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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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The effective number of parameters in kernel density estimation
We devise a new formula for measuring the effective degrees of freedom (EDoF) in kernel density estimation (KDE). Starting from the orthogonal polynomial sequence (OPS) expansion for the ratio of the empirical to the oracle density, we show how convolution with the kernel leads to a new OPS with respect to which one may express the resulting KDE. The expansion coefficients of the two OPS systems can then be related via a kernel sensitivity matrix, which leads to a natural oracle definition of EDoF through the trace operator. Asymptotic properties of the (empirical) plug-in EDoF are worked out through influence functions, and connections with other empirical EDoFs are established. Minimization of Kullback-Leibler divergence is investigated as an alternative to integrated squared error based bandwidth selection rules, yielding a new normal scale rule. The methodology, which arises from a proper oracle formulation and is not restricted to convolution kernels, suggests the possibility of a new bandwidth selection rule based on an information criterion such as AIC.
Error-mitigated data-driven circuit learning on noisy quantum hardware
Application-level benchmarks measure how well a quantum device performs meaningful calculations. In the case of parameterized circuit training, the computational task is the preparation of a target quantum state via optimization over a loss landscape. This is complicated by various sources of noise, fixed hardware connectivity, and generative modeling, the choice of target distribution. Gradient-based training has become a useful benchmarking task for noisy intermediate-scale quantum computers because of the additional requirement that the optimization step uses the quantum device to estimate the loss function gradient. In this work, we use gradient-based data-driven circuit learning to qualitatively evaluate the performance of several superconducting platform devices and present results that show how error mitigation can improve the training of quantum circuit Born machines with 28 tunable parameters.
Extreme hardness at high temperature with a lightweight additively manufactured multi-principal element superalloy
Materials are needed that can tolerate increasingly harsh environments, especially ones that retain high strength at extreme temperatures. Higher melting temperature alloys, like those consisting primarily of refractory elements, can greatly increase the efficiency of turbomachinery used in grid electricity production worldwide. Existing alloys, including Ni- and Co-based superalloys, used in components like turbine blades, bearings, and seals, remain a performance limiting factor due to their propensity, despite extensive optimization efforts, for softening and diffusion-driven elongation at temperatures often well above half their melting point. To address this critical materials challenge, we present results from integrating additive manufacturing and alloy design to guide significant improvements in performance via traditionally difficult-to-manufacture refractory alloys. We present an example of a multi-principal element alloy (MPEA), consisting of five refractory elements and aluminum, that exhibited high hardness and specific strength surpassing other known alloys, including superalloys. The alloy shows negligible softening up to 800°C and consists of four compositionally distinct phases, in distinction to previous work on MPEAs. Density functional theory calculations reveal a thermodynamic explanation for the observed temperature-independent hardness and favorability for the formation of this multiplicity of phases.
Electronic and optical properties of plutonium metal and oxides from Reflection Electron Energy Loss Spectroscopy
Reflection Electron Energy Loss Spectra (REELS) have been acquired on plutonium δ-Pu-Ga alloys, α-Pu 2 O 3 and PuO 2 . The position of the peaks attributable to the Pu 6p excitation can be used as ‘fingerprint’ for these species. X-ray Photoelectron Spectroscopy has been used to aid interpretation of the REELS spectra. The band gap of PuO 2 was determined from the onset of the energy loss in the REELS spectrum. The REELS data have been evaluated within the dielectric response theory by means of the QUEELS-ε(k, w)-REELS software. In this work, the Energy Loss Function, dielectric function, refractive index and extinction coefficient for these materials have been determined in the ultraviolet and extreme ultraviolet range and are compared to previously reported results.
Novel waste-derived non-noble metal catalysts to accelerate acidic and alkaline hydrogen evolution reaction
Carbon substrate support is a promising approach to facilitate catalytic activity of non-noble metal catalysts in hydrogen evolution reactions (HER). Here, in this paper, we report a waste-derived electrocatalyst P-CoNiO 2 @NC synthesized under mild condition. It exhibits outstanding HER performance in both acid (η 10 = 64.6 mV) and alkaline (η 10 = 96.4 mV) condition with good stability. Especially, it begins to outperform commercial Pt/C when the current density is over 74 mA/cm 2 in acid electrolyte and 29 mA/cm 2 in alkaline electrolyte. Moreover, the waste-derived carbon substrate could effectively promote HER activity of other dual 3d TMOs such as Fe and Co, Fe and Ni. The improved HER activity originates from electron transfer, electronic structure adjustment and optimization of intermediates adsorption according to X -ray photoelectron spectroscopy and density functional theory calculations. This work provides a new insight for converting wastes into value-added products and a sustainable and friendly strategy to facilitate hydrogen energy production.
Metal oxide-promoted calcium cuprate catalysts for diol oxidative dehydrocyclization to lactones
Here, this work investigates structure-function relationships in electronically tunable, redox-active, basic Cu-Ca mixed metal oxide catalysts for oxidative dehydrocyclization of liquid diols to lactones. Compositional screening identified Ni 2+ and Zn 2+ as effective promoters that increase the surface Cu 2+ population by ∼1.7× and Cu-normalized activity for liquid 1,4-butanediol conversion to γ-butyrolactone by ∼3–4×. In situ Raman spectroscopy, in situ X-ray absorption spectroscopy (XAS), in situ diffuse-reflectance Fourier transform infrared spectroscopy (DRIFTS), ex situ X-ray diffraction (XRD), and H 2 -temperature-programmed reduction (H 2 -TPR) show that Ni 2+ or Zn 2+ incorporation promotes the formation of Ca 0.82 Cu 1.00 O 2 nanoparticles under mild calcination conditions. This cuprate phase features stronger and shorter Cu–O bonds (1.90 Å) than inactive bulk CuO (1.95 Å) and square-planar Cu 2+ O 4 sites with enhanced d z2 electrophilicity, strengthening alkoxy adsorption. Pyridine-DRIFTS confirms the purely basic nature of the catalyst surface, while methanol-DRIFTS indicates Cu 2+ surface enrichment with Ni or Zn promotion, where Cu–O(Ca)–Cu sites can exist as amorphous domains or a truncation layer on crystalline nanoparticles.
Modeling gas breakthrough and flow phenomena through engineered barrier systems using a discrete fracture approach
Compacted clays are being considered to build engineered barrier systems (EBS) intended for the safe isolation of high-level nuclear waste (HLW) and spent nuclear fuel (SNF). The corrosion of the metallic canister containing the HLW/SNF will lead to the generation and buildup of the gas pressure in the more internal part of the clay buffer. This phenomenon would eventually trigger the formation and propagation of fractures in the clay barrier, jeopardizing its safety functions. Here, in this work, we propose to use the fragmentation technique (MFT) to model evolving fractures in clays triggered by gas pressurization. The MFT has been successfully used to model the formation of fractures in concrete, drying cracks in soil, hydraulic and thermo-fractures in rocks. In this work, we extend the MFT to deal with multiphase fluid flow in deformable porous media, and we upgraded a fully coupled computer finite element code using the extended technique. The proposed approach is first verified against analytical solutions and is then applied to model gas breakthrough experiments in clays. A very satisfactory performance of the method is observed in all the analyses, showing the potential of the MFT to tackle multiphase flow problems in deformable porous media with evolving discontinuities.
Carbon dots from surface-capping/passivation of small carbon nanoparticles with nanoscale titanium dioxide
Carbon dots are classically defined as small carbon nanoparticles (CNPs) with effective surface passivation, which has been accomplished predominantly by surface organic functionalization. In the current work, the passivation is achieved by the surface coating of CNPs with nanoscale TiO 2 for CNP/TiO 2 core/shell nanostructures, which are analogous to conventional semiconductor core/shell quantum dots (QDs). The TiO 2 capping of CNPs results in substantial enhancements in the fluorescence quantum yields, also analogous to the similar enhancements famously known for the semiconductor QDs. In conclusion, mechanistic implications of the findings, including the associated further validation on the classical definition of carbon dots, are discussed.
Towards smarter green infrastructure: Fusing bark ecology and stemflow hydrodynamics on tree stems
A wide array of bark surfaces sheath wooded plants in rural and urban areas alike. Much work has examined the function and role of bark in different contexts and different environs, including urban areas, finding that it is rich in life and can play a role in the transfer of water and matter to the ground surface. Accordingly, this paper presents a first step to weld and fuse bark ecology and stemflow hydrodynamics. It is an effort to develop a physically-based understanding of the transport of water and matter (e.g., solutes, particulates, microorganisms) along tree stems using relevant equations to allow a more informed consideration of bark in green infrastructure initiatives. In particular, the hydrodynamical equations are based on the conservation of water mass, conservation of momentum, and conservation of scalar mass. These equations, coupled with contemplation of corticular life, underpin and substantiate bark’s unifying role as a modulator and cultivator. By elucidating the ‘black box’ of the tree stem and utilizing the formulations set forth in this paper, urban foresters and planners can develop green infrastructure to help advance ecosystem services and sustainability development goals (SDG), especially SDG 11 and SDG 15.
Aim18p and Aim46p are chalcone isomerase domain–containing mitochondrial hemoproteins in Saccharomyces cerevisiae
Chalcone isomerases (CHIs) have well-established roles in the biosynthesis of plant flavonoid metabolites. Saccharomyces cerevisiae possesses two predicted CHI-like proteins, Aim18p (encoded by YHR198C) and Aim46p (YHR199C), but it lacks other enzymes of the flavonoid pathway, suggesting that Aim18p and Aim46p employ the CHI fold for distinct purposes. Here, we demonstrate using proteinase K protection assays, sodium carbonate extractions, and crystallography that Aim18p and Aim46p reside on the mitochondrial inner membrane and adopt CHI folds, but they lack select active site residues and possess an extra fungal-specific loop. Consistent with these differences, Aim18p and Aim46p lack CHI activity and also the fatty acid–binding capabilities of other CHI-like proteins, but instead bind heme. We further show that diverse fungal homologs also bind heme and that Aim18p and Aim46p possess structural homology to a bacterial hemoprotein. Collectively, our work reveals a distinct function and cellular localization for two CHI-like proteins, introduces a new variation of a hemoprotein fold, and suggests that ancestral CHI-like proteins were hemoproteins.
Irradiation-enhanced diffusion and diffusion-limited creep in U 3 Si 2
U 3 Si 2 is an advanced fuel candidate due to its relatively high fissile density and attractive thermal properties. Compared to standard UO 2 fuel, there are significant data gaps for the thermophysical and thermomechanical properties of U 3 Si 2 . Point defect concentrations and mobilities under irradiation govern a number of important fuel performance properties, such as creep and fission gas release. In this work, we utilized density functional theory (DFT) data to inform a cluster dynamics framework to predict point defect concentrations in U 3 Si 2 under irradiation. Molecular dynamics (MD) simulations were used to examine the contribution of atomic mixing during ballistic cascades to diffusion, as well as the diffusivity of U and Si at grain boundaries. These atomic scale models for diffusivity were then used to inform a creep model based on bulk (Nabarro-Herring) and grain boundary (Coble) diffusional creep, and climb-limited dislocation creep. The model compares well against available experimental data and has been implemented in the BISON fuel performance code. Finally, a demonstration case using simple power profiles has been carried out, showing that negligible creep occurs due to the low temperatures experienced by U 3 Si 2 in-reactor, a consequence of its high thermal conductivity.
Effects of charged interfaces on electrolyte decomposition at the lithium metal anode
Lithium–Sulfur batteries are promising candidates to substitute conventional Li-ion batteries due to their higher energy density and reduced cost. However, several challenges related to the reactivity of the lithium metal anode have prevented this technology from becoming broadly commercialized. Lithium's high reactive nature leads to the continuous decomposition of the electrolyte and the formation of the solid-electrolyte interphase (SEI) layer. Thus, a comprehensive understanding of how the SEI film is formed is crucial to help elucidate improvements for this battery technology. In this work, we use density functional theory (DFT) based computational methods to investigate the effect of charged interfaces on the electrolyte degradation at the lithium anode. Here, several electrolyte mixtures were considered including 1,2-dimethoxyethane (DME) and 1,3-dioxolane (DOL) as solvents, and lithium bis(fluorosulfonyl)imide (LiFSI) and lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) as salts. It is found that the extent of salt decomposition is higher when the interface is charged. In addition, two types of solvent reduction mechanisms are identified: C–O bond cleavage and radical attacks. Charge transfer and evolution analysis are characterized in detail. Finally, solvent decomposition reactions are found to become more thermodynamically favorable and their activation barriers to be diminished under the effect of constant potentials and electric fields.
Hydrogen trapping and storage in the group IVB-VIB transition metal carbides
The transition metal carbides have been known to act as traps for hydrogen in steels as well as potential materials for hydrogen storage. Despite numerous experimental and a few theoretical studies, what impacts hydrogen trapping and storage in the transition metal carbides is not well understood. In this work, we use density functional theory to systematically investigate the bulk trapping and storage capabilities of the transition metal carbides. We specifically examine how trapping and storage changes with the transition metal, from the group IVB to group VIB, carbon concentration, and structure. Our results demonstrate a strong correlation between the trap energy and the number of valence electrons in the transition metal, suggesting that the group IVB transition metal carbides are the best carbides for trapping and storage. Hydrogen preferentially sits at octahedral interstices, e.g., the carbon vacancies, in the structure except in certain cases near the Me 2 C concentration when tetrahedral interstices, devoid of nearest neighbor carbon, can become more favorable. Our results further demonstrate that the lower the carbon concentration, the more hydrogen the transition metal carbide can store. These results demonstrate which carbides will act as the best traps for hydrogen.
Unveiling X-ray absorption signatures of boron nitride via first-principles simulation and machine learning
Boron nitride (BN) allotropes hold great promise in many advanced applications ranging from optical and photonic devices to energy storage and battery systems to tribological components. The diverse functionalities of this material stem from BN’s highly tunable structural and electronic properties, which are governed by the versatile boron–nitrogen bonding configurations. Exploring the structural landscape of BN can unveil novel structures possessing unique properties suited for specific applications, therefore accelerating the design of next-generation advanced functional materials. In this work, we leverage boron K-edge X-ray absorption spectroscopy (XAS) as an effective probe for local structural features and chemical environments. A total of 210 BN crystal structures are generated via analogies to the extensive array of carbon allotropes, and XAS is simulated for each unique local motif within the resulting collection of structures. A mapping between structural features and spectral signatures was established by synergizing first-principle simulations with data-driven based post-analysis approaches. Specifically, we developed a neural network model that can satisfactorily predict spectra line shapes from local structural descriptors. Toward automatic spectroscopic interpretation of any new BN structures, supervised machine learning models, trained on this structure–spectrum dataset, can accurately infer local coordination environments from simulated XAS, highlighting the strength of this unique approach of combining high-fidelity first-principles simulation and machine-learning to accelerate target design of novel BN materials via rational understanding of local structure-spectrum correlations.
Study of the interaction between $Ξ$ baryons and light mesons via femtoscopy at the LHC
Meson-baryon systems with strangeness content provide a unique laboratory for investigating the strong interaction and testing theoretical models of hadron structure and dynamics. In this work, the measured correlation functions for oppositely charged $Ξ$-K and $Ξ$ − 𝜋 pairs obtained in high-multiplicity pp collisions at $\sqrt{𝑠}$ = 13 TeV at the LHC are presented. For the first time, high-precision data on the $Ξ$-K interaction are delivered at small relative momenta. The scattering lengths, extracted via the Lednický–Lyuboshits expression of the pair wavefunction, indicate a repulsive and a shallow attractive strong interaction for the $Ξ$-K and $Ξ$ − 𝜋 systems, respectively. The $Ξ$(1620) and $Ξ$(1690) states are observed in the $Ξ$ − 𝜋 correlation function and their properties, mass and width, are determined. These measurements are in agreement with other available results. Such high-precision data can help refine the understanding of these resonant states, provide stronger constraints for chirally motivated potentials, and address the key challenge of describing the coupled-channel dynamics that may give rise to molecular configurations .
Scale dependence in functional equivalence and difference in the soil microbiome
Climatic history can shape the functioning of soil microbial communities and thus rates of ecosystem processes such as organic matter decomposition. For example, broad spatial scale differences in climatic history, such as contrasting precipitation regimes, have been shown to generate unique microbial functional responses to contemporary moisture conditions. Yet it is an open question as to whether local differences in soil microclimate similarly influence the functional potential of decomposer communities. Here, we use a multi-scale approach within and among two temperate forest field sites to investigate this question. Soils from fifty-four microsites, that vary in their soil moisture climate-regimes, were used as inocula for a common leaf litter (Quercus rubra L.) in a controlled, laboratory microcosm study. Microcosms were placed under dry, mesic and wet lab-moisture conditions and the rate of carbon (C) mineralization of the litter was measured over 202 days. Our results reveal differences in decomposition rates under controlled conditions that highlight broad-scale functional differences between the soil communities at each site. Specifically, we found that C mineralization differed by as much as two-fold for soil communities when compared between the sites. Our results also show that functional differences of soil communities are observable within one site but not the other. In the site where local-scale functional legacies were apparent, the historical soil moisture microclimate-regimes generated as much as an 89% change in C mineralization rates of the leaf litter under the same contemporary, lab-imposed moisture conditions. Here, a similar pattern was not observable in the other site; instead, laboratory moisture conditions explained almost all variation in C mineralization. Notably, for the site with pronounced local-scale functional legacies, there was much greater within-site variation in field-soil microsite moisture than at the site which did not exhibit functional legacies, suggesting that the extent of local-scale variation in microclimate may act as control on whether local-scale functional legacies are observed. Regardless of whether this mechanism does explain our findings, our observations do confirm those from prior studies where regional-scale moisture-regime differences shape microbial function, and extend this prior work by providing evidence that pronounced local-scale differences in soil moisture microclimate-regimes are associated with microbial functional legacies.
An evaluation of electricity system technology competitiveness metrics: The case for profitability
In this work we develop standardized functional forms for electricity technology competitiveness metrics and show how the mathematical relationship between value and cost can affect the robustness of the metric. Here, we make the case to replace established metrics with economic profitability metrics – including a new profitability-based adjustment to LCOE. These profitability metrics are shown to be more robust for evaluating technology competitiveness by keeping comparisons on an equivalent monetary basis.