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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Multi-electrode/multi-modal atmospheric pressure glow discharge plasma ionization device

Apparatus include an atmospheric pressure glow discharge (APGD) analyte electrode defining an analyte discharge axis into an APGD volume, and a plurality of APGD counter electrodes having respective electrical discharge ends directed to the APGD volume, wherein the APGD analyte electrode and the APGD counter electrodes are configured to produce an APGD plasma in the APGD volume with a voltage difference between the APGD analyte electrode and one or more of the AGPD counter electrodes. An electrode can be integrated into an ion inlet. Apparatus can be configured to perform auto-ignition and/or provide multi-modal operation through selectively powering electrodes. Electrode holder devices are disclosed. Related methods are disclosed.

Koppenaal, David W.↗
DOE OSTI2023

Expanding Accurate Person Recognition to New Altitudes and Ranges: The BRIAR Dataset

Face recognition technology has advanced significantly in recent years due largely to the availability of large and increasingly complex training datasets for use in deep learning models. These datasets, however, typically comprise images scraped from news sites or social media plat-forms and, therefore, have limited utility in more advanced security, forensics, and military applications. These applications require lower resolution, longer ranges, and ele-vated viewpoints. To meet these critical needs, we collected and curated the first and second subsets of a large multi-modal biometric dataset designed for use in the research and development (R&D) of biometric recognition technolo-gies under extremely challenging conditions. Thus far, the dataset includes more than 350,000 still images and over 1,300 hours of video footage of approximately 1,000 sub-jects. To collect this data, we used Nikon DSLR cameras, a variety of commercial surveillance cameras, specialized long-rage R&D cameras, and Group 1 and Group 2 UAV platforms. The goal is to support the development of algorithms capable of accurately recognizing people at ranges up to 1,000 m and from high angles of elevation. These ad-vances will include improvements to the state of the art in face recognition and will support new research in the area of whole-body recognition using methods based on gait and anthropometry. This paper describes methods used to col-lect and curate the dataset, and the dataset's characteristics at the current stage.

Brogan, Joel↗
DOE OSTI2023

Ultrafast CMOS image sensors and data-enabled super-resolution for multimodal radiographic imaging and tomography

We summarize recent progress in ultrafast Complementary Metal Oxide Semiconductor (CMOS) image sensor development and the application of neural networks for post-processing of CMOS and charge-coupled device (CCD) image data to achieve sub-pixel resolution (thus $super$-$resolution$). The combination of novel CMOS pixel designs and data-enabled image post-processing provides a promising path towards ultrafast high-resolution multi-modal radiographic imaging and tomography applications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Anode Upcycling via Tailored Solvent Treatment

To achieve a truly closed-loop direct recycling process for lithium-ion batteries, all component materials must be recovered. To date, direct recycling method development has primarily focused on the high-value transition-metal cathode materials, while the inherently lower-value graphite has been challenging to recover in a cost-effective manner. However, end-of-life graphite contains a unique engineered value due to the presence of the solid electrolyte interphase (SEI). Growth of the SEI during the cell's active lifetime stabilizes the electronically reactive graphite surface through an irreversible consumption of Li, and thus necessitates both excess lithiation of the cathode and a costly and time-intensive formation procedure during manufacturing. An optimized pre-formed SEI that capitalizes on existing SEI components from end-of-life batteries has the potential to significantly reduce cathode lithiation requirements and eliminate the critical bottleneck of formation cycling during cell remanufacturing. Further, retaining Li at the anode obviates the need for a separate Li leaching and recovery step, improving the overall efficiency of the direct recycling line. In this work, we present a novel approach to "upcycling" spent graphite through use of tailored chemical treatment to remove adverse (i.e., highly resistive and/or poorly passivating) SEI species while retaining beneficially passivating components. We have explored a rational set of solvents spanning a range of polarity, proticity, and molecular size to evaluate structure-property-performance relationships between applied solvent(s), removed and remaining SEI species, and electrochemical response of the resulting graphite product. Further, we have developed and optimized a robust and holistic analysis procedure that couples symmetric-cell electrochemical testing, multi-modal materials characterization, and advanced electrochemical modeling. These analysis results inform a set of correlative metrics for graphite performance relative to both solvent properties and upcycled SEI composition. We demonstrate effective tunability in the residual SEI composition by varying solvent identity and concentration, and report on several promising solvent systems that achieve comparable or performance to pristine graphite.

anode recycling↗
DOE OSTI2025

Elucidating the corrosion mechanism of Ni-based superalloys in the presence of uranium-containing chloride molten salt

The United States Department of Energy (DOE) is committed to the advancement of nuclear reactor technology through initiatives such as the Advanced Reactor Development Program (ARDP), in an effort to diversify the United States energy portfolio towards more sustainable energy options. The ARDP includes demonstration by industry partners of molten chloride fast reactors (MCFRs). Construction of MCFRs requires qualified nuclear structural materials. Unfortunately, there are no current materials that are fully qualified by the Nuclear Regulatory Commission for the construction of molten salt reactors, including MCFRs. Adapting current structural material qualifications requires expansion of our current knowledgebase on the property-performance relationships regarding corrosion performance. In this investigation, we assess microstructural changes in a Ni-based superalloy after exposure to a UCl3¬-containing chloride salt eutectic mixture through a correlated multi-modal approach combining several advanced characterization techniques, including scanning electron microscopy/focused ion beam (SEM/FIB) and transmission electron microscopy (TEM). SEM/FIB analysis will illustrate changes in elemental composition, microstructure, and isotopic information acquired from energy x-ray dispersive spectroscopy (EDS), electron backscatter diffraction (EBSD), and secondary ion mass spectroscopy (SIMS), respectively. This information will then aid in identifying localized regions to elucidate the corrosion mechanism with TEM through a combination of electron diffraction, electron energy loss spectroscopy (EELS), and additional EDS. The findings from this investigation will further expand our assessment of the corrosion performance of structural materials in molten salt chloride systems, aiding to developing fully qualified materials for construction of MCFRs.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Elucidating the corrosion mechanism of commercial Ni-based Superalloys in UCl3 containing-chloride Molten Salt Systems

Molten salt reactors (MSRs) have gained renewed interest, providing several advantages over their predecessors, including the capability to consume spent fuels, enhancing the environmental sustainability of the uranium fuel cycle. For example, molten chloride fast reactors (MCFRs) can reach criticality with molten chloride spent fuel containing high concentrations of impurities, such as actinide products like uranium chloride (UCl3). However, the redox potential of chloride molten salt fuels may change in the presence of these impurities, dictating their corrosivity and in turn the corrosion performance of structural components, such as those constructed from nickel (Ni)-based alloys. The purpose of this investigation is to assess the extent of corrosion of Ni-based alloy, Inconel 617, when exposed to UCl3-LiCl-KCl eutectic salt. Inconel 617 one of only six structural materials that are fully qualified by the American Society for Mechanical Engineers (ASME) Boiler and Pressure Vessel Code for high-temperature nuclear reactor components, making it a technologically mature material to consider for constructing MCFRs. Inconel 617 specimens were submerged in a static LiCl-KCl-UCl3 eutectic salt mixture heated at 700 C for 1000 h under an inert atmosphere. Upon completion, the extent of corrosion was analyzed through a multi-modal characterization approach spanning the engineering to nanoscale, employing computed tomography, focused-ion beam, and transmission electron microscopy techniques. Results from this investigation will enhance our understanding of property-to-performance relationships of candidate structural materials for MSRs with respect to corrosion resistance and interactions between the salt and alloy interface.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Hiding-in-Plain-Sight (HiPS) Attack on CLIP for Targetted Object Removal from Images

Machine learning models are known to be vulnerable to adversarial attacks, but prior works have mostly focused on single-modalities. With the rise of large multi-modal models (LMMs) like CLIP, which combine vision and language capabilities, new vulnerabilities have emerged. However, these multimodal targeted attacks aim to completely change the model's output to what the adversary wants. In many realistic scenarios, an adversary might seek to make only subtle modifications to the output, so that the changes go unnoticed by downstream models or even by humans. We introduce Hiding-in-Plain-Sight (HiPS) attacks, a novel class of adversarial attacks that subtly modifies model predictions by selectively concealing target object(s), as if the target object was absent from the scene. We propose two HiPS attack variants, HiPS-cls and HiPS-cap, and demonstrate their effectiveness in transferring to downstream image captioning models, such as CLIP-Cap, for targeted object removal from image captions.

Daw, Arka [ORNL] (ORCID:0009000633191271)↗
DOE OSTI2025

Characterization of the degradation of gamma-irradiated elastomers using Raman spectroscopy

This report presents key findings from Raman spectroscopic analysis of gamma-irradiated rubber samples extracted from a laminated lead-damped rubber (LDR) seismic isolation device. The samples were exposed to gamma radiation from a 60Co source in a Foss Therapy Services gamma irradiator, reaching absorbed doses up to 1600 kGy. A distinct threshold near 400 kGy was identified, beyond which significant spectral changes were observed. Two Raman peaks - at approximately 425 cm-1 and 2440 cm-1 - were tracked as a function of dose using Gaussian fitting. The 425 cm-1 peak, attributed to sulfur–sulfur (S–S) bond stretching (resulting from vulcanization of the rubber), exhibited a dose-dependent upshift, indicating radiation-induced crosslinking within the sulfur-based polymer network. Conversely, the 2440 cm-1 peak, likely associated with vibrational modes of additives or impurities, showed a downward shift with increasing dose, suggesting chain scission and degradation of non-rubber constituents. These results provide first-of-a-kind insights into the microstructural evolution of elastomers under high-dose gamma irradiation and establish a preliminary dose threshold for significant degradation. Future work will incorporate multi-modal characterization—including Fourier Transform Infrared (FTIR) spectroscopy, scanning electrom microscopy (SEM) of the rubber surface morphology, thermogravimetric analysis (TGA) to determine changes in thermal stability, and mechanical testing—to correlate molecular-level changes with macroscopic performance of these elastomers as damping media in seismic isolation devices. These findings are expected to provide regulatory guidance and design criteria for qualifying low-damping rubber seismic isolators in advanced nuclear reactor applications.

36 - MATERIALS SCIENCE↗
DOE OSTI2024

Elucidating the corrosion mechanism of commercial Ni-based superalloys in UCl3 containing-chloride molten salt systems

Elucidating the role of UCl3 in the corrosion mechanism of Ni-based superalloys exposed to chloride molten salts Trishelle Copeland-Johnson1, Michael Woods1, Ruchi Gakhar1, Daniel J. Murray1, Guoping Cao1, Lingfeng He1 1Idaho National Laboratory, Idaho Falls, ID, United States The United States Department of Energy (DOE) aims to diversify the domestic energy portfolio towards more sustainable options, including implementation of molten salt reactor (MSR) technology. Chloride molten salts have been investigated as an appropriate MSR coolant and fuel because their relatively inexpensive, abundant, and exhibit favorable thermophysical properties. However, the corrosivity of chloride molten salts have not been extensively studied, especially with the inclusion of actinide products, such as UCl3. Accordingly, the development of nuclear structural materials with excellent corrosion performance is critical to the successful implementation of MSRs, particularly from a mechanistic perspective. In this investigation, we attempt to elucidate the interfacial corrosion mechanism between Ni-based structural materials, such as Inconel 617, and UCl3-containing salt systems through a multi-modal advanced characterization approach, including electron microscopy techniques. The findings from this investigation will expand the knowledgebase of chloride molten salt corrosion of MSR structural materials for strategic property-to-performance design.

36 - MATERIALS SCIENCE↗
DOE OSTI2026

LAF-Net: A Deep Residual and Cross-Attention Framework for Day-Ahead Load Forecasting: Preprint

Accurate day-ahead load forecasting is essential for reliable power system operations and market efficiency. System operators such as the Midcontinent Independent System Operator (MISO) rely on forecasts from multiple vendors, yet combining them effectively remains a persistent challenge due to vendor-specific biases. This paper presents a novel LSTM-Attention Fusion Network with Error Representation (LAF-Net) that enhances day-ahead hourly load forecasting through deep residual learning and multi-modal cross-attention. The proposed model builds a historical error memory from past vendor performance and dynamically queries it with future hour context to generate adaptive, hour-specific trust weights for each vendor. A bounded residual correction further refines forecasts by mitigating systematic and temporally localized errors. Tested on real MISO LBA data with multi-vendor forecasts, LAF-Net consistently outperforms the best vendor baseline across all 38 LBAs, achieving more than a 40% reduction in system-level mean absolute error (MAE) during peak load hours relative to the best vendor baseline.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Entropy-defect synergy for dual luminescence mechanism in spinel: Time-resolved anti-counterfeiting and fingerprint visualization

Multimodal luminescent materials, while promising for anti-counterfeiting, often lack dynamic time-dependent responses and controllable spatial distribution, limiting their encryption capabilities in the spatiotemporal dimension. Here, this work presents a coordinated control strategy based on entropy and defect engineering, and uses a backpropagation (BP) neural network for material screening to successfully prepare spinel Mg 0.8 (Fe 0.04 Co 0.04 Ni 0.04 Cu 0.04 Zn 0.04 )Cr 2 O 4 (MgA 5 CO) phosphors with time-dependent dynamic luminescence behavior. This phosphor simultaneously activated the d-d transition luminescence (∼618 nm) derived from Co 2+ /Cr 3+ and the defect luminescence (∼398 nm) related to zinc vacancies (V Zn ) in a single-phase solid solution. The phosphor exhibits a time-dependent color evolution from pink to purple under fixed-wavelength excitation, due to the different excited-state dynamics and decay lifetimes associated with the d-d transition and defect luminescence. Structural characterization and spectral analysis confirmed the existence of V Zn and its significant role in defect luminescence process. The fluorescent and dynamic luminescent properties of entropy-based spinel oxide enable its use in advanced anti-counterfeiting applications like fingerprint recognition and color-changing dedicated anti-counterfeiting mark, showing promise in high-end and time-dynamic anti-counterfeiting fields. This research not only developed a new type of fluorescent dynamic anti-counterfeiting material, but also provided a new idea for constructing advanced optical functional materials with multiple luminescence mechanisms.

Defect project↗
DOE OSTI2021

Fourier method for 3-dimensional data fusion of X-ray Computed Tomography and ultrasound

X-ray Computed Tomography (CT) is essential for nondestructive inspection of many manufactured components but is susceptible to various forms of artifacts and noise. In particular, multi-detector row cone-beam CT systems can suffer from so called “cone-beam” artifacts and partial volume effects, particularly on planar edges at the periphery of the field of view in the cone angle dimension. Uni-directional ultrasonic testing methods generally have extremely accurate in-plane depth resolution but poorer lateral resolution due to physical and geometric constraints. This paper presents a novel technique called Computed Tomographic Fusion (CT-F) which uses three-dimensional Fourier filtering to combine x-ray cone-beam CT reconstructions and ultrasound data in the frequency space. The result is a single image with improved accuracy and quality. CT-F mitigates artifacts while allowing for rapid, accurate characterization of large three-dimensional CT volumes. Finally, using simulations and experiments, we demonstrate artifact reduction and edge contrast improvement in volumetric reconstructions.

42 ENGINEERING↗
DOE OSTI2026

Ripening of Rh Nanoparticle Catalysts in Reverse Water–Gas Shift via a Data-Driven Model Combining Physics, Theory, and Experiment

Degradation via sintering is an ongoing challenge that impedes the broad commercial success of supported metallic nanoparticle catalysts. To mitigate degradation via informed catalyst design and process operations, here we aim to disambiguate the underlying mechanisms of sintering by combining theory and experiment in a quantitative framework. While mechanistic sintering models exist, they only model a single sintering pathway, even though multiple sintering mechanisms can occur simultaneously or dominate at different stages of the process. Data-driven machine learning models have emerged as a means to represent complex processes through data regression. However, machine learning models have very large data needs and lack mechanistic insights due to their black-box encoding. To develop an interpretive model of catalyst degradation via sintering, we constructed a hybrid model combining mechanistic “physics-based” models and data-driven methods to obtain both reliable predictions and mechanistic insights regarding experimentally observed sintering phenomena. Focusing on nanoparticle sintering in the Rh–TiO 2 catalyst for the reverse water–gas shift (RWGS) reaction, the hybrid model couples a mechanistic term for Ostwald ripening with energy values calculated via density functional theory (DFT) with a parametric, data-driven discrepancy function term for unmodeled mechanisms. The hybrid model is trained using Bayesian inference with data collected from small-angle X-ray scattering (SAXS) in situ experiments wherein average nanoparticle diameter versus time was measured at three relevant operating temperatures. The calibrated hybrid model results show that an Ostwald ripening-only model parameterized with fixed DFT energies does not fully capture the time and temperature dependence of the SAXS-observed sintering kinetics, and that an additional functional contribution, or DFT energy calibration, is required to reconcile simulation and experiment. Analysis of the hybrid-model error confirms that the hybrid model outperforms both the purely mechanistic and purely data-driven alternatives in terms of expected predictive accuracy for time-evolving average particle sizes. Furthermore, the results support the hypothesis that the Ostwald ripening mechanism is less important for explaining the sintering phenomena as operating temperature increases under an assumed fixed DFT parameterization. This could be explained in one of two ways: either latent, unmodeled sintering mechanisms dominate at higher temperatures, or the DFT uncertainty increases with temperature. The proposed modeling approach directly links theory to experiments and simulations via a statistical hybrid modeling framework and can be extended to other catalytic systems to improve predictive models and mechanistic understanding.

Bayesian hybrid modeling↗
DOE OSTI2022

Evaluating Contributions of Pitch-Carbon Coating to Improved Stability of Si Anodes Through Voltage-Resolved Multi-Phase Characterization

Silicon nanoparticles have emerged as a promising alternative to graphite to improve the energy density of next-generation lithium-ion battery anodes. Nano-sized Si domains facilitate rapid ion transport and minimize particle-scale mechanical degradation, but also exhibit increased (electro)chemical reactivity with Li-ion electrolyte components due to their high surface area. We have previously demonstrated that surface modification of Si nanoparticles with pitch-carbon is an effective strategy to reduce these parasitic reactions. In the present work, we holistically evaluate the mechanistic contribution of pitch-carbon coating to the observed stability improvement over uncoated Si. We utilize coupled in situ and ex situ methods to probe changes to solid-surface, volatile headspace, and gas-phase chemistry occurring during initial cycling. Measurements taken at targeted potentials associated with electrolyte species reduction enables the decoupling of specific reaction pathways tied to interfacial stability. Further, we demonstrate the non-trivial role of gas reconsumption in dictating the nature of the passivating surface layer evolved on both uncoated and pitch-coated Si. This multi-phase analysis offers insights into the mechanism of effective surface passivation, which may be applied to inform future Si material development.

ENERGY STORAGE↗
DOE OSTI2023

Extrapolation of the Rainflow-Counted Load Ranges for Fatigue Assessment of the Wind Turbine's Blades

Wind turbine design standards recommend the use of statistical modeling coupled with extrapolation of the short-term load data to long-term periods for fatigue reliability assessment. However, statistical error and computational expense can limit the accuracy of such approaches. In the case of wind turbine blades, the errors are more significant because of the high material fatigue exponent that makes the damage estimations more sensitive to variations. In addition, due to different excitation sources, the flapwise load range histogram is not unimodal, and thus its statistical modeling is complex. In the present work, we provide three methods for statistical modeling of the flapwise bending moment ranges including a novel approach based on frequency-based separation of the modes. The first two methods are simplified approaches for modeling the most crucial load ranges using unimodal distributions and the third method involves multimodal distribution fitting. The research is based on 3600 10-minute aeroelastic simulations of DTU 10MW case study wind turbine from which a benchmark damage equivalent load (DEL) is calculated. The DEL calculated by each of the three proposed methods is compared to this reference. The results show that the conventional approach based on using 6 seeds as well as using mixture models fitted on the limited data lead to under-conservative results with errors up to 23%. On the other hand, the simplified unimodal approaches provided in this work can provide conservative estimations of the fatigue damage with mean values 5% and 12% higher than the benchmark. However, the variability of the DEL estimates is higher when using unimodal extrapolation of the load ranges, and the data can be conservative by 17.5%. The proposed unimodal fits suggested for modeling and extrapolation of the blade's load ranges provide less errors relatively and most importantly conservative DEL estimations while maintaining computational efficiency.

blade fatigue↗