MD) calculations suggest that the sensitivity of the sensor is diffusion limited, with an advective contribution for ethanol. Furthermore, the designed Caf-CNTj sensors can potentially be important candidate to be a part of electronic nose used for various application such as breath analysis and food quality monitoring.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Ptychography at the Linac Coherent Light Source in a parasitic geometry

X-ray free-electron lasers (FELs) are being recognized as a powerful tool in an ever-increasing number of research fields, but are very limited as to the number of experiments that they can support. This work shows that more beamtime could be made available by using `parasitic' geometries, where a secondary experiment uses the X-ray beam that the primary experiment does not utilize. The first successful ptychography experiment, a scanning coherent diffractive imaging technique, in a parasitic geometry at an X-ray FEL is demonstrated. Utilizing the CXI hutch at the Linac Coherent Light Source (LCLS), it is shown that the obtained data are of high quality and that characterizing the beam using ptychography can be much faster than traditional imprinting methods.

42 ENGINEERING↗
DOE OSTI2020

Photonic devices fabricated from (111)‐oriented single crystal diamond

Abstract Diamond is a material of choice in the pursuit of integrated quantum photonic technologies. So far, the majority of photonic devices fabricated from diamond are made from (100)‐oriented crystals. In this work, we demonstrate a methodology for the fabrication of optically active membranes from (111)‐oriented diamond. We use a liftoff technique to generate membranes, followed by chemical vapor deposition of diamond in the presence of silicon to generate homogenous silicon vacancy color centers with emission properties that are superior to those in (100)‐oriented diamond. We further use the diamond membranes to fabricate microring resonators with quality factors exceeding ~ 3000. Supported by finite‐difference time‐domain calculations, we discuss the advantages of (111)‐oriented structures as building blocks for quantum nanophotonic devices. image

Regan, Blake↗
DOE OSTI2024

Paired autoencoders for likelihood-free estimation in inverse problems

Abstract We consider the solution of nonlinear inverse problems where the forward problem is a discretization of a partial differential equation. Such problems are notoriously difficult to solve in practice and require minimizing a combination of a data-fit term and a regularization term. The main computational bottleneck of typical algorithms is the direct estimation of the data misfit. Therefore, likelihood-free approaches have become appealing alternatives. Nonetheless, difficulties in generalization and limitations in accuracy have hindered their broader utility and applicability. In this work, we use a paired autoencoder framework as a likelihood-free estimator (LFE) for inverse problems. We show that the use of such an architecture allows us to construct a solution efficiently and to overcome some known open problems when using LFEs. In particular, our framework can assess the quality of the solution and improve on it if needed. We demonstrate the viability of our approach using examples from full waveform inversion and inverse electromagnetic imaging.

Chung, Matthias (ORCID:0000000178224539)↗
DOE OSTI2024

Plant Disease Detection Technology Assessment

Visual inspections by US Customs and Border Protection agriculture specialists have identified approximately 20,000 regulated, quarantined pests each year in agricultural products entering the United States. Most of these pests identified in the Agriculture Quarantine and Inspection (AQI) program are insects. Many pathogens are difficult to detect in agricultural products, particularly in early stages of infection. New technologies can help to detect plant pathogens and the diseases that they cause. This technology assessment was performed for the US Department of Homeland Security Science and Technology Directorate (DHS S&T) through the Food, Agriculture, and Veterinary Defense (FAV-D) program to identify emerging technologies that could address this hard problem. These emerging technologies differ in their diagnostic sensitivities and specificities, as well as in their measurement time and training requirements. New instruments that detect volatile organic compounds characteristic of plant disease or pathogens could provide a less invasive inspection method. Dogs, which can successfully detect many concealed agricultural products, have also been trained to detect some plant pests and pathogens. Simple immunological tests offer sensitive and specific detection of many pathogens at the point of use. Advanced imaging methods that use AI to sort fruits and vegetables and recognize anomalies at high speeds could be used in cooperation with exporters to improve food quality and reduce pests. Advances in nucleic acid–based detection methods that have become gold standards for confirmatory diagnostics are now making those methods available for faster, point-of-use detection. These new methods should be developed in the context of AQI operational requirements, which apply risk-based sampling protocols to protect agriculture and facilitate commerce and passenger transit.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Seismic Elastic Double-Beam Characterization of Faults and Fractures for CO₂ Storage Site Selection

Site characterization for underground injection and storage of gigatonne-scale CO₂ requires reliable and cost-effective methods to detect and characterize faults and fractures and to assess their stress state and fault activation potential. This is critical, as wastewater injection and disposal have been shown to activate faults and induce earthquakes, and CO₂ leakage remains a key concern for long-term storage. In this project, we developed seismic methods to detect and characterize large-scale sedimentary and crystalline basement faults and associated small-scale fractures below conventional seismic imaging resolution using multicomponent (9C) surface seismic data. Machine learning was used to automatically interpret large-scale faults, providing key information for estimating the maximum magnitude of potential induced earthquakes. High-fidelity imaging was achieved by exploiting redundancy across multiple elastic wave modes, where independent images from different modes and frequencies cross-validate each other. We also used our nonlinear signal comparison (NLSC) method for ground roll removal, improving data quality in complex near-surface conditions. The methods were validated using field data acquired in central Montana. Results show that basement faults extend into the sedimentary section and that small-scale fractures are widespread above the basement. The inferred stress orientation is consistent with regional stress data, and the estimated maximum induced earthquake magnitude is small (Mw ~2.3). The developed workflow provides a practical approach for fault and fracture characterization and for assessing induced seismicity and leakage risk. It is directly applicable to CO₂ storage site selection and to other subsurface systems.

02 PETROLEUM↗