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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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569 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Widespread Distribution and Functional Specificity of the Copper Importer CcoA: Distinct Cu Uptake Routes for Bacterial Cytochrome c Oxidases

ABSTRACT Cytochrome c oxidases are members of the heme-copper oxidase superfamily. These enzymes have different subunits, cofactors, and primary electron acceptors, yet they all contain identical heme-copper (Cu B ) binuclear centers within their catalytic subunits. The uptake and delivery pathways of the Cu B atom incorporated into this active site, where oxygen is reduced to water, are not well understood. Our previous work with the facultative phototrophic bacterium Rhodobacter capsulatus indicated that the copper atom needed for the Cu B site of cbb 3 -type cytochrome c oxidase ( cbb 3 -Cox) is imported to the cytoplasm by a major facilitator superfamily-type transporter, CcoA. In this study, a comparative genomic analysis of CcoA orthologs in alphaproteobacterial genomes showed that CcoA is widespread among organisms and frequently co-occurs with cytochrome c oxidases. To define the specificity of CcoA activity, we investigated its function in Rhodobacter sphaeroides , a close relative of R. capsulatus that contains both cbb 3 - and aa 3 -Cox. Phenotypic, genetic, and biochemical characterization of mutants lacking CcoA showed that in its absence, or even in the presence of its bypass suppressors, only the production of cbb 3 -Cox and not that of aa 3 -Cox was affected. We therefore concluded that CcoA is dedicated solely to cbb 3 -Cox biogenesis, establishing that distinct copper uptake systems provide the Cu B atoms to the catalytic sites of these two similar cytochrome c oxidases. These findings illustrate the large variety of strategies that organisms employ to ensure homeostasis and fine control of copper trafficking and delivery to the target cuproproteins under different physiological conditions. IMPORTANCE The cbb 3 - and aa 3 -type cytochrome c oxidases belong to the widespread heme-copper oxidase superfamily. They are membrane-integral cuproproteins that catalyze oxygen reduction to water under hypoxic and normoxic growth conditions. These enzymes diverge in terms of subunit and cofactor composition, yet they all share a conserved heme-copper binuclear site within their catalytic subunit. In this study, we show that the copper atoms of the catalytic center of two similar cytochrome c oxidases from this superfamily are provided by different copper uptake systems during their biogenesis. This finding illustrates different strategies by which organisms fine-tune the trafficking of copper, which is an essential but toxic micronutrient.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Many but not all deep neural network audio models capture brain responses and exhibit correspondence between model stages and brain regions

Models that predict brain responses to stimuli provide one measure of understanding of a sensory system and have many potential applications in science and engineering. Deep artificial neural networks have emerged as the leading such predictive models of the visual system but are less explored in audition. Prior work provided examples of audio-trained neural networks that produced good predictions of auditory cortical fMRI responses and exhibited correspondence between model stages and brain regions, but left it unclear whether these results generalize to other neural network models and, thus, how to further improve models in this domain. We evaluated model-brain correspondence for publicly available audio neural network models along with in-house models trained on 4 different tasks. Most tested models outpredicted standard spectromporal filter-bank models of auditory cortex and exhibited systematic model-brain correspondence: Middle stages best predicted primary auditory cortex, while deep stages best predicted non-primary cortex. However, some state-of-the-art models produced substantially worse brain predictions. Models trained to recognize speech in background noise produced better brain predictions than models trained to recognize speech in quiet, potentially because hearing in noise imposes constraints on biological auditory representations. The training task influenced the prediction quality for specific cortical tuning properties, with best overall predictions resulting from models trained on multiple tasks. The results generally support the promise of deep neural networks as models of audition, though they also indicate that current models do not explain auditory cortical responses in their entirety.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Dilute magnetic impurity-induced effective phonon magnetic moment in Fe-doped monolayer MoS 2

Realization of large effective phonon magnetic moment in monolayer MoS 2 has established an important route for exploring intriguing magnetic phenomena in a nonmagnetic material. The sizable coupling between the orbital transition and the circularly polarized phonon results in the large effective phonon magnetic moment. In this work, using magneto-Raman spectroscopy, we investigate substitutional doping of magnetic atoms as a tuning knob of the electronic and phononic properties of MoS 2 . We show that Fe-doping polarizes the spin of the conduction bands and introduces a localized Fe band underneath the conduction band. As a result, an additional orbital transition between the Mo 4d and Fe 3d states emerges, producing an orbital-phonon hybridized mode at 283 cm −1 . Our magnetic field dependent measurements demonstrate that this new mode carries 2.8 $\mu_{\mathrm{B}}$ effective phonon magnetic moment, which is comparable to that of the undoped MoS 2 . Moreover, even though a long-range magnetic order is absent in Fe-doped MoS 2 , the local magnetic moment of Fe modifies the nature of the spin fluctuation, producing monotonically increasing quasielastic scattering spectral weight as temperature decreases. Our results highlight two-dimensional dilute magnetic semiconductors synthesized by substitutional doping as a promising material platform to manipulate the phonon magnetic moment through orbital-phonon coupling.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A New Family of Ternary Intermetallic Compounds with Dualistic Atomic Ordering – The ZIP Phases

A new family of nanostructured ternary intermetallic compounds − named the ZIP phases − is introduced in this work. The ZIP phases exhibit dualistic atomic ordering, i.e., they form two structural variants: one with the fcc diamond cubic structure (space group Fd$\bar{3}$m) and one with the hexagonal structure (space group P6 3 /mmc). They are also characterized by metallic behavior, ionic bonding, and atomic zigzagging. Powder metallurgical routes involving pressure-assisted densification are adopted to demonstrate ZIP phase synthesis in the Nb-Si-Ni, Nb-Si-Co, Ta-Si-Ni, V-Si-Ni, and Nb-Si-Fe ternary systems. Crucially, reactive hot pressing is capable of producing high-purity ZIP phase materials after the judicious, elemental system-specific optimization of the processing route. Synthesis of phase-pure materials – demonstrated in the Nb-Si-Ni ternary system by the synthesis of quasi phase-pure Nb 3 SiNi 2 and Ni 3 SiNb 2 ZIP phase-based materials – is a steppingstone to the prospective exploitation of the ZIP phases. Characterization of Nb 3 SiNi 2 and Ni 3 SiNb 2 involves crystal structure determination, spatially resolved chemical analysis, and determination of select thermal, electrical, magnetic, mechanical, and physical properties. Density functional theory is used to assess the stability of Nb 3 SiNi 2 & Ni 3 SiNb 2 and derivative binary compounds at different temperatures, also exploring the exfoliation of these two ZIP phases along specific surfaces to produce 2D derivatives.

Intermetallic compounds (IMCs)↗
DOE OSTI2021

Analysis and prediction of reaction kinetics using the degree of rate control

“Degree of rate control” (DRC) analysis provides a quantitative approach for analysing the kinetics of multi-step reaction mechanisms that has been widely applied to both heterogeneous and homogeneous catalysis research, as well as electrocatalysis. The DRC of any given transition state or intermediate is defined as a partial derivative such that it approximately equals the fractional increase in net rate to the product of interest per differential decrease in its standard-state free energy for that species (÷RT), holding constant the standard-state free energies of all other transition states and intermediates. Even very complex mechanisms usually have only a few species with non-zero DRCs and are thus the species whose interactions with the catalyst most strongly affect the net rate. These key DRC values thus offer a simple and intuitive route to optimize catalyst materials, especially with the assistance of computational methods like density functional theory (DFT). These high-DRC species are also the species whose energetics must be most accurately measured or calculated to achieve an accurate kinetic model for any reaction mechanism. In simple cases with a single “rate-determining step”, the DRC for its transition state (TS) is + 1. Catalyst-bound intermediates, on the other hand, often have negative DRCs equal to a small integer times their fractional occupancy of catalyst sites. The apparent activation energy equals a weighted average of the standard-state enthalpies (relative to reactants) of all the species (intermediates, transition states and products) in the reaction mechanism, each weighted by its DRC (+RT). It has been shown that the apparent transfer coefficient in electrocatalysis, an inverted form of the Tafel slope, is a weighted average of the number of electrons transferred to generate each intermediate or product species in the mechanism, weighted again by the DRC. Quantitative analysis of kinetic isotope effects (KIEs, or the ratio of net rates for different reactant isotopes) in complex mechanisms has shown that the logarithm of the KIE equals the weighted average over all species in the mechanism of the difference between the two isotopes in their standard-state free energies (÷RT), again weighted by the DRC. The reaction orders with respect to fluid-phase concentrations of reactants, products and intermediates have also been proven to be directly related to DRCs. Thus, there are numerous experimental observables which equate to short linear combinations of DRCs, so that combinations of experimental measurements might provide access to DRC values. Since its invention for transition states in 1994 and its generalization to include intermediates in 2009, the DRC has thus far mainly been calculated for microkinetic models based either entirely on DFT or on DFT with the key energies (i.e., those for high-DRC species) being fine-tuned to match experiments. The relationships summarized in this work provide new opportunities for using experiments earlier in the development and optimization of microkinetic models that require input from computational catalysis.

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

Development and Experimental Optimization of High-Temperature Modeling Tools and Methods for Concentrated Solar Power Particle - Systems

A novel, open-source radiative modeling toolset was developed to extend the functionality of particle-based modeling software (e.g. discrete element method (DEM)) to environmental conditions relevant to concentrated solar power applications. This toolset was optimized for deployment on desktop workstations instead of high-performance computing systems, to render such tools more accessible to the research community. Both particle-based modeling and radiative exchange modeling are computationally expensive and often require specialized programming expertise, making these methods cumbersome to use. Recent developments in DEM software by DCS Computing have greatly reduced these challenges, providing a graphical-user-interface based platform and modeling optimization for desktop workstations, HPCs, and cloud computing. The University of Dayton leveraged the experience of DCS Computing in developing a user-friendly, open-source radiative heat transfer expansion for DEM modeling. The University of Dayton DEM+ radiative modeling toolset was developed using a combination of fundamental experimental measurements, modeling, and simplified flow experiments over a range of temperatures and flow conditions. The toolset provides researchers with access to multiple radiative models including an accelerated Monte-Carlo Ray Tracing (application agnostic, highly computationally expensive), an expanded database of distance-based approximations (application limited, computationally light), and a weighted blending of the two methods capable of achieving over 90% reduction in computation time with equivalent accuracy compared to Monte-Carlo Ray Tracing. Through a graphical user interface, users can customize the radiative models to match their desired accuracy and available computational resources, improving access to particle based modeling for the research community. Ceramic sintered bauxite proppants were used in modeling and experimentally as a baseline. Both the radiative heat transfer and flow properties for particulate systems were investigated at elevated temperatures up to 800 °C. The major accomplishments for this work include a verified, open-source radiative modeling toolset to be distributed amongst the research community and the fabrication of three small-scale test facilities to investigate particle behavior and tune DEM flow properties for operation up to 800 °C. The findings have been shared with the research community via conference modeling workshops, deployment of the tools in DCS Computing Aspherix®, and open-source access to the developed radiative modeling tool. The development of next-generation CSP facilities and thermal energy storage systems based on ceramic particles requires providing access to computationally efficient and accurate modeling tools. Particles will experience a wide range of environments (20-800 °C) and handling conditions (dilute curtains or dense packing), requiring specially designed and optimized equipment. Optimizing solid particle physics models and establishing best-practices for particle modeling in CSP environments will assist researchers with designing optimized equipment, accelerating the deployment of more economically-competitive CSP facilities.

14 SOLAR ENERGY↗
DOE OSTI2020

Crystal structure and physical properties of Yb 2 In and Eu 2–x Yb x In alloys

While binary R E 2 In , where R E = rare earth , have been reported a few decades ago, recent investigations revealed intriguing new physical insights. For instance, the discovery of a nearly ideal first-order ferromagnetic transition in Eu 2 In calls for further exploration of structures and properties of R E 2 In , in particular for the least-documented R E = Eu and Yb cases. In this work, we investigate Eu 2 – x Yb x In pseudobinaries with nominal values of x = 0.25 , 0.5, 0.75, 1, 1.5, 2 by powder x-ray diffraction (including as function of temperature from 100 to 375 K for Yb 2 In ), magnetization (5–300 K), as well as electrical resistivity (5–300 K) and calorimetric (2–150 K) measurements for Yb 2 In . Compared to other RE , Yb or Eu always raise challenging questions linked to their valence states. From average atomic volume, Yb is anticipated to be divalent in Yb 2 In , at least between 100 and 375 K, which is in line with the absence of 4 f magnetism. In agreement with x-ray diffraction and magnetization data, the resistivity of Yb 2 In is rather featureless and typical of a metal. Establishing Yb 2 In as a nonmagnetic isostructural reference for Eu 2 In allows one to use its heat capacity to revisit that of the latter, and get experimental insights into the exceptional magnetocaloric effect of the compound with Eu. In particular, we show that a third of the total magnetic entropy ( S m ≈ 35.6 J mo l – 1 K – 1 at T = 100 K ) is concentrated in a 3 K temperature window around the T C of Eu 2 In . Starting from the ferromagnetic compound Eu 2 In [ T C = 55.2 ( 5 ) K ] , we show that Yb substitutions in Eu 2 – x Yb x In lead to a decrease in both the Curie temperature [ T C = 41 ( 2 ) and 32(2) K for x = 0.25 and 0.5] and magnetic saturation, while weakening the first-order character of the transition as x increases. A significant isothermal entropy change of 5.1 ( 4 ) J mo l – 1 K – 1 for Δ B = 2 T is found at 44 K in Eu 1.75 Yb 0.25 In , demonstrating that the giant magnetocaloric effect of Eu 2 In can be tuned to lower temperatures by Yb substitutions.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Extreme-Scale Bayesian Inference for Uncertainty Quantification of Complex Simulations

Uncertainty quantification (UQ)—that is, quantifying uncertainties in complex mathematical models and their large-scale computational implementations—is widely viewed as one of the outstanding challenges facing the field of CS&E over the coming decade. The EUREKA project set to address the most difficult class of UQ problems: those for which both the underlying PDE model as well as the uncertain parameters are of extreme scale. In the project we worked on these extreme-scale challenges in the following four areas: 1. Scalable parallel algorithms for sampling and characterizing the posterior distribution that exploit the structure of the underlying PDEs and parameter-to-observable map. These include structure-exploiting versions of the randomized maximum likelihood method, which aims to overcome the intractability of employing conventional MCMC methods for solving extreme-scale Bayesian inversion problems by appealing to and adapting ideas from large-scale PDE-constrained optimization, which have been very successful at exploring high-dimensional spaces. 2. Scalable parallel algorithms for construction of prior and likelihood functions based on learning methods and non-parametric density estimation. Constructing problem-specific priors remains a critical challenge in Bayesian inference, and more so in high dimensions. Another challenge is construction of likelihood functions that capture unmodeled couplings between observations and parameters. We will create parallel algorithms for non-parametric density estimation using high dimensional N-body methods and combine them with supervised learning techniques for the construction of priors and likelihood functions. 3. Bayesian inadequacy models, which augment physics models with stochastic models that represent their imperfections. The success of the Bayesian inference framework depends on the ability to represent the uncertainty due to imperfections of the mathematical model of the phenomena of interest. This is a central challenge in UQ, especially for large-scale models. We propose to develop the mathematical tools to address these challenges in the context of extreme-scale problems. 4. Parallel scalable algorithms for Bayesian optimal experimental design (OED). Bayesian inversion yields quantified uncertainties in the model parameters, which can be propagated forward through the model to yield uncertainty in outputs of interest. This opens the way for designing new experiments to reduce the uncertainties in the model parameters and model predictions. Such experimental design problems have been intractable for large-scale problems using conventional methods; we will create OED algorithms that exploit the structure of the PDE model and the parameter-to-output map to overcome these challenges. Parallel algorithms for these four problems were created, analyzed, prototyped, implemented, tuned, and scaled up for leading-edge supercomputers, including UT-Austin’s own 10 petaflops Stampede system, ANL’s Mira system, and ORNL’s Titan system. While our focus is on fundamental mathematical/computational methods and algorithms, we will assess our methods on model problems derived from several DOE mission applications, including multiscale mechanics and ice sheet dynamics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Large tetragonality and room temperature ferroelectricity in compressively strained CaTiO 3 thin films

We report that ferroelectricity and piezoelectricity are desirable for a variety of high-temperature applications such as actuators and sensors in heat engines, high-temperature manufacturing, and space technologies; however, the number of such material candidates is currently limited. Here, we demonstrate that CaTiO 3 , the first discovered perovskite mineral that is abundantly found in the Earth, as a non-polar bulk form, becomes a high-temperature ferroelectric oxide under compression. A strain-phase-temperature diagram of CaTiO 3 is created by growing a series of thin films with a range of compressive and tensile strains. Using temperature dependent optical second harmonic generation analysis, we show that tensile strained films exhibit predominantly in-plane polarization with orthorhombic-like point group symmetry with a phase transition below room temperature. On the other hand, compressively strained CaTiO 3 films exhibit a neartetragonal unit cell with a c/a ratio of 1.03, larger than that of classic ferroelectric, e.g. BaTiO 3 (c/a~1.01). These films exhibit a robust and switchable out-of-plane polarization at room temperature, with a ferroelectric transition temperature up to ~800 K. Density functional theory calculations reveal that compressive strain gives rise to a large out-of-plane displacement of Ti-cations inside the TiO 6 octahedral cages responsible for the strain induced polarization of ~ 9μC/cm 2 . Given that nearly half of the perovskites exhibit the bulk symmetry of CaTiO 3 , compressive strain tuning of this family may prove to be a fertile ground for the discovery of strain-induced piezoelectrics and ferroelectrics at high-temperatures. High-temperature piezoelectric sensors are important for many industries such as automotive, aircraft, and aerospace, as well as nuclear and geothermal energy, where dynamic structural monitoring of high-speed rotors in engines, turbines, and power generators is critical for safety and reliability.[1, 2] Modern sensors are however limited, where the main challenges are maximizing operating temperatures while maintaining good sensing capability and low electrical leakage.[3] High-temperature piezoelectric materials are typically derived from BaTiO 3 or PbTiO 3 based ceramics and single crystals, as these materials are also ferroelectric, meaning that they have a built-in spontaneous polarization (Ps). In bulk, these standard ferroelectric perovskite materials yield maximum operating temperatures ranging from 250-450 °C.[4], where above these temperatures the spontaneous polarization (P) and piezoelectric properties disappear due to the onset of a ferroelectric-to-paraelectric phase transition (TC). As next-generation engines, turbines, and reactors require hotter operating temperatures above 500 °C, materials with enhanced ferroelectric properties and increased ferroelectric transition temperatures will be needed in such extreme conditions. A promising way of boosting ferroelectric transition temperatures and polarization is through epitaxial strain engineering,[5] where tremendous in-plane distortions can be applied to thin film crystal materials, well beyond the point at which their bulk ceramic and single crystal counterparts would crack. Strain engineering has proven to drastically enhance ferroelectric polarizations and transition temperatures in classic ferroelectric materials such as BaTiO 3 [6] and PbTiO 3 [7], and strain can even turn on ferroelectricity in otherwise non-polar materials such as SrTiO 3 and CaTiO 3 ,[8] although reported strain-induced ferroelectric transition temperatures are only at room temperature or below.[9-12] In this work, we will focus on strain-induced ferroelectricity in CaTiO 3 , the first discovered perovskite mineral and long known to be a simple nonpolar insulating crystal.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Atomically Precise Membranes for the Separation of Gases

Industrial separations require enormous amounts of energy, accounting for approximately half the industrial energy use and 10–15% of the total energy consumption. Distillation alone accounts for about half the energy demand for industrial separations. If these processes could be replaced by an energy-efficient membrane separation process, this energy demand could be reduced by 90%. However, although some membrane processes have made inroads into thermal distillation, for membrane-based separations to replace the distillation process to a far more significant and practical level, new membranes with higher robustness, selectivity, and flux still need be developed. In this membrane development program, we leveraged Temple University’s prior work in spiroligomers to develop robust membrane structures. These structures can be formed into atomically precise pores by controlling the chemical synthesis and the oligomer building blocks to first build precisely controlled macrocycles and then subsequently crosslinking these macrocycles to produce a membrane. Furthermore, through the highly controllable chemistry of our molecular building blocks, not only can we integrate pores with highly controllable and reproducible size and morphology, but we can also target internal functionalization. By using a range of scalable membrane synthesis approaches, combined with control of each and every pores internal chemistry and molecular conformation we can achieve membrane structures that can display the ultimate in high selectivity and permeance. The Mainstream-Temple University membranes can be designed to achieve ultra-selective separations based on the key factors of molecular size, shape, and functionality. In our approach to fabricate scalable atomically precise membranes, we used our molecular Lego nanostructures. We demonstrated an approach to create atomically precise pores within the membrane with pores that are the dimensions of the molecule we are trying to separate. Moreover, in addition to controlling the morphology of the membrane structure by controlling the size of every pore, we can also decorate every pore with precisely targeted and placed functional groups. These accurately placed functional groups can provide selective binding to molecules and provide enhanced selectivity via a facilitated transport mechanism. In Phase II, the Mainstream-Temple University team demonstrated the scalable synthesis of oligomers and the ability to control the pore internal and external functionality, or chemistry, to allow the fabrication of a thin-film membrane. The initial steps of the spiroligomer synthesis to produce the two key bis-amino enantiomer building blocks were scaled from the gram scale to the kilogram scale, obtaining 25 kg of the materials by transferring the procedure to a toll manufacturer. Finally, we successfully optimized the fabrication of these macrocycles into robust membranes. We successfully transitioned from a laboratory based, hard-to-scale Langmuir trough synthesis to a highly scalable, roll-to-roll applicable, interfacial polymerization process. During this Phase II program, we established a platform of atomically precise membranes where our highly controllable, atomically precise macrocycles served as a scaffold with precisely and uniformly controlled pores. Furthermore, this layer can be tailored to accommodate a diverse range of functional groups, which both further controlled the pore size to enhance the sieving effect as well as imparting precisely controlled targeted selectivity through biomimetic molecular interactions. In this Phase II, we established and scaled up a platform approach to both tune the pore size and chemistry as well as scale it to produce membranes that can be applied to a wide range of industries. In future development, the project team expects to scale up both the macrocycle building block production and interfacial polymerization process to produce the atomically precise membrane with targeted pore sizes and pore chemistries.

36 MATERIALS SCIENCE↗
DOE OSTI2026

In Situ Studies of Ru-CeO 2 –TiO 2 Catalysts for Selective CO 2 Hydrogenation to Methane: Importance of Metal ↔ Oxide–Oxide Interactions

Here, this work investigates Ru-CeO 2 -TiO 2 catalysts for the CO 2 methanation reaction and compares their performance with previously studied Ru-CeO 2 systems. Despite the lower Ru loading, the TiO 2 -containing catalysts exhibit significantly higher activity. To understand this behavior, in situ X-ray absorption spectroscopy (XAS) was carried out at the Ru K-edge and Ce L 3 -edge. Unlike Ru-CeO 2 , which displays reversible redox behavior of Ru, the Ru-CeO 2 -TiO 2 catalysts show irreversible Ru reduction and a substantially higher fraction of Ce 3+ species under all tested conditions (H 2 , CO 2 , H 2 /CO 2 ). The stabilization of metallic Ru during methanation, together with the enhanced formation of Ce 3+ promoted by TiO 2 through interfacial electronic transfer, accounts for their superior performance. Complementary in situ DRIFTS measurements reveal the formation and rapid consumption of bidentate carbonates and formates. These species act as a key intermediate in methane formation. Overall, these findings highlight the crucial role of the mixed CeO 2 -TiO 2 oxide in tuning the surface chemistry of the catalysts by stabilizing metallic Ru, enhancing ceria reducibility, and promoting efficient reaction pathways for CO 2 methanation. The manipulation of metal↔oxide-oxide interactions can be a very useful tool when dealing with the valorization of CO 2 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗