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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 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Probing a Silent Metal: A Combined X-ray Absorption and Emission Spectroscopic Study of Biologically Relevant Zinc Complexes

As the second most common transition metal in the human body, Zinc is of great interest in research but has few viable routes for its direct structural study in biological systems. Herein, Zn valence-to-core X-ray emission spectroscopy (VtC XES) and Zn K-edge X-ray absorption spectroscopy (XAS) are presented as a means to understand the local structure of zinc in biological systems through the application of these methods to a series of biologically relevant model complexes. Taken together, the Zn K-edge XAS and VtC XES provide a means to establish the ligand identity, local geometry, and metal-ligand bond lengths. Experimental results were supported by strongly correlated density-functional-theory-based calculations, which, coupled with the complementarity of the two spectroscopic approaches, further enables future applications to protein systems to be examined in a predictive fashion. In this regard, test calculations were performed to showcase the sensitivity of these techniques to realistic biological applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Atomic-Scale STEM Analysis Shows Structural Changes of Au–Pd Nanoparticles in Various Gaseous Environments

We report dynamical restructuring effects in free-standing Au 0.75 Pd 0.25 nanoparticles occurring in gaseous environments at elevated temperatures. The freshly prepared sample was found to have a core–shell structure with a Pd-rich phase on the surface. The evolution of sample composition and morphology under exposure to 1 bar of pure gases, namely O 2 , H 2 , air, CO, and CO 2 , at different temperatures was studied using in situ scanning transmission electron microscopy (STEM) and energy-dispersive X-ray spectroscopy (EDS). We observed sharper facets on the surface of the particles under O 2 or air at 400 °C. Small islands of Pd were present on the surface, although some Pd was redistributed inside the bulk when the temperature was increased under O 2 . Subtle changes in surface roughness were noted when O 2 was substituted with H 2 at 400 °C, an observation correlated to density functional theory (DFT) calculations. The particles lost clean surface facets when CO was introduced at room temperature and at 200 °C. No substantial changes could be observed after exposure to CO 2 at 250 °C. The adsorption of CO molecules on the surface modifies the surface of the particles and decreases the facet prevalence. Furthermore, these in situ observations show how gases can induce subtle modification of the surface of nanocatalysts, potentially impacting their chemical properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Using Light to Enhance Charge Transfer in Battery Materials: Increasing Charging Rates in LiNi 0.5 Mn 1.5 O 4

Electrochemical, spectroscopic, and computational studies on the interplay between light perturbation and coupled electron and Li-ion transfer in LiNi 0.5 Mn 1.5 O 4 (LNMO) cathodes identify that photon perturbation moves the system out of equilibrium, increases entropy, lowers the impedance of the battery, and increases the charging capacity by at least 15% during fast charging by promoting the oxidation of Ni 3+ to Ni 4+ . Here, the correlation with density functional theory calculations points out that oxidation of Ni 3+ faces a 30% higher barrier compared with the oxidation of Ni 2+ and hence exhibits greater responses to the energy transferred from photons. Structural analysis elucidates that photon energy also assists the transition of LNMO from the Jahn−Teller distorted asymmetric lattice with Ni 3+ to the nondistorted Ni 4+ . Synergistically, photons energize the removal of electrons from Ni 3+ and create photoinduced Ni 4+ intermediates, followed by the removal of Li + with almost two times faster diffusion rates.

5 V spinel↗
DOE OSTI2023

Kβ X-ray Emission Spectroscopy of Cu(I)-Lytic Polysaccharide Monooxygenase: Direct Observation of the Frontier Molecular Orbital for H 2 O 2 Activation

Lytic polysaccharide monooxygenases (LPMOs) catalyze the degradation of recalcitrant carbohydrate polysaccharide substrates. These enzymes are characterized by a mononuclear Cu(I) active site with a three-coordinate T-shaped “His-brace” configuration including the N-terminal histidine and its amine group as ligands. Here, this study explicitly investigates the electronic structure of the d 10 Cu(I) active site in a LPMO using Kβ X-ray emission spectroscopy (XES). The lack of inversion symmetry in the His-brace site enables the 3d/p mixing required for intensity in the Kβ valence-to-core (VtC) XES spectrum of Cu(I)-LPMO. These Kβ XES data are correlated to density functional theory (DFT) calculations to define the bonding, and in particular, the frontier molecular orbital (FMO) of the Cu(I) site. These experimentally validated DFT calculations are used to evaluate the reaction coordinate for homolytic cleavage of the H 2 O 2 O–O bond and understand the contribution of this FMO to the low barrier of this reaction and how the geometric and electronic structure of the Cu(I)-LPMO site is activated for rapid reactivity with H 2 O 2 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Genome-resolved metagenomics reveals role of iron metabolism in drought-induced rhizosphere microbiome dynamics

Recent studies have demonstrated that drought leads to dramatic, highly conserved shifts in the root microbiome. At present, the molecular mechanisms underlying these responses remain largely uncharacterized. Here we employ genome-resolved metagenomics and comparative genomics to demonstrate that carbohydrate and secondary metabolite transport functionalities are overrepresented within drought-enriched taxa. These data also reveal that bacterial iron transport and metabolism functionality is highly correlated with drought enrichment. Using time-series root RNA-Seq data, we demonstrate that iron homeostasis within the root is impacted by drought stress, and that loss of a plant phytosiderophore iron transporter impacts microbial community composition, leading to significant increases in the drought-enriched lineage, Actinobacteria. Finally, we show that exogenous application of iron disrupts the drought-induced enrichment of Actinobacteria, as well as their improvement in host phenotype during drought stress. Collectively, our findings implicate iron metabolism in the root microbiome’s response to drought and may inform efforts to improve plant drought tolerance to increase food security.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Deep generative learning of magnetic frustration in artificial spin ice from magnetic force microscopy images

Increasingly large datasets of microscopic images with nanoscale resolution facilitate the development of machine learning methods to identify and analyze subtle physical phenomena embedded within the images. In this work, microscopic images of honeycomb lattice spin-ice samples serve as datasets from which we automate the calculation of net magnetic moments and directional orientations of spin-ice configurations. In the first stage of our workflow, machine learning models are trained to accurately predict magnetic moments and directions within spin-ice structures. Variational Autoencoders (VAEs), an emergent unsupervised deep learning technique, are employed to generate high-quality synthetic magnetic force microscopy (MFM) images and extract latent feature representations, thereby reducing experimental and segmentation errors. The second stage of proposed methodology enables precise identification and prediction of frustrated vertices and nanomagnetic segments, effectively correlating structural and functional aspects of microscopic images. This facilitates the design of optimized spin-ice configurations with controlled frustration patterns, enabling potential on-demand synthesis.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Tunable metal hydroxide–organic frameworks for catalysing oxygen evolution

The oxygen evolution reaction is central to making chemicals and energy carriers using electrons. Combining the great tunability of enzymatic systems with known oxide-based catalysts can create breakthrough opportunities to achieve both high activity and stability. Here we report a series of metal hydroxide–organic frameworks (MHOFs) synthesized by transforming layered hydroxides into two-dimensional sheets crosslinked using aromatic carboxylate linkers. MHOFs act as a tunable catalytic platform for the oxygen evolution reaction, where the π–π interactions between adjacent stacked linkers dictate stability, while the nature of transition metals in the hydroxides modulates catalytic activity. Substituting Ni-based MHOFs with acidic cations or electron-withdrawing linkers enhances oxygen evolution reaction activity by over three orders of magnitude per metal site, with Fe substitution achieving a mass activity of 80A $g^{-1}_{catalyst}$ 1 at 0.3 V overpotential for 20 h. Density functional theory calculations correlate the enhanced oxygen evolution reaction activity with the MHOF-based modulation of Ni redox and the optimized binding of oxygenated intermediates.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Structure of iso-density sets in supersonic isothermal turbulence

Context: The gas density structure of the cold molecular phase of the interstellar medium is the main controller of star formation. Aims: A theoretical framework is proposed to describe the structural content of the density field in isothermal supersonic turbulence. Methods: It makes use of correlation and structure functions of the phase indicator field defined for different iso-density values. The relations between these two-point statistics and the geometrical features of iso-density sets such as the volume fraction, the surface density, the curvature, and fractal characteristics are provided. An exact scale-by-scale budget equation is further derived revealing the role of the turbulent cascade and dilation on the structural evolution of the density field. Although applicable to many flow situations, this tool is here first invoked for characterising supersonic isothermal turbulence, using data from the currently best-resolved numerical simulation. Results: We show that iso-density sets are surface fractals rather than mass fractals, with dimensions that markedly differ between dilute, neutral, and dense regions. The surface–size relation is established for different iso-density values. We further find that the turbulent cascade of iso-density sets is directed from large towards smaller scales, in agreement with the classical picture that turbulence acts to concentrate more surface into smaller volumes. Intriguingly, there is no range of scales that complies with a constant transfer rate in the cascade, challenging our fundamental understanding of interstellar turbulence. Finally, we recast the virial theorem in a new formulation drawing an explicit relation between the aforementioned geometrical measures and the dynamics of iso-density sets.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Extension of frozen natural orbital approximation to open-shell references: Theory, implementation, and application to single-molecule magnets

Natural orbitals are often used to achieve a more compact representation of correlated wave-functions. Using natural orbitals computed as eigenstates of the virtual–virtual block of the state density matrix instead of the canonical Hartree–Fock orbitals results in smaller errors when the same fraction of virtual space is frozen. This strategy, termed frozen natural orbital (FNO) approach, is effective in reducing the cost of regular coupled-cluster (CC) calculations and some multistate methods, such as EOM-IP-CC (equation-of-motion CC for ionization potentials). This contribution extends the FNO approach to the EOM-SF-CC ansatz (EOM-CC with spin-flip). In contrast to EOM-IP-CCSD, EOM-SF-CCSD relies on high-spin open-shell references. Using FNOs computed for an open-shell reference leads to an erratic behavior of the EOM-SF-CC energies and properties due to an inconsistent truncation of the α and β orbital spaces. A general solution to problems arising in the EOM-CC calculations utilizing open-shell references, termed OSFNO (open-shell FNO), is proposed. Furthermore, by means of singular value decomposition (SVD) of the overlap matrix of the α and β orbitals, the OSFNO algorithm identifies the corresponding orbitals and determines virtual orbitals corresponding to the singly occupied space. This is followed by SVD of the singlet part of the state density matrix in the remaining virtual orbital subspace. The so-computed FNOs preserve the spin purity of the open-shell orbital subspace to the extent allowed by the original reference, thus facilitating a safe truncation of the virtual space. The performance of OSFNO is benchmarked for selected diradicals and triradicals.

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

Bond length alternation of π -conjugated polymers predicted by the Fermi–Löwdin orbital self-interaction correction method

π-conjugated polymers have been used in a wide range of practical applications, partly due to their unique properties that originate in the delocalization of electrons through the polymer backbone. The level of delocalization can be characterized by the induced bond length alternation (BLA), with shorter BLA connected with strong delocalization and vice versa. The accurate description of this structural parameter can be considered a benchmark for testing the capability of different electronic structure methods for self-interaction error (SIE) removal and electron correlation inclusion. Density functional theory (DFT), in its local or semi-local flavors, suffers from SIE and, thus, underestimates the BLA compared to self-interaction-free methods. In this work, we utilize the Fermi–Löwdin orbital self-interaction correction (FLOSIC) method for one-electron self-interaction removal to characterize the BLA of five oligomers with increasing length extrapolated to the polymeric limit. We compare the self-interaction-free BLA to several DFT approximations, Møller–Plesset second-order perturbation theory (MP2), and the BLA obtained with the domain based local pair natural orbital CCSD(T) [DLPNO-CCSD(T)] approximation. Our findings show that FLOSIC corrects for the small BLA given by (semi-)local DFT approximations, but it tends to overcorrect with respect to CAM-B3LYP, MP2, and DLPNO-CCSD(T).

Chemistry↗
DOE OSTI2024

Data Science Shows that Entropy Correlates with Accelerated Zeolite Crystallization in Monte Carlo Simulations

We have performed a data science study of Monte Carlo simulation trajectories to understand factors that can accelerate formation of zeolite nanoporous crystals, a process that can take days or even weeks. In previous work, Monte Carlo simulations predicted and experiments confirmed that using a secondary organic structure-directing agent (OSDA) accelerates crystallization of all-silica LTA zeolite, with experiments finding a three-fold speedup [PCCP 24, 142-148 (2022)]. However, it remains unclear what physical factors cause the speed-up. Here, we apply data science to analyze the simulation trajectories to discover what drives accelerated zeolite crystallization in Monte Carlo going from a one-OSDA synthesis (1OSDA) to a two-OSDA version (2OSDA). We encoded simulation snapshots using the Smooth Overlap of Atomic Positions approach, which represents all 2- and 3-body correlations within a given cutoff distance. Principal component analyses failed to discriminate datasets of structures from 1OSDA and 2OSDA simulations, while the Support Vector Machine (SVM) approach succeeded at classifying such structures with an area-under-curve (AUC) score of 0.99 (where AUC = 1 is a perfect classification) with all 3-body correlations, and as high as 0.94 with only 2-body correlations. SVM decision functions reveal relatively broad / narrow histograms for 1OSDA / 2OSDA datasets, suggesting that the two simulations differ strongly in information heterogeneity. Informed by these results, we performed pair (2-body) entropy calculations during crystallization, resulting in entropy differences that semi-quantitatively account for the speedup observed in the previous Monte Carlo simulations. We conclude that altering synthesis conditions in ways that substantially changes the entropy of labile silica networks may accelerate zeolite crystallization, and we discuss possible approaches for achieving such acceleration.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

The primordial matter power spectrum on sub-galactic scales

The primordial matter power spectrum quantifies fluctuations in the distribution of dark matter immediately following inflation. Over cosmic time, overdense regions of the primordial density field grow and collapse into dark matter haloes, whose abundance and density profiles retain memory of the initial conditions. By analysing the image magnifications in 11 strongly lensed and quadruply imaged quasars, we infer the abundance and concentrations of low-mass haloes, and cast the measurement in terms of the amplitude of the primordial matter power spectrum. We anchor the power spectrum on large scales, isolating the effect of small-scale deviations from the Lambda cold dark matter (ΛCDM) prediction. Assuming an analytic model for the power spectrum and accounting for several sources of potential systematic uncertainty, including three different models for the halo mass function, we obtain correlated inferences of $\log _{10}\left(P / P_{\Lambda \rm {CDM}}\right)$, the power spectrum amplitude relative to the predictions of the concordance cosmological model, of $0.0_{-0.4}^{+0.5}$, $0.1_{-0.6}^{+0.7}$, and $0.2_{-0.9}^{+1.0}$ at k = 10, 25, and 50 $\rm {Mpc^{-1}}$ at $68 {{\ \rm per\ cent}}$ confidence, consistent with CDM and single-field slow-roll inflation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Cryogenic growth of aluminum: Structural morphology, optical properties, superconductivity, and microwave dielectric loss

We explore the molecular beam epitaxy synthesis of superconducting aluminum thin films grown on c-plane sapphire substrates at cryogenic temperatures of 6 K and compare their behavior with films synthesized at room temperature. We demonstrate that cryogenic growth increases structural disorder, producing crystalline grains that modify the optical, electrical, and superconducting properties of aluminum. We observe that cryogenic deposition changes the color of aluminum from fully reflective to yellow and correlate the pseudodielectric function and reflectance with structural changes in the film. We find that smaller grain sizes enhance the superconductivity of aluminum, increasing its critical temperature and critical field. We then estimate the superconducting gap and coherence length of Cooper pairs in aluminum in the presence of disorder. In conclusion, we fabricate superconducting microwave resonators on these films and find that, independently of the growth temperature, the system is dominated by two-level system loss with similar quality factors in the high- and low-power regimes. We further measure a higher kinetic inductance in the cryogenically grown films.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Coherent optical imaging of moving objects hidden in a heavily scattering random medium

Experimental results are presented that provide insight into the physics of statistical imaging in heavily scattering random media based on measured speckle correlations as a function of the change in position of a moving object. In this way, definitive interpretation of a rather complex and earlier theory is achieved, making this work an experimental complement to that theory paper []. Motion could be natural, where the set of positions is estimated or separately obtained, or directed, where a mechanical stage can be used to adjust the object's position. In the experiment, a coherent laser illuminates two scattering diffusers, while an object is translated between them in the resulting speckled field and images are collected in a transmission configuration. Results are shown for various objects of differing size and geometry, allowing the theory to be validated and interpreted with new understanding. This work demonstrates imaging opportunities, and applications include material characterization, environmental imaging and sensing, and deep-tissue imaging. Published by the American Physical Society 2025

Hastings, Ryan L. (ORCID:0009000095977807)↗
DOE OSTI2020

Calculating fluctuations and self-correlations numerically for causal charge diffusion in relativistic heavy-ion collisions

Here, we study the propagation and diffusion of electric charge fluctuations in the Bjorken hydrodynamic model with both white and Catteneo noise using purely numerical methods. We show that a lattice of noise fluctuations is required to fully calculate the two-point correlators of charge. We introduce a numerical procedure to solve the stochastic differential equations that arise from the charge conservation equation on the lattice event by event. We explicitly identify the self-correlation term in the case of Catteneo noise and provide a physical interpretation. We provide a numerical recipe to remove this contribution from the full two-point correlators. Finally, we calculate the balance functions for charged hadrons. By limiting the speed of signal propagation, we observe the expected narrowing of the balance functions after removing the self-correlations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Assessing spin-density wave formation in La 3 Ni 2 O 7 from electronic structure calculations

Here, we employ correlated density-functional theory methods (DFT + Hubbard U) to investigate the spin-density wave state of the bilayer Ruddlesden-Popper (RP) nickelate La 3 Ni 2 O 7 which becomes superconducting under pressure. We predict that the ground state of this bilayer RP material has traits of both the double spin-stripe and the single spin-charge stripe phases proposed in the literature as it corresponds to in-plane up/up′/down/down′ diagonal stripes with up/down being high spin (formally Ni 2+ : d 8 ), and up′ /down′ being low spin (formally Ni 3+ : d 7 ). The main feature of this solution (that is insulating even at U = 0) is the dominant role of the d x 2 −y 2 bands around the Fermi level, which would become doped with the introduction of electrons via oxygen vacancies. In spite of the similarity with cuprates in terms of the dominant role of d x 2 −y 2 bands, some differences are apparent in the magnetic ground state of La 3 Ni 2 O 7 : the antiferromagnetic out-of-plane coupling within the bilayer (linked to the d z 2 orbitals forming a spin-singlet-like configuration) is found to be the dominant one while in-plane interactions are reduced due to the stripe order of the ground state. With pressure, this striped magnetic ground state remains similar in nature but the increase in bandwidth quickly transitions La 3 Ni 2 O 7 into a metallic state with all the activity close to the Fermi level involving, to a large extent, d x 2 −y 2 orbitals. This is reminiscent of the cuprates and may provide key insights into how superconductivity arises in this material under pressure.

LaBollita, Harrison↗
DOE OSTI2022

Ultrafast electron diffraction: Visualizing dynamic states of matter

Since the discovery of electron-wave duality, electron scattering instrumentation has developed into a powerful array of techniques for revealing the atomic structure of matter. Beyond detecting local lattice variations in equilibrium structures with the highest possible spatial resolution, recent research efforts have been directed toward the long-sought-after dream of visualizing the dynamic evolution of matter in real time. The atomic behavior at ultrafast timescales carries critical information on phase transition and chemical reaction dynamics, the coupling of electronic and nuclear degrees of freedom in materials and molecules, and the correlation among structure, function, and previously hidden metastable or nonequilibrium states of matter. Ultrafast electron pulses play an essential role in this scientific endeavor, and their generation has been facilitated by rapid technical advances in both ultrafast laser and particle accelerator technologies. Here this review presents a summary of the noteworthy developments in this field in the last few decades. The physics and technology of ultrafast electron beams is presented with an emphasis on the figures of merit most relevant for ultrafast electron diffraction experiments. Recent developments in the generation, manipulation, and characterization of ultrashort electron beams aimed at improving the combined spatiotemporal resolution of these measurements are discussed. The fundamentals of electron scattering from atomic matter and the theoretical frameworks for retrieving dynamic structural information from solid-state and gas-phase samples is described. Essential experimental techniques and several landmark works that have applied these approaches are also highlighted to demonstrate the widening applicability of these methods. Ultrafast electron probes with ever-improving capabilities, combined with other complementary photon-based or spectroscopic approaches, hold tremendous potential for revolutionizing our ability to observe and understand energy and matter at atomic scales.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Weakly Supervised Event Classification Using Imperfect Real-world PMU Data with Scarce Labels

This paper studies event classification using imperfect real-world phasor measurement unit (PMU) data with scarce event types (labels). By investigating the real-world PMU data, it is observed that most real-world PMU data's event type is unknown, which makes it challenging to directly use such dataset to build event classifiers as existing classification techniques require high-quality training data with known event type (i.e., label). To address this challenge, a weakly supervised learning based event classification approach is developed, which can use noisy and low-quality PMU data for the training. First, data quality issues are fixed using data preprocessing techniques and then event features are constructed from the PMU data. Using these features, a series of labeling functions are learnt to generate initial estimates of the labels of large amounts of unlabeled PMU data. As the labeling functions are learnt using the same data with scarce labels, the label estimates from the labeling functions can be correlated, noisy, and bias. To enhance these initial estimates, a generative model is developed to characterize the dependencies among the estimated labels, based on which better labels are obtained for training event classifiers. Numerical experiments using the real-world dataset from the Western Interconnection of the U.S. power transmission grid show that the proposed weakly supervised event classifier trained using the dataset with only 5% labeled data can achieve 78.4% classification accuracy.

Liu, Yunchuan↗