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At least 1,171 records · Page 65

All sources NASA NTRS DOE OSTI

DOE OSTI2023

Customization for Current Market Applications of the Carbon Nanotubes Produced by PNNL’s Regenerable Catalytic Pyrolysis (ReCaP) for Scaled Hydrogen Production

Here, we demonstrated the Regenerative Catalytic Pyrolysis (ReCaP) process that generates carbon dioxide-free hydrogen (CO 2 -free H 2 ) from inexpensive and domestically abundant natural gas (NG), while simultaneously producing H 2 at net production cost of $1.0/kg through the sale of valuable carbon nanotube (CNT) co-product. The specific goal of this project is to 1) scale up the production of CNT, 2) correlate the properties of the CNT with the reaction conditions, 3) perform a techno-economic analysis to obtain minimum selling price of H 2 and CNT, and 4) identify industrial partners interested in the CNT co-product.

03 NATURAL GAS↗

DOE OSTI2026

A Flexible Quasi-Static Mooring Design Optimization Method for Floating Structures

This paper presents a flexible and efficient design method for optimizing the mooring systems of floating structures. Mooring system optimization is challenging because of the strong nonlinearity of mooring system behavior and the many technical constraints that must be satisfied. Furthermore, different mooring configurations can have very different design spaces. While some successful examples of mooring design optimization exist in the literature, developing an optimization approach that can work across various mooring design problems is a larger challenge. We present such a method based on a flexible parameterization that allows a wide variety of mooring designs to be described by a list of variables, a quasi-static mooring model that provides efficient evaluation of a mooring design without directly considering mooring system dynamics, and an optimization framework that generates, evaluates, and adjusts the mooring design while considering user-specified constraints such as offset limits, strength safety factors, and seabed contact limits. We demonstrate the design optimization framework on four mooring design problems, each for a different type of mooring system. We compare the use of different design modes to simplify the optimization problem, showing that they can reduce the computation time by up to 75%. We also compare different optimization algorithms and find that the resulting computational speed can vary by up to 51 times. We perform a sensitivity study on one design and find that the local sensitivity of anchoring radius to water depth has a positive correlation of 0.29, but the global sensitivity shows large nonlinearities. Lastly, we perform a coupled dynamic analysis on one of the optimized designs and find that the predicted mean platform motions and mooring line tensions are within 1% of dynamic results and the extreme motions and tensions are within 14%. Lastly, we show that a DEA-Chain-Polyester mooring configuration is cost-optimal for the given design problem of the demonstrations, which aligns with general industry practice.

16 TIDAL AND WAVE POWER↗

DOE OSTI2022

Unsupervised Machine Learning for Exploratory Data Analysis of Exoplanet Transmission Spectra

Abstract Transit spectroscopy is a powerful tool for decoding the chemical compositions of the atmospheres of extrasolar planets. In this paper, we focus on unsupervised techniques for analyzing spectral data from transiting exoplanets. After cleaning and validating the data, we demonstrate methods for: (i) initial exploratory data analysis, based on summary statistics (estimates of location and variability); (ii) exploring and quantifying the existing correlations in the data; (iii) preprocessing and linearly transforming the data to its principal components; (iv) dimensionality reduction and manifold learning; (v) clustering and anomaly detection; and (vi) visualization and interpretation of the data. To illustrate the proposed unsupervised methodology, we use a well-known public benchmark data set of synthetic transit spectra. We show that there is a high degree of correlation in the spectral data, which calls for appropriate low-dimensional representations. We explore a number of different techniques for such dimensionality reduction and identify several suitable options in terms of summary statistics, principal components, etc. We uncover interesting structures in the principal component basis, namely well-defined branches corresponding to different chemical regimes of the underlying atmospheres. We demonstrate that those branches can be successfully recovered with a K-means clustering algorithm in a fully unsupervised fashion. We advocate for lower-dimensional representations of the spectroscopic data in terms of the main principal components, in order to reveal the existing structure in the data and quickly characterize the chemical class of a planet.

Matchev, Konstantin T. (ORCID:0000000341829096)↗

DOE OSTI2020

Probing U 5f Covalency in Uranium Compounds through Oxidant 2p Bonding

Oxidant K (1s) X-ray Emission Spectroscopy (XES) was used to investigate covalency in the Oxidant 2p – Uranium 5f bonds of Uranium Dioxide (UO 2 ) and Uranium Tetrafluoride (UF 4 ). It will be shown that the width of the 2p Occupied Density of States (ODOS), determined from the XES measurements, correlates with the increased 5f covalency of UO 2 and the increased ionicity of UF 4 . Analysis of the XES results includes comparison to spectral simulations, cluster calculations and peak fitting, demonstrating the potential of the oxidant XES measurements as a probe of 5f covalency.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2022

LBNL CRADA (FP00009949) with the American Public Power Association: Electricity Reliability Metrics, Analysis, and Planning (Final Technical Report)

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Electricity Reliability Metrics, Analysis, and Planning (CRADA Final Report)

LBNL and APPA (the team) jointly examined the extent to which differences in distribution feeder characteristics are correlated with differences in their reliability performance when exposed to three different types of natural hazards (wildlife, weather, and vegetation). The team employed data-driven approaches to quantify the relationships between various measures of feeder reliability and a suite of feeder characteristics individually and jointly via a statistically-based clustering method. The team developed suggestions on how comparisons across groupings of feeders and review of the relative contributions of the constituents of SAIFI and SAIDI could be used to help prioritize utility actions to improve reliability. However, they also caution that their suggestions require further evaluation because they are based on only one year of information from a modest number of small utilities.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Quantifying experimental edge plasma evolution via multidimensional adaptive Gaussian process regression

The edge density and temperature of tokamak plasmas are strongly correlated with energy and particle confinement and their quantification is fundamental to understanding edge dynamics. These quantities exhibit behaviours ranging from sharp plasma gradients and fast transient phenomena (e.g. transitions between low and high confinement regimes) to nominal stationary phases. Analysis of experimental edge measurements therefore require robust fitting techniques to capture potentially stiff spatiotemporal evolution. Additionally, fusion plasma diagnostics inevitably involve measurement errors and data analysis requires a statistical framework to accurately quantify uncertainties. This paper outlines a generalized multidimensional adaptive Gaussian process routine capable of automatically handling noisy data and spatiotemporal correlations. We focus on the edge-pedestal region in order to underline advancements in quantifying time-dependent plasma profiles including transport barrier formation on the Alcator C-Mod tokamak.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

Impact of electrode porosity architecture on electrochemical performances of 1 mm-thick LiFePO4 binder-free Li-ion electrodes fabricated by Spark Plasma Sintering

Thick electrodes with high active material loadings have been intensively studied over the last couple of decades in pursuit of achieving high energy density systems. To optimize and enhance the electrochemical performance of such electrodes, one has to control the pore morphology by, for example, varying the pore size and shape, and the level of porosity. In the present work, the fabrication of thick binder-free LiFePO4 (LFP) electrodes with two different pore sizes (12 and 20 mu m) and porosities (21 vol% and 44 vol%) using Spark Plasma Sintering (SPS) and templating approach is reported. The well-controlled porous architecture inside the thick electrodes is realized by fine-tuning experimental parameters. The impact of porosity architecture on electrochemical performance is quantified and correlated with the 3D tortuosity values determined from both micro-computed tomography and electrochemical impedance-based experimental methods. Based on the micro-computed tomography data analysis, estimated tortuosity values along X, Y, and Z axes reveal an anisotropic effect perpendicularly to the SPS compression axis (Z-direction). This is particularly profoundly observed in the samples with larger pores (20 mu m). The correlation between morphological properties and the rate capability performance is established indicating that the capacity loss happens mainly due to the Li-ion diffusion limitations.

Cathode material↗

DOE OSTI2026

Data for FUN-PROSE: A Deep Learning Approach to Predict Condition-Specific Gene Expression in Fungi

mRNA levels of all genes in a genome is a critical piece of information defining the overall state of the cell in a given environmental condition. Being able to reconstruct such condition-specific expression in fungal genomes is particularly important to metabolically engineer these organisms to produce desired chemicals in industrially scalable conditions. Most previous deep learning approaches focused on predicting the average expression levels of a gene based on its promoter sequence, ignoring its variation across different conditions. Here we present FUN-PROSE—a deep learning model trained to predict differential expression of individual genes across various conditions using their promoter sequences and expression levels of all transcription factors. We train and test our model on three fungal species and get the correlation between predicted and observed condition-specific gene expression as high as 0.85. We then interpret our model to extract promoter sequence motifs responsible for variable expression of individual genes. We also carried out input feature importance analysis to connect individual transcription factors to their gene targets. A sizeable fraction of both sequence motifs and TF-gene interactions learned by our model agree with previously known biological information, while the rest corresponds to either novel biological facts or indirect correlations.

Genomics↗

DOE OSTI2020

NuMI Beam Muon Monitor Data Analysis and Simulation for Improved Beam Monitoring

The NuMI muon monitors (MMs) are a very important diagnostic tool for monitoring the stability of the neutrino beam used by the NOvA experiment at Fermilab. The goal of our study is to maintain the quality of the MM signal and to establish the correlations between the neutrino and muon beam profile. This study could also inform the LBNF decision on the beam diagnostic tools. We report on the progress of beam scan data analysis (beam position, spot size, and magnetic horn current scan) and comparison with the simulation outcomes.

43 PARTICLE ACCELERATORS↗

DOE OSTI2025

In situ investigation of high-pressure hydrogen-induced swelling in elastomers and its correlation with material properties

The resistance of elastomeric materials to high-pressure hydrogen-induced damage is essential for ensuring the reliability of hydrogen infrastructure. Here, in this study, we systematically investigated the swelling behavior and hydrogen transport properties of four elastomer types – EPDM, NBR, FKM, and HNBR – using a custom in-situ view cell system capable of real-time monitoring during decompression from pressures up to 96.5 MPa. Each elastomer was formulated with and without fillers and plasticizers to assess the effects of formulation on swelling response. Thermal desorption analysis (TDA) was employed to determine equilibrium hydrogen content and diffusion coefficients, providing insight into gas uptake and mobility within each material. Correlation analyses using Pearson and Spearman coefficients revealed that the diffusion coefficient showed a stronger relationship with swelling behavior than hydrogen content, highlighting the dominant role of hydrogen mobility. Filled elastomers, particularly those with carbon black, consistently showed reduced swelling due to enhanced stiffness and reduced diffusivity. These results deepen our understanding of diffuso-mechanical interactions in elastomers and support the rational design of sealing materials for high-pressure hydrogen systems.

EPDM↗

DOE OSTI2024

Measurement of flow coefficients in high-multiplicity 𝑝 + Au, 𝑑 + Au, and 3 He + Au collisions at $\sqrt{s_{NN}}$ = 200 GeV

Flow coefficients (𝑣 2 and 𝑣 3 ) are measured in high-multiplicity 𝑝 + Au, 𝑑 + Au, and 3 He + Au collisions at a center-of-mass energy of $\sqrt{s_{NN}}$ = 200 GeV using the STAR detector. The measurements utilize two-particle correlations with a pseudorapidity requirement of |𝜂| < 0.9 and a pair gap of |Δ⁢𝜂| > 1.0. The primary focus is on analysis methods, particularly the subtraction of nonflow contributions. Four established nonflow subtraction methods are applied to determine 𝑣 𝑛 , validated using the HIJING event generator. 𝑣 𝑛 values are compared across the three collision systems at similar multiplicities; this comparison cancels the final-state effects and isolates the impact of initial geometry. While 𝑣 2 values show differences among these collision systems, 𝑣 3 values are largely similar, consistent with expectations of subnucleon fluctuations in the initial geometry. The ordering of 𝑣 𝑛 differs quantitatively from previous measurements using two-particle correlations with a larger rapidity gap, which, according to model calculations, can be partially attributed to the effects of longitudinal flow decorrelations. As a result, the prospects for future measurements to improve our understanding of flow decorrelation and subnucleonic fluctuations are also discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Predicting Selective Laser Printing Print Quality of Polymer Powders through Melt Flow Index

Selective Laser Sintering (SLS) uses a precisely controlled laser to fuse polymer powder to build complex 3D shapes. While SLS covers a wide application space, the processing knowledge of polymer powder is limited, restricting the number of commercial powders available. This study serves to expand the processing knowledge of polypropylene and polyethylene in parallel with a “mature” SLS feedstock, nylon, through melt flow index (MFI) characterization. Differential Scanning Calorimetry (DSC) was used to explore the sintering window of the polymers. Polyethylene and polypropylene exhibited a relatively narrow sintering window between 4 – 5 °C, whereas the sintering window of nylon was much wider, between 23 – 24 °C. This suggests that the print quality between polyethylene and polypropylene would be similar; however, X-ray computed tomography revealed a higher volume of print defects, voids, and de lamination in polyethylene than in polypropylene. MFI analysis provided additional insight into the difference in print quality, as the MFI of polyethylene was 7.16 g/10 min, 9.95 g/10 min for polypropylene, and 17.23 g/10 min for nylon. MFI is inversely correlated to melt viscosity, and a low melt viscosity is desired for proper coalescing between the layers and particles. These results suggest that MFI is a promising tool, in conjunction with traditional thermal analysis, for screening candidate powder feedstocks for SLS and optimization of print parameters for novel powders.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Numerical analysis of a time discretized method for nonlinear filtering problem with Lévy process observations

Abstract In this paper, we consider a nonlinear filtering model with observations driven by correlated Wiener processes and point processes. We first derive a Zakai equation whose solution is an unnormalized probability density function of the filter solution. Then, we apply a splitting-up technique to decompose the Zakai equation into three stochastic differential equations, based on which we construct a splitting-up approximate solution and prove its half-order convergence. Furthermore, we apply a finite difference method to construct a time semi-discrete approximate solution to the splitting-up system and prove its half-order convergence to the exact solution of the Zakai equation. Finally, we present some numerical experiments to demonstrate the theoretical analysis.

Mathematics↗

DOE OSTI2022

DISTRIBUTION OF ION-EXCHANGEABLE RARE EARTH ELEMENTS AND CRITICAL MINERALS IN COAL-HOSTED CLAYS OF THE POWDER RIVER BASIN, WY

Increasing demand for a more robust domestic supply of rare earth elements (REE) and critical minerals (CM) has led to significant investigation into unconventional sources. Coal and coal byproducts from the Powder River Basin (PRB) of Wyoming are potential sources of REE but lack complete characterization of mineral hosts including ion-adsorption clays (IACs). IACs, which provide a substantial portion of the global REE supply, form when hydrated REE complexes are physiosorbed to negatively charged sites on clay surfaces. Typical IAC minerals include kaolinite, illite, and smectite. REE complexes are easily leached from IACs via ion exchange, making them economically attractive. However, research regarding the partitioning behaviors of strategic metals into PRB coal-hosted clays is limited. This project aims to quantify the modes of occurrence and distribution of REE and CM in clay minerals derived from PRB coals. Strategic metals investigated in this project have historic production or promising deposits within the PRB and/or high anticipated supply risk, and include: REE (lanthanides and Y), V, Ti, platinum group elements (PGE; Pt, Pd, Rh, Ru, Ir, Os), Al, Co, Ga, Zr, and W. Semi-quantitative compositional data from portable x-ray fluorescence spectroscopy (pXRF) on coal core from the PRB is consistent with the known correlation between REE and Al content, suggesting increased REE abundance will be found in clay-rich units. pXRF data also identifies elevated V, Ti, Co, and Zr correlated with Al. Preliminary x-ray diffraction (XRD) data indicates kaolinite, illite, and dickite are abundant minerals in overburden and clay-rich partings. Quantitative analysis via inductively coupled plasma mass spectrometry (ICP-MS) provides insights into REE and CM fractionation in coals as we compare trace metal occurrence to variations in mineralogy. Additionally, we apply scanning electron microscopy (SEM) and energy dispersive x-ray spectroscopy (EDS) data to correlate clay size and morphology to trace metal occurrence. Our findings supplement a broader effort to couple REE and CM extraction with existing domestic coal production and contribute to the fundamental understanding of adsorption processes that occur in low-temperature basin environments.

Stuart, Sophia↗

DOE OSTI2022

Magnetic pair distribution function data using polarized neutrons and ad hoc corrections

Here, we report the first example of magnetic pair distribution function (mPDF) data obtained through the use of neutron polarization analysis. Using the antiferromagnetic semiconductor MnTe as a test case, we present high-quality mPDF data collected on the HYSPEC instrument at the Spallation Neutron Source using longitudinal polarization analysis to isolate the magnetic scattering cross section. Clean mPDF patterns are obtained for MnTe in both the magnetically ordered state and the correlated paramagnet state, where only short-range magnetic order is present. We also demonstrate significant improvement in the quality of high-resolution mPDF data through the application of ad hoc corrections that require only minimal human input, minimizing potential sources of error in the data processing procedure. We briefly discuss the current limitations and future outlook of mPDF analysis using polarized neutrons. Overall, this work provides a useful benchmark for mPDF analysis using polarized neutrons and provides an encouraging picture of the potential for routine collection of high-quality mPDF data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Investigation of K + K – interactions via femtoscopy in Pb-Pb collisions at s N N = 2.76 TeV at the CERN Large Hadron Collider

Femtoscopic correlations of nonidentical charged kaons (K + K – ) are studied in Pb-Pb collisions at a center-of-mass energy per nucleon-nucleon collision $\sqrt{s_{NN}}$=2.76 TeV by ALICE at the CERN Large Hadron Collider. One-dimensional K + K – correlation functions are analyzed in three centrality classes and eight intervals of particle-pair transverse momentum. The Lednický and Luboshitz interaction model used in the K + K – analysis includes the final-state Coulomb interactions between kaons and the final-state interaction through a 0 (980) and f 0 (980) resonances. The mass of f 0 (980) and coupling were extracted from the fit to K + K – correlation functions using the femtoscopic technique. The measured mass and width of the f 0 (980) resonance are consistent with other published measurements. The height of the $\phi$(1020) meson peak present in the K + K – correlation function rapidly decreases with increasing source radius, qualitatively in agreement with an inverse volume dependence. A phenomenological fit to this trend suggests that the $\phi$(1020) meson yield is dominated by particles produced directly from the hadronization of the system. The small fraction subsequently produced by final-state interactions could not be precisely quantified with data presented in this paper and will be assessed in future work.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Ternary complexes of isopentenyl phosphate kinase from Thermococcus paralvinellae reveal molecular determinants of non‐natural substrate specificity

Abstract Isopentenyl phosphate kinases (IPKs) have recently garnered attention for their central role in biocatalytic “isoprenol pathways,” which seek to reduce the synthesis of the isoprenoid precursors to two enzymatic steps. Furthermore, the natural promiscuity of IPKs toward non‐natural alkyl‐monophosphates (alkyl‐Ps) as substrates has hinted at the isoprenol pathways' potential to access novel isoprenoids with potentially useful activities. However, only a handful of IPK crystal structures have been solved to date, and even fewer of these contain non‐natural substrates bound in the active site. The current study sought to elucidate additional ternary complexes bound to non‐natural substrates using the IPK homolog fromThermococcus paralvinellae(TcpIPK). Four such structures were solved, each bound to a different non‐natural alkyl‐P and the phosphoryl donor substrate/product adenosine triphosphate (ATP)/adenosine diphosphate (ADP). As expected, the quaternary, tertiary, and secondary structures ofTcpIPK closely resembled those of IPKs published previously, and kinetic analysis of a novel alkyl‐P substrate highlighted the potentially dramatic effects of altering the core scaffold of the natural substrate. Even more interesting, though, was the discovery of a trend correlating the position of two α helices in the active site with the magnitude of an IPK homolog's reaction rate for the natural reaction. Overall, the current structures ofTcpIPK highlight the importance of continued structural analysis of the IPKs to better understand and optimize their activity with both natural and non‐natural substrates.

Biochemistry & Molecular Biology↗