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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Hydrochlorination of Uranium Dioxide in a Molten Salt Mixture - Phase 1: Tube Furnace Cross Flow Experiments

In 2023, Metatomic® Inc., a South Carolina based company, was awarded a Gateway for Advanced Innovation in Nuclear (GAIN) research voucher for a proposed series of experiments aimed at demonstrating the viability of a spent nuclear fuel (SNF) recycling process patented by Met atomic® Inc. For the GAIN voucher, Metatomic® Inc. selected Savannah River National Laboratory (SRNL) as a partner in executing the proposed proof-of-concept experiments. This report outlines the proof-of-concept experiments performed by SRNL for Metatomic® Inc. during Phase 1 (of 2) experimentation. The Phase 1 hydrochlorination experiments consisted of weighing UO2 into alumina crucibles with a eutectic mixture of NaCl and CsCl, heating the uranium/salt mixture to a varied temperature (550, 650, or 750 °C), and flowing anhydrous hydrogen chloride (AHCl) gas across the surface of the uranium-bearing molten salt mixture. The hydrochlorination process conditions were maintained for 4-5 hours and the percent conversion for each batch of UO2 was determined using a suite of analytical characterization techniques. The degree of UO2 conversion was found to be greatest at the highest tested temperature, ultimately achieving 32.9% conversion of UO2 to water soluble uranium-chloro species (e.g., UO2Cl2, UCl4) after exposure to AHCl for 4.25 hours at 750 °C.

Nguyen, Vinh T. [Savannah River National Laborator↗
DOE OSTI2022

Superresolved Motions of Single Molecular Catalysts during Polymerization Show Wide Distributions

The motion of single molecular ruthenium catalysts during and after single turnover events of ring-opening metathesis polymerization is imaged through single-molecule superresolution tracking with a positional accuracy of ±32 nm. This tracking is achieved through the real-time incorporation of spectrally tagged monomer units into active polymer chain ends during living polymerization; thus, by design, only active-catalyst motion is detected and imaged, without convolution by inactive catalysts. The catalysts show diverse individualistic diffusive behaviors with respect to time that persist for up to 20 s. Catalysts occupy three mobility populations: quasi-stationary (23%), intermediate (53%, 65 nm), and large (24%, 145 nm) step sizes. Differences in catalyst mobility populations also exist between individual aggregates (p < 0.001). Such differential motion indicates widely different local catalyst microenvironments during the catalytic turnover. These mobility differences are uniquely observable through single-catalyst microscopy and are not measurable through traditional ensemble analytical techniques for characterizing the behavior of molecular catalysts, such as nuclear magnetic resonance spectroscopy. The measured distributions of active molecular catalyst motions would not be readily predictable through modeling or first-principles, and the range likely impacts individual catalyst turnover rate and selectivity. This range plausibly contributes to property distributions observable in bulk polymers, such as molecular weight polydispersity (e.g., 1.9 in this system), leading to a revised understanding of the mechanistic, microscale origins of macroscale polymer properties.

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

X-ray diffraction under grazing incidence conditions

Material properties frequently relate to structures at or near surfaces, particularly in thin films. As a result, it is essential to understand these structures at the molecular and atomistic levels. The most accurate and widely used techniques for characterizing crystallographic order are based on X-ray diffraction. When dealing with thin films or interfaces, standard approaches, such as single crystal or powder diffraction, are not suitable. However, X-ray diffraction under grazing incidence conditions can provide the required information. Here, in this Primer, grazing incidence X-ray diffraction (GIXD) is comprehensively introduced, starting from basic considerations on X-ray diffraction at crystals with reduced dimensionality and the optical properties of X-rays, followed by a more in-depth description of an experimental performance, including X-ray sources, goniometers and detectors. Experimental errors, limitations and reproducibility are discussed. Various applications, from highly ordered inorganic single crystal surfaces to weakly ordered polymer thin films, are presented to illustrate the potential of GIXD. Data visualizations, representations and evaluation strategies are summarized, based on the example of anthracene thin films. The Primer compiles information relevant to perform high-quality GIXD experiments, evaluate data and interpret results, to extend knowledge about X-ray diffraction from surfaces, interfaces and thin films.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Turn-key constrained parameter space exploration for particle accelerators using Bayesian active learning

Abstract Particle accelerators are invaluable discovery engines in the chemical, biological and physical sciences. Characterization of the accelerated beam response to accelerator input parameters is often the first step when conducting accelerator-based experiments. Currently used techniques for characterization, such as grid-like parameter sampling scans, become impractical when extended to higher dimensional input spaces, when complicated measurement constraints are present, or prior information known about the beam response is scarce. Here in this work, we describe an adaptation of the popular Bayesian optimization algorithm, which enables a turn-key exploration of input parameter spaces. Our algorithm replaces the need for parameter scans while minimizing prior information needed about the measurement’s behavior and associated measurement constraints. We experimentally demonstrate that our algorithm autonomously conducts an adaptive, multi-parameter exploration of input parameter space, potentially orders of magnitude faster than conventional grid-like parameter scans, while making highly constrained, single-shot beam phase-space measurements and accounts for costs associated with changing input parameters. In addition to applications in accelerator-based scientific experiments, this algorithm addresses challenges shared by many scientific disciplines, and is thus applicable to autonomously conducting experiments over a broad range of research topics.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Experimental characterization of a triply periodic minimal surface PCM-to-air thermal energy storage device

Here, the expansion of energy production has intensified research into thermal energy storage (TES) to manage variability and improve system efficiency. Heat exchanger design is critical to achieving high power densities in TES systems. This study presents a high-surface-area phase change material (PCM)-to-air heat exchanger fabricated via resin-based stereolithography with a novel gyroid-based geometry tailored for enhanced performance. Material properties of the commercial PCM and resin were characterized using analytical techniques. A controlled air loop was used to evaluate heat transfer and pressure drop at various flow rates and inlet temperatures. Results show a strong dependence on the inlet temperature difference (ΔT) relative to the PCM melting point. During charging at the highest flow rate, increasing the inlet air temperature from ΔT = 5 °C to 20 °C above the melting point increased the average heat transfer rate by 187%. During discharging, decreasing the inlet temperature by the same ΔT below the melting point led to a 232% increase. Notably, the high-surface-area design enabled nearly symmetric charging and discharging behavior, a novel result for PCM-based TES systems which are often restricted by natural convection and other effects. The overall heat transfer coefficient was calculated and compared to values from standard design correlations. The maximum thermal effectiveness reached 95% at ΔT = 20 °C and a moderate flow rate of 34 m 3 /h. Peak coefficients of performance (COP) of 7 during charging and 6.4 during discharging were observed at ΔT = 20 °C and a low flow rate of 20 m 3 /h. These results demonstrate the viability of additively manufactured geometries for advanced TES applications.

25 ENERGY STORAGE↗
DOE OSTI2023

Biobased Semi-Interpenetrating Polymer Networks of Poly(ε-caprolactone) and Epoxidized Soybean Oil with Nanoscale Morphology, Shape-Memory Effect, and Biocompatibility

Creating biobased polymer blends with outstanding properties, nanoscale morphology, shape-memory capability, and biocompatibility is very crucial and requires a fundamental understanding of the phase behavior, macromolecular structure, and biological compatibility of the polymer blends with living cells. It is very critical to understand the complex relationships among the polymer structure, morphology, and performance of multifunctional smart materials under conditions that they are likely to encounter during use, particularly in biomedical applications. Biobased semi-interpenetrating polymer networks of poly(ε-caprolactone) and epoxidized soybean oil with nanoscale morphology have been successfully synthesized via in situ cationic polymerization and compatibilization in a homogeneous solution. Varies analytical and characterization techniques, such as Fourier transform infrared spectroscopy, differential scanning calorimetry, dynamic mechanical analysis, transmission electron microscopy, X-ray scattering, cell toxicity, and shape-memory effects (SMEs), have been employed to understand the structure–properties relationship of these smart, biobased nanostructured polymer blends. The synthesized nano blends were nontoxic or biocompatible and supported attachment of human vein endothelial cells, showing their potential use in biomedical applications. The current versatile, low-cost strategy for synthesizing the nanoscale morphology of semi-interpenetrating polymer networks with SMEs and biocompatibility should be widely applicable for polymer systems. This study is also considered as a continuation to our efforts in the area of biobased polymers to develop innovative technologies to transform natural resources into smart multifunctional materials for a wide range of applications, including coatings, adhesives, and medical devices.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Oil and Gas Produced Water Reuse: Opportunities, Treatment Needs, and Challenges

Advances in water treatment technologies paired with potential restrictions on oil and gas (O&G) produced water disposal could incentivize the beneficial reuse of treated produced water in the O&G industry. However, the remote nature of O&G operations limits the applicability of many of these solutions, which may be spatially inefficient, require operator supervision, or are ill-suited for the complex nature of produced water. Furthermore, the responsible, sustainable reuse of produced water as an alternative water source requires standardized analytical techniques for characterizing and determining the toxicity of treated produced water and improving our understanding of the fate and transport of various constituents. In the past decade, we made little progress in economically treating produced water for beneficial reuse outside of oilfield operations; the sole major breakthrough has been in the development of salt-tolerant fracturing chemicals that allow for reuse of produced water for fracking operations. Guided research should assist in the development of fit-for-purpose solutions to maximize the reuse of treated produced water. This is exemplified by the case studies presented here that detail currently operating treatment facilities for reclamation and reuse of produced water.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Forecasting of in situ electron energy loss spectroscopy

Abstract Forecasting models are a central part of many control systems, where high-consequence decisions must be made on long latency control variables. These models are particularly relevant for emerging artificial intelligence (AI)-guided instrumentation, in which prescriptive knowledge is needed to guide autonomous decision-making. Here we describe the implementation of a long short-term memory model (LSTM) for forecasting in situ electron energy loss spectroscopy (EELS) data, one of the richest analytical probes of materials and chemical systems. We describe key considerations for data collection, preprocessing, training, validation, and benchmarking, showing how this approach can yield powerful predictive insight into order-disorder phase transitions. Finally, we comment on how such a model may integrate with emerging AI-guided instrumentation for powerful high-speed experimentation.

36 MATERIALS SCIENCE↗
DOE OSTI2021

RapidEELS: machine learning for denoising and classification in rapid acquisition electron energy loss spectroscopy

Recent advances in detectors for imaging and spectroscopy have afforded in situ, rapid acquisition of hyperspectral data. While electron energy loss spectroscopy (EELS) data acquisition speeds with electron counting are regularly reaching 400 frames per second with near-zero read noise, signal to noise ratio (SNR) remains a challenge owing to fundamental counting statistics. In order to advance understanding of transient materials phenomena during rapid acquisition EELS, trustworthy analysis of noisy spectra must be demonstrated. In this study, we applied machine learning techniques to denoise high frame rate spectra, benchmarking with slower frame rate “ground truths”. The results provide a foundation for reliable use of low SNR data acquired in rapid, in-situ spectroscopy experiments. Such a tool-set is a first step toward both automation in microscopy as well as use of these methods to interrogate otherwise poorly understood transformations.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Cubic-meter scale laboratory fault re-activation experiments to improve the understanding of induced seismicity risks

Abstract To understand fluid induced seismicity, we have designed a large-scale laboratory experiment consisting of a one-cubic-meter sandstone with an artificial fault cut and fluid-injection boreholes. The sandstone block is assembled in a true triaxial loading frame and equipped with 38 piezoelectric sensors to locate and characterise acoustic emission events. The differential stress on the artificial fault is increased in stages to bring it towards a critically stressed state. After each stage of differential stress increase, fluids are injected at low pressures through boreholes to test the potential of fault re-activation. In addition, a high-pressure injection was conducted that created a hydraulic fracture from the injection borehole towards the artificial fault. The newly generated fluid pathway resulted in an activation of the complete block through a stick–slip movement. We compare acoustic emission measurements from the laboratory experiment with seismicity observations from the field-scale CO 2 injection at Decatur, Illinois, U.S., and conclude that the existence of fluid pathways plays a decisive role for the potential of induced seismicity.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Quantifying the electrochemical active site density of precious metal-free catalysts in situ in fuel cells

We report advances in the development of precious-group metal-free (PGM-free) catalysts for the oxygen reduction reaction (ORR) in fuel cell cathodes have produced active catalysts that reduce the performance gap to the incumbent Pt-based materials. However, utilization of state-of-the-art PGM-free catalysts for commercial applications is currently impeded by their relatively low durability. Methods designed to study catalyst degradation in the operation of fuel cells are therefore critical for understanding durability issues and, ultimately, their solutions. Here we report the use of Fourier-transform alternating current voltammetry as an electrochemical method for accurate quantification of the electrochemically active site density of PGM-free cathode catalysts, and to follow their degradation in situ during the operation of polymer electrolyte fuel cells. Using this method, we were able to quantify the electrochemical active site density, which will enable the elucidation of degradation mechanisms of PGM-free ORR catalysts in situ in fuel cells.

25 ENERGY STORAGE↗
DOE OSTI2022

Multiple magnetic orders in LaFeAs 1-x P x O uncover universality of iron-pnictide superconductors

The iron-pnictide superconductors have generated tremendous excitement as the competition between magnetism and superconductivity has allowed unique in-roads towards elucidating a microscopic theory of unconventional high-temperature superconductivity. In addition to the stripe spin density wave ($C^a_{2M}$) phase observed in the parent compounds of all iron-pnictide superconductors, two novel magnetic orders have recently been discovered in different parent structures: an out-of-plane collinear double-Q ($C^c_{4M}$) structure in the hole-doped (Ca, Sr, Ba) 1-x (Na) x Fe 2 As 2 and Ba 1-x K x Fe 2 As 2 families, and a spin vortex crystal “hedgehog” ($C^{ab}_{4M}$) structure in the CaKFe 4 As 4 family. Using neutron diffraction, we demonstrate that LaFeAs 1-x P x O contains all three magnetic orders within a single-phase diagram as a function of substitution, all of which compete strongly with superconductivity. Our experimental observations combined with theoretical modeling demonstrate how the reduction in electronic correlations by chemical substitution results in larger Fermi surfaces and the sequential stabilization of multiple magnetic anisotropies. Our work presents a unified narrative for the competing magnetic and superconducting phases observed in various iron-pnictide systems with different crystal structures and chemistry.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Three-dimensional imaging of grain boundaries via quantitative fluorescence X-ray tomography analysis

Abstract Three-dimensional visualization of material composition within multiple grains and across complex networks of grain boundaries at nanoscales can provide new insight into the structure evolution and emerging functional properties of the material for diverse applications. Here, using nanoscale scanning X-ray fluorescence tomography, coupled with an advanced self-absorption correction algorithm developed in this work, we analyze the three-dimensional gain distributions and compositions in a Ce 0.8 Gd 0.2 O 2-δ -CoFe 2 O 4 mixed ionic-electronic conductor system with high accuracy and statistical significance. Our systematic investigation reveals an additional emergent phase and uncovers highly intriguing composition stability ranges for the multiple material phases within this system. The presented visualization of composition variations across complex interfaces, supported by our quantitative composition analysis, discloses mechanistic pathways of the diverse phase transformations occurring in the material synthesis, providing insights for the optimization of transport properties in the mixed ionic-electronic conductor system.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Determination of the Raman polarizability tensor in the optically anisotropic crystal potassium dihydrogen phosphate and its deuterated analog

Abstract The Raman tensor of the dominant A 1 modes of the nonlinear optical crystalline material potassium dihydrogen phosphate and its 70% deuterated analog have been ascertained. Challenges in determining the A 1 mode tensor element values based on previous reports have been resolved using a specially designed experimental setup that makes use of spherical crystal samples. This novel experimental design enabled the determination of measurement artifacts, including polarization rotation of the pump and/or scattered light propagating through the sample and the contribution of additional overlapping phonon modes, which have hindered previous efforts. Results confirmed that the polarization tensor is diagonal, and matrix elements were determined with high accuracy.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Challenges and Opportunities of Fe-based Core-Shell Catalysts for Fischer-Tropsch Synthesis

Here, Fe-based catalysts are an active, selective, and low-cost option for tuning Fischer-Tropsch synthesis (FTS) selectivity toward desirable light olefins. By encapsulating Fe within ZSM-5, the resultant core-shell catalysts have the potential to control the product distribution via secondary reactions that occur over the acid sites of the zeolite shell. In this paper, Fe is encapsulated within ZSM-5 via the seed-directed growth technique and characterized with a suite of analytical techniques including Mössbauer spectroscopy and X-ray absorption fine structure (XAFS). Characterization of the core-shell catalysts indicates that some of the Fe-based active phase is destabilized during seed-directed growth, demonstrating the challenges associated with encapsulating an Fe-based active phase within zeolites. However, comparing FTS performance of the core-shell catalyst with the Fe-based control synthesized via incipient wetness impregnation demonstrates improved selectivity toward the desired C 2 -C 4 olefins and C 5+ hydrocarbons.

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

Standardization Gaps in Powder Feedstock Characterization and Establishing Acceptability for Reuse in Additive Manufacturing

Characterization techniques for powder feedstocks used in additive manufacturing (AM) have long been relied upon to describe the inputs to an AM workflow. However, functional gaps remain between tests to measure intrinsic and extrinsic properties with the direct performance within AM equipment. Furthermore, the common practice of reusing powder through multiple build cycles introduces effects and changes to feedstock performance that are otherwise difficult to measure quantitatively. Here, standardization and the development of new test methods have not kept pace with the rapid evolution of the AM industry and its reliance on highly coupled process-structure–property-performance relationships.

Characterization and Analytical Technique↗
DOE OSTI2023

Conotoxin Prediction: New Features to Increase Prediction Accuracy

Conotoxins are toxic, disulfide-bond-rich peptides from cone snail venom that target a wide range of receptors and ion channels with multiple pathophysiological effects. Conotoxins have extraordinary potential for medical therapeutics that include cancer, microbial infections, epilepsy, autoimmune diseases, neurological conditions, and cardiovascular disorders. Despite the potential for these compounds in novel therapeutic treatment development, the process of identifying and characterizing the toxicities of conotoxins is difficult, costly, and time-consuming. This challenge requires a series of diverse, complex, and labor-intensive biological, toxicological, and analytical techniques for effective characterization. While recent attempts, using machine learning based solely on primary amino acid sequences to predict biological toxins (e.g., conotoxins and animal venoms), have improved toxin identification, these methods are limited due to peptide conformational flexibility and the high frequency of cysteines present in toxin sequences. This results in an enumerable set of disulfide-bridged foldamers with different conformations of the same primary amino acid sequence that affect function and toxicity levels. Consequently, a given peptide may be toxic when its cysteine residues form a particular disulfide-bond pattern, while alternative bonding patterns (isoforms) or its reduced form (free cysteines with no disulfide bridges) may have little or no toxicological effects. Similarly, the same disulfide-bond pattern may be possible for other peptide sequences and result in different conformations that all exhibit varying toxicities to the same receptor or to different receptors. We present here new features, when combined with primary sequence features to train machine learning algorithms to predict conotoxins, that significantly increase prediction accuracy.

collisional cross section↗
DOE OSTI2022

An insight into microscopy and analytical techniques for morphological, structural, chemical, and thermal characterization of cellulose

Cellulose obtained from plants is a bio-polysaccharide and the most abundant organic polymer on earth that has immense household and industrial applications. Hence, the characterization of cellulose is important for determining its appropriate applications. In this article, we review the characterization of cellulose morphology, surface topography using microscopic techniques including optical microscopy, transmission electron microscopy, scanning electron microscopy, and atomic force microscopy. Additionally, other physicochemical characteristics like crystallinity, chemical composition, and thermal properties are studied using techniques including X-ray diffraction, Fourier transform infrared, Raman spectroscopy, nuclear magnetic resonance, differential scanning calorimetry, and thermogravimetric analysis. This review may contribute to the development of using cellulose as a low-cost raw material with anticipated physicochemical properties.

59 BASIC BIOLOGICAL SCIENCES↗