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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Graph theory and nighttime imagery based microgrid design

Reducing the duration and frequency of blackouts in remote communities poses an engineering challenge for grid operators. Outage effects can also be mitigated locally through microgrids. This paper develops a systematic procedure to account for these challenges by creating microgrids prioritizing high value assets within vulnerable communities. Nighttime satellite imagery is used to identify vulnerable communities. Using an asset classification and rating system, multi-asset clusters within these communities are prioritized. Infrastructure data, geographic information systems, satellite imagery, and spectral clustering are used to form and rank microgrid candidates. A microgrid sizing algorithm is included to guide through the microgrid design process. Finally, an application of the methodology is presented using real event, location, and asset data.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Monotonic multi-state quantum f -divergences

We use the Tomita–Takesaki modular theory and the Kubo–Ando operator mean to write down a large class of multi-state quantum f-divergences and prove that they satisfy the data processing inequality. For two states, this class includes the (α, z)-Rényi divergences, the f-divergences of Petz, and the Rényi Belavkin-Staszewski relative entropy as special cases. The method used is the interpolation theory of non-commutative Lωp spaces, and the result applies to general von Neumann algebras, including the local algebra of quantum field theory. We conjecture that these multi-state Rényi divergences have operational interpretations in terms of the optimal error probabilities in asymmetric multi-state quantum state discrimination.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Time dependence of reflected entropy in rational and holographic conformal field theories

We calculate the time dependence of the reflected entropy of two disconnected regions after a global quench in (1 + 1)-dimensional conformal field theories and in large temperature limit. For rational conformal field theories, we find that the time evolution of the reflected entropy is the same as that of the mutual information. We get the same result for holographic theories in the limit where the separation between disconnected regions is much smaller than their respective sizes. We discuss how this result is consistent with the quasi-particle picture of Calabrese and Cardy.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Out of time order correlation of the Hubbard model with random local disorder

The out-of-time-order correlator (OTOC) serves as a powerful tool for investigating quantum information spreading and chaos in complex systems. We present a method employing non-equilibrium dynamical mean-field theory and coherent potential approximation combined with diagrammatic perturbation on the Schwinger–Keldysh contour to calculate the OTOC for correlated fermionic systems subjected to both random disorder and electron interaction. Furthermore, our key finding is that random disorder enhances the OTOC decay in the Hubbard model for the metallic phase in the weakly interacting limit. However, the current limitation of our perturbative solver restricts the applicability to weak interaction regimes.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Large Angle Rocking Beam Electron Diffraction Utilizing Electron Direct Detector

Electron diffraction of a crystal is fundamentally a function of the potential of that crystal. The intensity of electron diffraction patterns is, as a result, sensitive to the charge density of atoms and bonding inside crystals. Experimentally, the traditional method to probe this information is quantitative Convergent Beam Electron Diffraction (CBED). Quantitative CBED or QCEBD is a method which uses dynamic diffraction theory to quantify CBED intensities and to extract information about crystal structure and bonding. The sensitivity of dynamical scattering is leveraged to measure crystal symmetry and crystal structure factors. At its limit, electron structure factors are measured at high accuracy allowing access to chemical bonding information, which corroborate theoretical calculations through multipole model refinements of the experimental charge density. The primary limit to QCBED is the requirement of a non-overlapping convergent beam. This subsequently limits QCBED to crystals with small unit cells which are stable under the focused probe.

Busch, Robert↗
DOE OSTI2026

Multi‐Scale Model‐Informed Deep Learning for Plasma‐Nanoparticle Interaction

The Overarching Goal of this proposed research is to understand and quantitively determine the interactions between non-thermal plasma (hot electrons, reactive radicals, vibrationally excited species) and surface reactions on influencing the activity and selectivity of the desired reactions via developing multi-scale model informed deep learning algorithm. Investigating non-thermal plasma-surface interaction is feasible due to the low bulk temperature in the discharge region. To investigate the role of plasma-nanoparticle interaction on enhancing the reaction kinetics, we will focus on ammonia cracking to generate clean hydrogen over earth-abundant, non-critical metallic nanoparticles, which is of great significance for decarbonization. We hypothesize that (1) reactive radicals interacting with surface reaction species via Eley–Rideal mechanism will significantly lower the energetics of the potential rate-limiting step of nitrogen formation; (2) the surface will be charged heterogeneously under non-thermal plasma conditions and the charged site will lower the energetics of ammonia cracking through Langmuir– Hinshelwood mechanism; (3) vibrationally excited ammonia will further promote the initial N-H bond cleavage. To access the hypothesis, we will (1) reveal the surface charge effects on tunning the reaction energetics via interpretable, physics-informed deep learning accelerated density functional theory (DFT) calculations; (2) determine the reactive radicals interacting with surface reaction species on tuning the reaction energetics via DFT; (3) reveal the surface charge effects on tunning the reaction energetics via DFT and deep learning models, (4) quantify how vibrationally excited species, reactive radicals, and surface charging effects on enhancing the catalysis via developing DFT-based microkinetic modeling (MKM) and active learning. Deep and active learning of plasma-nanoparticle interactions effects on enhancing ammonia cracking to generate hydrogen represents a new paradigm for designing high performance plasma materials. The fundamental science of how plasma-nanoparticle interactions will change the plasma kinetics and will improve the energy efficiency for decarbonization and sustainability. The interpretable and physics-informed machine learning model will accelerate low temperature plasma chemistry and material discovery with physics rules and model interpretation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Informing nuclear physics via machine learning methods with differential and integral experiments

Information from differential nuclear-physics experiments and theory is often too uncertain to accurately define nuclear-physics observables such as cross sections or energy spectra. Integral experimental data, representing the applications of these observables, are often more precise but depend simultaneously on too many of them to unambiguously identify issues in the observable with human expert analysis alone. Here, we explore how we can leverage physics knowledge gained from differential experimental data, nuclear theory, integral experiments, and neutron-transport calculations to better understand nuclear-physics observables in the context of the application area represented by integral experiments. We support this task with machine-learning methods to discern trends in a large amount of convoluted data. Differential and integral information was used in an analysis augmented by the random forest and the Shapley additive explanations metric. We chose as an application area one that is represented by criticality measurements and pulsed-sphere neutron-leakage spectra. We show one representative example ( 241 Pu fission observables) where the combination of differential and integral information allowed to resolve issues in data representing these observables. As a starting point, the machine learning (ML) algorithms highlighted several observables as leading potentially to bias in simulating integral experiments. Differential information, paired with sensitivity to integral quantities, allowed us then to pinpoint one specific observable ( 241 Pu fission cross section) as the main driver of bias. The comparison to integral experiments, on the other hand, allowed us to indicate a likely reliable experiment among several discrepant ones for this observables. In other cases (e.g., 239 Pu observables), we were not able to resolve the confounding introduced by integral experiments but instead highlighted the need for targeted new experiments and theory developments to better constrain the nuclear-physics space for the application area represented by integral experiments. We were able to combine information from differential experimental data, nuclear-physics theory, integral experiments, and neutron-transport simulations of the latter experiments with the help of the random forest algorithm and expert judgment. This combination of knowledge allows to improve our description of nuclear-physics observables as applied to a particular application area.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

The Coordination Behaviour of Cu I Photosensitizers Bearing Multidentate Ligands Investigated by X-ray Absorption Spectroscopy

A systematic series of four novel homo- and heteroleptic CuI photosensitizers based on tetradentate 1,10-phenanthroline ligands of the type X^N^N^X containing two additional donor moieties in the 2,9-position (X=SMe or OMe) were designed. Their solid-state structures were assessed by X-ray diffraction. Cyclic voltammetry, UV-vis absorption, emission and X-ray absorption spectroscopy were then used to determine their electrochemical, photophysical and structural features in solution. Following, time-resolved X-ray absorption spectroscopy in the picosecond time scale, coupled with time-dependent density functional theory calculations, provided in-depth information on the excited state electron configurations. For the first time, a significant shortening of the Cu–X distance and a change in the coordination mode to a pentacoordinated geometry is shown in the excited states of the two homoleptic complexes. These findings are important with respect to a precise understanding of the excited state structures and a further stabilization of this type of photosensitizers.

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

Scalar modular bootstrap and zeros of the Riemann zeta function

Using the technology of harmonic analysis, we derive a crossing equation that acts only on the scalar primary operators of any two-dimensional conformal field theory with U(1) c symmetry. From this crossing equation, we derive bounds on the scalar gap of all such theories. Rather remarkably, our crossing equation contains information about all nontrivial zeros of the Riemann zeta function. As a result, we rephrase the Riemann hypothesis purely as a statement about the asymptotic density of scalar operators in certain two-dimensional conformal field theories. We discuss generalizations to theories with only Virasoro symmetry.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

A physics-informed deep learning description of Knudsen layer reactivity reduction

A physics-informed neural network (PINN) is used to evaluate the fast ion distribution in the hot spot of an inertial confinement fusion target. The use of tailored input and output layers to the neural network is shown to enable a PINN to learn the parametric solution to the Vlasov–Fokker–Planck equation in the absence of any synthetic or experimental data. As an explicit demonstration of the approach, the specific problem of Knudsen layer fusion yield reduction is treated. Here, the predictions from the Vlasov–Fokker–Planck PINN are used to provide a non-perturbative solution of the fast ion tail in the vicinity of the hot spot, thus allowing the spatial profile of the fusion reactivity to be evaluated for a range of collisionalities and hot spot conditions. Excellent agreement is found between the predictions of the Vlasov–Fokker–Planck PINN and the results from traditional numerical solvers with respect to both the energy and spatial distribution of fast ions and the fusion reactivity profile, demonstrating that the Vlasov–Fokker–Planck PINN provides an accurate and efficient means of determining the impact of Knudsen layer yield reduction across a broad range of plasma conditions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Solution–Doped Donor–Acceptor Copolymers Based on Diketopyrrolopyrrole and 3, 3'–Bis (2–(2–(2–Methoxyethoxy) Ethoxy) ethoxy)–2, 2'–Bithiophene Exhibiting Outstanding Thermoelectric Power Factors with p –Dopants

The design of polymeric semiconductors exhibiting high electrical conductivity (σ) and thermoelectric power factor (PF) will be vital for flexible large-area electronics. In this work, four polymers based on diketopyrrolopyrrole (DPP), 2,3-dihydrothieno[3,4-b][1,4]dioxine (EDOT), thieno[3,2-b]thiophene (TT), and 3, 3'-bis (2-(2-(2-methoxyethoxy) ethoxy) ethoxy)-2, 2'-bithiophene (MEET) are investigated as side-chains, with the MEET polymers newly synthesized for this study. These polymers are systematically doped with tetrafluorotetracyanoquinodimethane ( F 4 TCNQ), CF3SO3H, and the synthesized dopant Cp(CN) 3 -(COOMe) 3 , differing in geometry and electron affinity. The DPP-EDOT-based polymer containing MEET as side-chains exhibits the highest conductivity (σ) ≈700 S cm–1 in this series with the acidic dopant (CF 3 SO 3 H). This polymer also shows the lowest oxidation potential by cyclic voltammetry (CV), the strongest intermolecular interactions evidenced by differential scanning calorimetry (DSC), and has the most oxygen-based functionality for possible hydrogen bonding and ionic screening. Other polymers exhibit high σ ≈300–500 S cm–1 and power factor up to 300 µW m –1 K –2 . The mechanism of conductivity is predominantly electronic, as validated by time-dependent conductance studies and transient thermo voltage monitoring over time, including for those doped with the acid. Furthermore, these materials maintain significant thermal stability and air stability over ≈6 weeks. Density functional theory calculations reveal molecular geometries and inform about frontier energy levels. Raman spectroscopy, in conjunction with scanning electron microscopy (SEM-EDS) and x-ray diffraction, provides insight into the solid-state microstructure and degree of phase separation of the doped polymer films. Infrared spectroscopy enables this study to further quantify the degree of charge transfer from polymer to dopant.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Structure-thermal property relationships of aikinite PbCuBiS 3

Understanding the structural properties and chemical bonding in complex multinary chalcogenides is highly valuable, as it provides vital information for controlling their physical properties to suit various technological applications. Here, we report on the structural, thermal, and electronic properties of PbCuBiS 3 by incorporating an experimental and theoretical investigation of this material. Analysis of the experimental temperature-dependent heat capacity and thermal conductivity data show that weak bonding and strong lattice anharmonicity give rise to ultralow thermal conductivity. Chemical bonding information obtained from the density functional theory electronic calculations reveal significant antibonding interactions that underscore the dominant role of the Cu-S tetrahedra on occupied antibonding states, as well as a highly distorted Pb/Bi-S environment inducing weak bonding and strong lattice anharmonicity. This study broadens our understanding of the structure-property relationships for this material, and can be useful for developing this and similar chalcogenide materials for potential applications of interest.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Exploring the Limits of Dative Boratrane Bonding: Iron as a Strong Lewis Base in Low-Valent Non-Heme Iron-Nitrosyl Complexes

We previously reported the synthesis and preliminary characterization of a unique series of low-spin (ls) {FeNO} 8–10 complexes supported by an ambiphilic trisphosphineborane ligand, [Fe(TPB)(NO)] +/0/– . In this study, we use advanced spectroscopic techniques and density functional theory (DFT) calculations to extract detailed information as to how the bonding changes across the redox series. We find that, in spite of the highly reduced nature of these complexes, they feature an NO + ligand throughout with strong Fe–NO π-backbonding and essentially closed-shell electronic structures of their FeNO units. This is enabled by an Fe–B interaction that is present throughout the series. In particular, the most reduced [Fe(TPB)(NO)] – complex, an example of a ls-{FeNO} 10 species, features a true reverse dative Fe → B bond where the Fe center acts as a strong Lewis-base. Hence, this complex is in fact electronically similar to the ls-{FeNO} 8 system, with two additional electrons “stored” on site in an Fe–B single bond. The outlier in this series is the ls-{FeNO} 9 complex, due to spin polarization (quantified by pulse EPR spectroscopy), which weakens the Fe–NO bond. These data are further contextualized by comparison with a related N 2 complex, [Fe(TPB)(N 2 )] – , which is a key intermediate in Fe(TPB)-catalyzed N 2 fixation. Our present study finds that the Fe → B interaction is key for storing the electrons needed to achieve a highly reduced state in these systems, and highlights the pitfalls associated with using geometric parameters to try to evaluate reverse dative interactions, a finding with broader implications to the study of transition metal complexes with boratrane and related ligands.

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

Turbulence-driven flow dynamics in general axisymmetric toroidal geometry

The present work gives the equations governing the generation of toroidally axisymmetric flows by turbulent Reynolds and Maxwell stresses in finite aspect ratio, general cross section tokamak plasmas. Inclusion of the divergence-free flow constraint in lowest order changes the time scale for evolution of the poloidal flows driven by turbulence by substantial factors. In the pedestal region for present day machines, comparing to earlier cylindrical models, the time scale evaluated using a large aspect ratio circular cross section model can be two orders of magnitude longer while the present, general geometry result can be about one order of magnitude longer. Inclusion of gyroviscosity in the calculation shows that the only lowest order radial velocity fluctuations that enter the problem are those due to fluctuating E×B flows. Toroidal and poloidal flow effects on the toroidally axisymmetric flows are inextricably coupled due to the neoclassical poloidal viscosity. Accordingly, the physics is inherently three dimensional and measurements of all three velocity components are required to obtain the information needed to quantitatively test the theory. The parallel and angular momentum equations for the lowest order, toroidally axisymmetric flows look like radial transport equations when the turbulence is included. The turbulence terms provide the radial transport fluxes. In standard neoclassical theory, the parallel flow equation is local on each flux surface; there is no radial derivative term. However, adding turbulence gives a way, in principle, for radial transport to lead to poloidal flows that deviate from the neoclassical prediction. As a result, the inclusion of the Maxwell stress provides a mechanism for MHD fluctuations to alter the toroidally axisymmetric flows.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Learning intermolecular forces at liquid–vapor interfaces

By adopting a perspective informed by contemporary liquid-state theory, we consider how to train an artificial neural network potential to describe inhomogeneous, disordered systems. Here, we find that neural network potentials based on local representations of atomic environments are capable of describing some properties of liquid-vapor interfaces but typically fail for properties that depend on unbalanced long-ranged interactions that build up in the presence of broken translation symmetry. These same interactions cancel in the translationally invariant bulk, allowing local neural network potentials to describe bulk properties correctly. By incorporating explicit models of the slowly varying long-ranged interactions and training neural networks only on the short-ranged components, we can arrive at potentials that robustly recover interfacial properties. We find that local neural network models can sometimes approximate a local molecular field potential to correct for the truncated interactions, but this behavior is variable and hard to learn. Generally, we find that models with explicit electrostatics are easier to train and have higher accuracy. We demonstrate this perspective in a simple model of an asymmetric dipolar fluid, where the exact long-ranged interaction is known, and in an ab initio water model, where it is approximated.

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

The QICK (Quantum Instrumentation Control Kit): Readout and control for qubits and detectors

We introduce a Xilinx RF System-on-Chip (RFSoC)-based qubit controller (called the Quantum Instrumentation Control Kit, or QICK for short), which supports the direct synthesis of control pulses with carrier frequencies of up to 6 GHz. The QICK can control multiple qubits or other quantum devices. The QICK consists of a digital board hosting an RFSoC field-programmable gate array, custom firmware, and software and an optional companion custom-designed analog front-end board. We characterize the analog performance of the system as well as its digital latency, important for quantum error correction and feedback protocols. We benchmark the controller by performing standard characterizations of a transmon qubit. We achieve an average gate fidelity of ℱ avg =99.93%. All of the schematics, firmware, and software are open-source.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Stability of Kuramoto networks subject to large and small fluctuations from heterogeneous and spatially correlated noise

Oscillatory networks subjected to noise are broadly used to model physical and technological systems. Due to their nonlinear coupling, such networks typically have multiple stable and unstable states that a network might visit due to noise. In this article, we focus on the assessment of fluctuations resulting from heterogeneous and spatially correlated noise inputs on Kuramoto model networks. We evaluate the typical, small fluctuations near synchronized states and connect the network variance to the overlap between stable modes of synchronization and the input noise covariance. Going beyond small to large fluctuations, we introduce the indicator mode approximation that projects the dynamics onto a single amplitude dimension. Such an approximation allows for estimating rates of fluctuations to saddle instabilities, resulting in phase slips between connected oscillators. Statistics for both regimes are quantified in terms of effective noise amplitudes that are compared and contrasted for several noise models. In conclusion, bridging the gap between small and large fluctuations, we show that a larger network variance does not necessarily lead to higher rates of large fluctuations.

42 ENGINEERING↗
DOE OSTI2024

First-order crosstalk mitigation in parallel quantum gates driven with multi-photon transitions

Here, we demonstrate an order of magnitude reduction in the sensitivity to optical crosstalk for neighboring trapped-ion qubits during simultaneous single-qubit gates driven with individual addressing beams. Gates are implemented via two-photon Raman transitions, where crosstalk is mitigated by offsetting the drive frequencies for each qubit to avoid first-order crosstalk effects from inter-beam two-photon resonance. The technique is simple to implement, and we find that phase-dependent crosstalk due to optical interference is reduced on the most impacted neighbor from a maximal fractional rotation error of 0.185(4) without crosstalk mitigation to ≤0.006 with the mitigation strategy. Furthermore, we characterize first-order crosstalk in the two-qubit gate and avoid the resulting rotation errors for the arbitrary-axis Mølmer–Sørensen gate via a phase-agnostic composite gate. Finally, we demonstrate holistic system performance by constructing a composite CNOT gate using the improved single-qubit gates and phase-agnostic two-qubit gate. This work is done on the Quantum Scientific Computing Open User Testbed; however, our methods are widely applicable for individual addressing Raman gates and impose no significant overhead, enabling immediate improvement for quantum processors that incorporate this technique.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗