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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 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Low-iridium stabilized ruthenium oxide anode catalyst for durable proton-exchange membrane water electrolysis

While mixing iridium (Ir) with ruthenium oxide (RuO 2 ) has proven to be an effective strategy for reducing Ir loading in anode catalysts for proton-exchange membrane (PEM) water electrolysers, achieving industrially relevant long-term stability typically requires an Ir-rich, Ru-lean combination. Here, in this study, by combining density functional theory with Metropolis Monte Carlo methods, we discovered that sufficient stabilization in the RuO 2 lattice could be achieved with less than 50 at.% of Ir, and that Ir in the first subsurface layer plays a critical role. By effectively dispersing Ir dopants within the RuO 2 lattice, we demonstrated an Ir:Ru atomic ratio of only 1:6 that exhibited exceptional stability for over 1,500 h of continuous water electrolysis at 2 A cm −2 . Our Ru 6 IrO x catalyst has the potential to reduce Ir loading by 80% compared with current commercial PEM water electrolysers, and its stability was further validated under industrial testing conditions in a 25-cm 2 PEM electrolyser.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Atomic-level structural correlations across the morphotropic phase boundary of a ferroelectric solid solution: xBiMg 1/2 Ti 1/2 O 3 -(1$-$x)PbTiO 3

Revelation of unequivocal structural information at the atomic level for complex systems is uniquely important for deeper and generic understanding of the structure property connections and a key challenge in materials science. Here in this paper we report an experimental study of the local structure by applying total elastic scattering and Raman scattering analyses to an important non-relaxor ferroelectric solid solution exhibiting the so-called composition-induced morphotropic phase boundary (MPB), where concomitant enhancement of physical properties have been detected. The powerful combination of static and dynamic structural probes enabled us to derive direct correspondence between the atomic-level structural correlations and reported properties. The atomic pair distribution functions obtained from the neutron total scattering experiments were analysed through big-box atom-modelling implementing reverse Monte Carlo method, from which distributions of magnitudes and directions of off-centred cationic displacements were extracted. We found that an enhanced randomness of the displacement-directions for all ferroelectrically active cations combined with a strong dynamical coupling between the A- and B-site cations of the perovskite structure, can explain the abrupt amplification of piezoelectric response of the system near MPB. Finally, altogether this provides a more fundamental basis in inferring structure-property connections in similar systems including important implications in designing novel and bespoke materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Neuromorphic scaling advantages for energy-efficient random walk computations

Neuromorphic computing, which aims to replicate the computational structure and architecture of the brain in synthetic hardware, has typically focused on artificial intelligence applications. What is less explored is whether such brain-inspired hardware can provide value beyond cognitive tasks. Here we show that the high degree of parallelism and configurability of spiking neuromorphic architectures makes them well suited to implement random walks via discrete-time Markov chains. Overall, these random walks are useful in Monte Carlo methods, which represent a fundamental computational tool for solving a wide range of numerical computing tasks. Using IBM’s TrueNorth and Intel’s Loihi neuromorphic computing platforms, we show that our neuromorphic computing algorithm for generating random walk approximations of diffusion offers advantages in energy-efficient computation compared with conventional approaches. We also show that our neuromorphic computing algorithm can be extended to more sophisticated jump-diffusion processes that are useful in a range of applications, including financial economics, particle physics and machine learning.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Neural-network quantum states for ultra-cold Fermi gases

Abstract Ultra-cold Fermi gases exhibit a rich array of quantum mechanical properties, including the transition from a fermionic superfluid Bardeen-Cooper-Schrieffer (BCS) state to a bosonic superfluid Bose-Einstein condensate (BEC). While these properties can be precisely probed experimentally, accurately describing them poses significant theoretical challenges due to strong pairing correlations and the non-perturbative nature of particle interactions. In this work, we introduce a Pfaffian-Jastrow neural-network quantum state featuring a message-passing architecture to efficiently capture pairing and backflow correlations. We benchmark our approach on existing Slater-Jastrow frameworks and state-of-the-art diffusion Monte Carlo methods, demonstrating a performance advantage and the scalability of our scheme. We show that transfer learning stabilizes the training process in the presence of strong, short-ranged interactions, and allows for an effective exploration of the BCS-BEC crossover region. Our findings highlight the potential of neural-network quantum states as a promising strategy for investigating ultra-cold Fermi gases.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2014

Probabilistic evaluation of shallow groundwater resources at a hypothetical carbon sequestration site

Carbon sequestration in geologic reservoirs is an important approach for mitigating greenhouse gases emissions to the atmosphere. This study first develops an integrated Monte Carlo method for simulating CO 2 and brine leakage from carbon sequestration and subsequent geochemical interactions in shallow aquifers. Then, we estimate probability distributions of five risk proxies related to the likelihood and volume of changes in pH, total dissolved solids, and trace concentrations of lead, arsenic, and cadmium for two possible consequence thresholds. The results indicate that shallow groundwater resources may degrade locally around leakage points by reduced pH and increased total dissolved solids (TDS). The volumes of pH and TDS plumes are most sensitive to aquifer porosity, permeability, and CO 2 and brine leakage rates. The estimated plume size of pH change is the largest, while that of cadmium is the smallest among the risk proxies. Plume volume distributions of arsenic and lead are similar to those of TDS. The scientific results from this study provide substantial insight for understanding risks of deep fluids leaking into shallow aquifers, determining the area of review, and designing monitoring networks at carbon sequestration sites.

58 GEOSCIENCES↗
DOE OSTI2016

Unraveling nonadiabatic ionization and Coulomb potential effect in strong-field photoelectron holography

Strong field photoelectron holography has been proposed as a means for interrogating the spatial and temporal information of electrons and ions in a dynamic system. After ionization, part of the electron wave packet may directly go to the detector (the reference wave), while another part may be driven back and scatters off the ion(the signal wave). The interference hologram of the two waves may be used to extract target information embedded in the collision process . Unlike conventional optical holography, however, propagation of the electron wave packet is affected by the Coulomb potential as well as by the laser field. In addition, electrons are emitted over the whole laser pulse duration, thus multiple interferences may occur. In this work, we used a generalized quantum-trajectory Monte Carlo method to investigate the effect of Coulomb potential and the nonadiabatic subcycle ionization on the photoelectron hologram. Here, we showed that photoelectron hologram can be well described only when the effect of nonadiabatic ionization is accounted for, and Coulomb potential can be neglected only in the tunnel ionization regime. Our results help paving the way for establishing photoelectron holography for probing spatial and dynamic properties of atoms and molecules.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2016

Quantum Monte Carlo analysis of a charge ordered insulating antiferromagnet: The Ti 4 O 7 Magneli phase

The Magneli phase Ti 4 O 7 is an important transition metal oxide with a wide range of applications because of its interplay between charge, spin, and lattice degrees of freedom. At low temperatures, it has non-trivial magnetic states very close in energy, driven by electronic exchange and correlation interactions. We have examined three low- lying states, one ferromagnetic and two antiferromagnetic, and calculated their energies as well as Ti spin moment distributions using highly accurate Quantum Monte Carlo methods. We compare our results to those obtained from density functional theory- based methods that include approximate corrections for exchange and correlation. Our results confirm the nature of the states and their ordering in energy, as compared with density-functional theory methods. However, the energy differences and spin distributions differ. Here, a detailed analysis suggests that non-local exchange-correlation functionals, in addition to other approximations such as LDA+U to account for correlations, are needed to simultaneously obtain better estimates for spin moments, distributions, energy differences and energy gaps.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Self-assembly of trimer colloids: effect of shape and interaction range

Trimers with one attractive bead and two repulsive beads, similar to recently synthesized trimer patchy colloids, were simulated with flat-histogram Monte Carlo methods to obtain the stable self-assembled structures for different shapes and interaction potentials. Extended corresponding states principle was then successfully applied to self-assembling systems in order to approximately collapse the results for models with the same shape, but different interaction range. This helps us directly compare simulation results with previous experiment, and good agreement was found between the two. In addition, a variety of self-assembled structures were observed by varying the trimer geometry, including spherical clusters, elongated clusters, monolayers, and spherical shells. In conclusion, our results help to compare simulations and experiments, via extended corresponding states, and we predict the formation of self-assembled structures for trimer shapes that have not been experimentally synthesized.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Cuboidal Liquid Crystal Phases Under Multiaxial Geometrical Frustration.

Cuboidal liquid crystal phases - the so-called blue phases - consist of a network of topological defects arranged into a cubic symmetry. They exhibit striking optical properties, including Bragg reflection in the visible range and fast response times. Confining surfaces can interfere with the packing of such a network, leading to structures that have not been explored before. In this work, a Landau-de Gennes free energy formalism for the tensor alignment field Q is used to investigate the behavior of chiral liquid crystals under non-isotropic confinement. The underlying free energy functional is solved by relying on a Monte Carlo method that facilitates efficient exploration of configuration space. The results of simulations are expressed in terms of phase diagrams as a function of chirality and temperature for three families of spheroids: oblate, spherical, and prolate. Upon deformation, blue phases adapt and transform to accommodate the geometrical constraints, thereby resulting in a wider range of thermal stability. For oblate spheroids, confinement interferes with the development of a full blue phase structure, resulting on a combination of half skyrmions. For prolate spheroids, the blue phases are hybridized and exhibit features of blue phases I and II. More generally, it is shown that mechanical deformation provides an effective means to control, manipulate and stabilize blue phases and cholesterics confined in tactoids.

Palacio-Betancur, Viviana↗
DOE OSTI2020

Surface diffusion manifestation in electrodeposition of metal anodes

Metal anode-based battery systems have been deemed indispensable towards energy storage renaissance engendering extensive research into strategies countering dendritic growth of metal electrodeposition. Fundamentally, the morphological evolution of a material is uniquely characterized by the heights of its self-diffusion barrier across multiple pathways. Herein, based on a coarse-grained kinetic Monte Carlo method, we derive insights into the nucleation and growth of metallic electrodeposits in liquid electrolytes, governed by surface self-diffusion characteristics cognizant of the diverse diffusion routes including terrace, away from step and interlayer pathways. We deconvolve the roles played by each of these surface diffusion mechanisms in conjunction with the electrochemical reaction rate on the deposition morphology regime (film vs. mossy vs. fractal). We identify interlayer diffusion as the predominant morphology-determining mechanism; dendrite-free deposition even at moderate current rates constrains this diffusion barrier to an upper limit. Additionally, we highlight subtle features amidst the realm of the morphological growth assortment that connect to the cell's electrochemical performance. Finally, we delineate morphological features of Li, Na, Mg and Al based on their respective surface diffusion barriers and applied overpotentials, and provide a baseline for the interpretation of experimental observations. This fundamental study sheds light on the mesoscale underpinnings of morphological variances in mono-valent and multi-valent metal electrodeposition.

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

Automating Bayesian inference and design to quantify acoustic particle levitation

Self-propulsion of micro- and nanoparticles powered by ultrasound provides an attractive strategy for the remote manipulation of colloidal matter using biocompatible energy inputs. Quantitative understanding of particle motion and its dependence on size, shape, and composition requires accurate characterization of the acoustic field, which depends sensitively on the experimental setup. Here, we show how automated experiments based on Bayesian inference and design can accurately and efficiently characterize the acoustic field within resonant chambers used to propel acoustic nanomotors. Repeated cycles of observation, inference, and design (OID) are guided by a physical model that describes the rate at which levitating particles approach the nodal plane. Using video microscopy, we observe the relaxation of tracer particles to this plane following the application of the acoustic field. We use sequential Monte Carlo methods to infer model parameters such as the amplitude and frequency of the resonant chamber while accounting for particle-level measurement noise and population-level heterogeneity in the field. Guided by simulated outcomes, we select the optimal design for the next experiment as to maximize the information gain in the relevant parameters. We show how this iterative process serves to discriminate between competing hypotheses and efficiently converges to accurate parameter estimates using only few automated experiments. We discuss the need for model criticism to ensure the validity of the guiding model throughout automated cycles of observation, inference, and design. Furthermore, this work demonstrates how Bayesian methods can learn the parameters of nonlinear, hierarchical models used to describe video microscopy data of active colloids.

42 ENGINEERING↗
DOE OSTI2017

Optimal investment strategies for distributed generation in distribution networks with real option analysis

Efficient and well‐timed investment in distributed generation (DG) in distribution networks in the electricity market environment presents a big challenge for distribution companies. In this study, a real option valuation framework is proposed to determine the optimal investment strategies for DG including the investment location, size, and timing. Within the proposed framework, the profit from investing in DG is modelled, where the benefits include the operation cost savings and capacity update deferral benefit compared with a no‐DG‐investment scenario over the study period. Future power demands and electricity prices are modelled as stochastic variables. The candidate DG investment plans are considered as multiple mutually exclusive options, and the corresponding managerial flexibility for seizing opportunities and mitigating risk of loss upon an unfavourable unfolding of future uncertainties is assessed with real option analysis using the extended least square Monte Carlo method. The distribution of future investment strategies and optimal initial investment threshold levels under various scenarios are analysed in a case study to demonstrate the characteristics of the proposed framework and methodology.

Zou, Bo↗
DOE OSTI2023

VarIabiLity seLection of AstrophysIcal sources iN PTF (VILLAIN): I. Structure function fits to 71 million objects

Light-curve variability is well-suited to characterizing objects in surveys with high cadence and a long baseline. This is especially relevant in view of the large datasets to be produced by the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). We aim to determine variability parameters for objects in the Palomar Transient Factory (PTF) and explore differences between quasars (QSOs), stars, and galaxies. We relate variability and colour information in preparation for future surveys. We fit joint likelihoods to structure functions (SFs) of 71 million PTF light curves with a Markov chain Monte Carlo method. For each object, we assume a power-law SF and extract two parameters: the amplitude on timescales of one year, A, and a power-law index, γ. With these parameters and colours in the optical (Pan-STARRS1) and mid-infrared (WISE), we identify regions of parameter space dominated by different types of spectroscopically confirmed objects from SDSS. Candidate QSOs, stars, and galaxies are selected to show their parameter distributions. QSOs show high-amplitude variations in the R band, and the highest γ values. Galaxies have a broader range of amplitudes and their variability shows relatively little dependency on timescale. With variability and colours, we achieve a photometric selection purity of 99.3% for QSOs. Even though hard cuts in monochromatic variability alone are not as effective as seven-band magnitude cuts, variability is useful in characterizing object subclasses. Through variability, we also find QSOs that were erroneously classified as stars in the SDSS. We discuss perspectives and computational solutions in view of the upcoming LSST.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Thermonuclear 28 P(p, γ ) 29 S reaction rate and astrophysical implication in ONe nova explosion

An accurate 28 P(p, γ) 29 S reaction rate is crucial to defining the nucleosynthesis products of explosive hydrogen burning in ONe novae. Using the recently released nuclear mass of 29 S, together with a shell model and a direct capture calculation, we reanalyzed the 28 P(p, γ) 29 S thermonuclear reaction rate and its astrophysical implication. We focus on improving the astrophysical rate for 28 P(p, γ) 29 S based on the newest nuclear mass data. Our goal is to explore the impact of the new rate and associated uncertainties on the nova nucleosynthesis. We evaluated this reaction rate via the sum of the isolated resonance contribution instead of the previously used Hauser-Feshbach statistical model. The corresponding rate uncertainty at different energies was derived using a Monte Carlo method. Nova nucleosynthesis is computed with the 1D hydrodynamic code SHIVA. The contribution from the capture on the first excited state at 105.64 keV in 28 P is taken into account for the first time. We find that the capture rate on the first excited state in 28 P is up to more than 12 times larger than the ground-state capture rate in the temperature region of 2.5 × 10 7 K to 4 × 10 8 K, resulting in the total 28 P(p, γ) 29 S reaction rate being enhanced by a factor of up to 1.4 at ~1 × 10 9 K. In addition, the rate uncertainty has been quantified for the first time. It is found that the new rate is smaller than the previous statistical model rates, but it still agrees with them within uncertainties for nova temperatures. The statistical model appears to be roughly valid for the rate estimation of this reaction in the nova nucleosynthesis scenario. Using the 1D hydrodynamic code SHIVA, we performed the nucleosynthesis calculations in a nova explosion to investigate the impact of the new rates of 28 P(p, γ) 29 S. Our calculations show that the nova abundance pattern is only marginally affected if we use our new rates with respect to the same simulations but statistical model rates. Finally, the isotopes whose abundance is most influenced by the present 28 P(p, γ) 29 S uncertainty are 28 Si, 33,34 S, 35,37 Cl, and 36 Ar, with relative abundance changes at the level of only 3% to 4%.

Astronomy & Astrophysics↗
DOE OSTI2016

Ab initio calculation of the electromagnetic and neutral-weak response functions of 4He and 12C

Precise measurement of neutrino oscillations, and hence the determination of their masses demands a quantitative understanding of neutrino-nucleus interactions. To this aim, two-body meson-exchange currents have to be accounted for along within realistic models of nuclear dynamics. We summarize our progresses towards the construction of a consistent framework, based on quantum Monte Carlo methods and on the spectral function approach, that can be exploited to accurately describe neutrino interactions with atomic nuclei over the broad kinematical region covered by neutrino experiments.

Lovato, A.↗
DOE OSTI2024

Monte Carlo evaluation of the semiclassical multi-step direct reaction series

We show how the multi-step direct reaction series can be evaluated using Monte Carlo methods. Nucleon-nucleon collisions occur according to the random selection of a nucleon’s attenuation factor along its classical trajectory. The particles and hole excited in a collision are selected from a local Fermi distribution. We assume that the particles continue to propagate and possibly collide again before leaving the nucleus. We assume that holes collide in place, to possibly produce other particles and holes.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2011

Medium-range icosahedral order in quasicrystal-forming Zr{sub 2}Pd binary metallic glass

Medium-range order in Zr{sub 2}Pd metallic glass was studied using a combination of x-ray diffraction experiment and atomistic simulations. We show that, in contrast to earlier experimental interpretations, the icosahedral-like polyhedron is centered around Pd, rather than Zr. Furthermore, we find that the ordered icosahedral packing around Pd extends to the third shell in the way similar to that in the Bergman-type clusters. The existence of Bergman-type clusters sheds interesting light into the formation of nanoquasicrystal phase during crystallization process of Zr{sub 2}Pd metallic glass.

36 MATERIALS SCIENCE↗
DOE OSTI2011

Structure, Stability, and Mobility of Small Pd Clusters on the Stoichiometric and Defective TiO2 (110) Surfaces

We report on the structure and adsorption properties of Pdn (n = 1–4) clusters supported on the rutile TiO2 (110) surfaces with the possible presence of a surface oxygen vacancy or a subsurface Ti-interstitial atom. As predicted by the density functional theory, small Pd clusters prefer to bind to the stoichiometric titania surface or at sites near subsurface Ti-interstitial atoms. The adsorption of Pd clusters changes the electronic structure of the underlying surface. For the surface with an oxygen vacancy, the charge localization and ferromagnetic spin states are found to be largely attenuated owing to the adsorption of Pd clusters. The potential energy surfaces of the Pd monomer on different types of surfaces are also reported. The process of sintering is then simulated via the Metropolis Monte Carlo method. The presence of oxygen vacancy likely leads to the dissociation of Pd clusters. On the stoichiometric surface or surface with Ti-interstitial atom, the Pd monomers tend to sinter into larger clusters, whereas the Pd dimer, trimer, and tetramer appear to be relatively stable below 600 K. This result agrees with the standard sintering model of transition metal clusters and experimental observations.

Environmental Molecular Sciences Laboratory↗