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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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Optimizing Component–Component Interaction Energies in the Self-Assembly of Finite, Multicomponent Structures
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Evolution and interplay of lithium metal interphase components revealed by experimental and theoretical studies
ABSTRACT: Lithium metal batteries (LMB) have high energy densities and are crucial for clean energy solutions. The characterization of lithium metal interphase is fundamentally and practically important but technically challenging. Taking advantage of synchrotron x-ray which has the unique capability of analyzing crystalline/amorphous phases quantitative-ly with statistical significance, we study the composition and dynamics of LMB interphase for a newly developed im-portant LMB electrolyte that is based on fluorinated ether. Pair distribution function analysis revealed the sequential role of anion and solvent in interphase formation during cycling. The relative ratio between Li2O and LiF first increases and then decreases during cycling, suggesting suppressed Li2O formation in both initial and long extended cycles. Theoretical studies revealed that in initial cycles, this is due to the energy barrier in many-electron transfer. In long extended cycles, the anion decomposition product Li2O encourages solvent decomposition by facilitating solvent adsorption on Li2O which is followed by concurrent depletion of both. This work highlights the important role of Li2O in transitioning from anion-derived interphase to a solvent-derived one.
Multi-component background learning automates signal detection for spectroscopic data
Abstract Automated experimentation has yielded data acquisition rates that supersede human processing capabilities. Artificial Intelligence offers new possibilities for automating data interpretation to generate large, high-quality datasets. Background subtraction is a long-standing challenge, particularly in settings where multiple sources of the background signal coexist, and automatic extraction of signals of interest from measured signals accelerates data interpretation. Herein, we present an unsupervised probabilistic learning approach that analyzes large data collections to identify multiple background sources and establish the probability that any given data point contains a signal of interest. The approach is demonstrated on X-ray diffraction and Raman spectroscopy data and is suitable to any type of data where the signal of interest is a positive addition to the background signals. While the model can incorporate prior knowledge, it does not require knowledge of the signals since the shapes of the background signals, the noise levels, and the signal of interest are simultaneously learned via a probabilistic matrix factorization framework. Automated identification of interpretable signals by unsupervised probabilistic learning avoids the injection of human bias and expedites signal extraction in large datasets, a transformative capability with many applications in the physical sciences and beyond.
Dose-rate controlled energy dispersive x-ray spectroscopic mapping of the metallic components in a biohybrid nanosystem
In this work, we showcase that through precise control of the electron dose rate, state-of-the-art large solid angle energy dispersive X-ray spectroscopy (EDS) mapping in aberration-corrected scanning transmission electron microscope (STEM) is capable of faithful and unambiguous chemical characterization of the Pt and Pd distribution in a peptide-mediated nanosystem. This low-dose-rate recording scheme adds another dimension of flexibility to the design of elemental mapping experiments, and holds significant potential for extending its application to a wide variety of beam sensitive hybrid nanostructures.
Tracking scattered signals in the acoustic coda using independent component analysis in a topographically complex setting
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Effective model of QCD magnetic monopoles from numerical study of one- and two-component Coulomb quantum Bose gases
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LCOE Design Optimization Using Genetic Algorithm with Improved Component Models for Medium-Voltage Transformerless PV Inverters
For real-world installations of photovoltaic and other renewable energy resources, the critical design metric is the levelized cost of energy (LCOE); however, many power electronics design optimizations are performed with efficiency and power density as the primary design goals. Recent work has shown that a new LCOE-focused optimization approach can yield improved system designs balancing cost and energy generation. This paper expands the LCOE optimization approach by considering comprehensive optimization parameters, adding new modeling of inductor cost, extending the semiconductor model to include effects of losses on housing cost, and implementing a genetic algorithm to improve computation efficiency.
Viscosity of glass-forming melt at the bottom of high-level waste melter feed cold caps: Effects of temperature and incorporation of solid components
At the final stages of conversion of melter feed (glass batch) to molten glass, the transient glass-forming melt becomes a continuous liquid phase encapsulating dissolving solid particles and gas bubbles that produce primary foam at the bottom of the cold cap (the reacting melter feed in an electric glass melting furnace). The glass-forming melt viscosity plays a dominant role in primary foam formation, stability, and eventual collapse, thus affecting the rate of melting (the glass production rate per cold cap area). For several melter feeds designed for nuclear waste vitrification, we have traced the glass-forming melt viscosity during the final stages of feed-to-glass conversion as it changes in response to changing temperature and composition (resulting from dissolving solid particles). Starting with a relatively low values at the moment when the melt connects, melt viscosity reaches maximum within the primary foam layer and then decreases to its final melter-operating temperature value. We paid a particular attention to the cold-cap bottom—the boundary between the primary foam layer and the thermal boundary layer—where the melt viscosity affects the rate of melting predominantly through its effect on the temperature at which primary foam is collapsing.
Inhibition of fucosylation of cell wall components by 2‐fluoro 2‐deoxy‐ l ‐fucose induces defects in root cell elongation
Summary Screening of commercially available fluoro monosaccharides as putative growth inhibitors in Arabidopsis thaliana revealed that 2‐fluoro 2‐ l ‐fucose (2F‐Fuc) reduces root growth at micromolar concentrations. The inability of 2F‐Fuc to affect an Atfkgp mutant that is defective in the fucose salvage pathway indicates that 2F‐Fuc must be converted to its cognate GDP nucleotide sugar in order to inhibit root growth. Chemical analysis of cell wall polysaccharides and glycoproteins demonstrated that fucosylation of xyloglucans and of N ‐linked glycans is fully inhibited by 10 μ m 2F‐Fuc in Arabidopsis seedling roots, but genetic evidence indicates that these alterations are not responsible for the inhibition of root development by 2F‐Fuc. Inhibition of fucosylation of cell wall polysaccharides also affected pectic rhamnogalacturonan‐ II ( RG ‐ II ). At low concentrations, 2F‐Fuc induced a decrease in RG ‐ II dimerization. Both RG ‐ II dimerization and root growth were partially restored in 2F‐Fuc‐treated seedlings by addition of boric acid, suggesting that the growth phenotype caused by 2F‐Fuc was due to a deficiency of RG ‐ II dimerization. Closer investigation of the 2F‐Fuc‐induced growth phenotype demonstrated that cell division is not affected by 2F‐Fuc treatments. In contrast, the inhibitor suppressed elongation of root cells and promoted the emergence of adventitious roots. This study further emphasizes the importance of RG ‐ II in cell elongation and the utility of glycosyltransferase inhibitors as new tools for studying the functions of cell wall polysaccharides in plant development. Moreover, supplementation experiments with borate suggest that the function of boron in plants might not be restricted to RG ‐ II cross‐linking, but that it might also be a signal molecule in the cell wall integrity‐sensing mechanism.
Reman Co-Design: A Combined Design and Remanufacturing Optimization Framework for the Sustainable Design of High-Value Components
Remanufacturing is a process that returns end-of-life equipment to as-new conditions and offers numerous environmental and economic benefits. To fully capitalize on remanufacturing, its synergistic interactions with design must be fully realized and addressed during the design stage. Although this fact is widely recognized in the literature, most of the current studies focus primarily either on the design or remanufacturing aspects of design for remanufacturing (DfRem). In an effort to offer a more integrated DfRem approach than those reported in the literature, we propose a new combined design and remanufacturing optimization (reman co-design) framework that takes a holistic approach by leveraging the intricate interplay between design and remanufacturing. Here, the aim of this formulation is to identify the optimal decisions that maximize the benefits of remanufacturing throughout the entire lifespan of a product. To showcase the utility of the new formulation, we are using a case study of a hydraulic manifold, (re)manufactured by John Deere. Using this industry example, we compare the results of reman co-design to the ones from a decoupled remanufacturing design approach. Results reveal that remanufacturing benefits are better realized and improved upon when using the developed reman co-design approach.
sCO2 Power Cycle Component Cost Correlations from DOE Data Spanning Multiple Scales and Applications.
Abstract not provided.
Determination of Single- and Multi-Component Nanoparticle Sizes by X-ray Absorption Spectroscopy
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Analysis of Electric Vehicle Charging Behavior Patterns with Function Principal Component Analysis Approach
This manuscript focused on analyzing electric vehicles’ (EV) charging behavior patterns with a functional data analysis (FDA) approach, with the goal of providing theoretical support to the EV infrastructure planning and regulation, as well as the power grid load management. 5-year real-world charging log data from a total of 455 charging stations in Kansas City, Missouri, was used. The focuses were placed on analyzing the daily usage occupancy variability, daily energy consumption variability, and station-level usage variability. Compared with the traditional discrete-based analysis models, the proposed FDA modeling approach had unique advantages in preserving the smooth function behavior of the data, bringing more flexibility in the modeling process with little required assumptions or background knowledge on independent variables, as well as the capability of handling time series data with different lengths or sizes. In addition to the patterns revealed in the EV charging station’s occupancy and energy consumption, the differences between EV driver’s charging time and parking time were analyzed and called for the needs for parking regulation and enforcement. The different usage patterns observed at charging stations located on different land-use types were also analyzed.