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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Defect Structure in Quantum-Cutting Yb3+-Doped CsPbCl3 Perovskites Probed by X-Ray Absorption and Atomic Pair Distribution Function Analysis

Ytterbium-doping in all-inorganic lead-halide perovskites (CsPb(Cl1-xBrx)3) generates novel properties including quantum cutting and narrow line emission, making these materials attractive spectral down-converters for solar photovoltaics. The relationship between this optical efficiency and the defect structure(s) associated with Yb3+ dopants within perovskites is not well understood. Various charge-neutral doping motifs have previously been proposed and studied computationally, including clusters involving two substitutional Yb3+ ions charge-compensated by a single local Pb2+ vacancy. Near-band-edge defect states associated with such motifs are believed to play an important mechanistic role in quantum cutting itself. Here, we report the results of X-ray absorption and X-ray total-scattering measurements on ytterbium-doped CsPbCl3. XANES shows that the dopant oxidation state is exclusively Yb3+, and a combination of Yb L3 and Pb L3 EXAFS shows that this Yb3+ substitutes exclusively at Pb2+ sites, where it adopts a pseudo-octahedral [YbCl6]3- coordination environment. Shell-by-shell fits to the data show a short Yb-Cl bond distance of 2.58 Å compared to the Pb-Cl bond distance of 2.83 Å. We confirm this finding by X-ray pair distribution function analysis, which also shows evidence of additional Pb2+ vacancy formation induced by Yb3+ doping. We evaluate whether this is the primary mechanism of charge compensation using simulated EXAFS and pair distribution function data for several computed defect structures. Together, these results resolve the local dopant structures and charge-compensation mechanisms in lanthanide-doped all-inorganic lead-halide perovskites, and, as such, significantly advance the understanding of structure-function relationships in this important class of materials.

Kluherz, Kyle (ORCID:0000000279865167)↗
DOE OSTI2023

A Medium-/Low-Voltage Joint State Estimator Through Linear Uncertainty Propagation

Traditionally, distribution system state estimations (DSSE) are challenged by the lack of measurements at both primary and secondary sides of the system. The widely available cable television (CATV) voltage sensors installed in low-voltage (LV) networks bring opportunities to achieve higher quality DSSE covering a broader area of the distribution network. This study proposes a medium-/low-voltage (MV/LV) joint distribution system state estimation approach using the untapped CATV measurements. It aims at addressing the need for system situational awareness at the grid edge while improving the estimation accuracy at both the primary and secondary sides compared to its disjointed counterpart. Linearized measurement functions and boundary condition uncertainty propagation rules are derived to ensure the computational efficiency and accuracy of the joint state estimator. Numerical experiments are conducted on an IEEE test feeder to demonstrate the efficacy of the proposed method and the value of CATV measurements.

joint state estimation↗
DOE OSTI2023

A Verified Optimizer for Quantum Circuits

We present VOQC, the first verified optimizer for quantum circuits, written using the Coq proof assistant. Quantum circuits are expressed as programs in a simple, low-level language called SQIR, a small quantum intermediate representation, which is deeply embedded in Coq. Optimizations and other transformations are expressed as Coq functions, which are proved correct with respect to a semantics of SQIR programs. SQIR programs denote complex-valued matrices, as is standard in quantum computation, but we treat matrices symbolically to reason about programs that use an arbitrary number of quantum bits. SQIR’s careful design and our provided automation make it possible to write and verify a broad range of optimizations in VOQC, including full-circuit transformations from cutting-edge optimizers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

SENTRA: A Modular Computational Graph Framework for Critical Mineral and Materials Supply Chains: Part I: Network Construction Latent-Quantity Estimation, and Temporal Graph Forecasting

Global supply chains for critical minerals and materials are complex, evolving networks of countries, products, production stages, and trade relationships. Existing analytical approaches are limited by fragmented data and static network representations that do not capture the dynamic production dependencies linking raw materials, intermediate products, and final goods across multiple countries. Trade and production statistics provide only a partial view of domestic production, inventories, and material flows, making it difficult to identify indirect sourcing pathways, hidden dependencies, and embedded foreign exposures. This paper introduces the Supply Chain Exposure Network Tracking and Risk Assessment (SENTRA) framework, a modular graph-based computational framework for constructing, analyzing, and forecasting dynamic supply chain networks. As the first paper in a three-part methodological series, it establishes the computational foundation of SENTRA by constructing a temporal attributed multi-relational graph whose nodes represent product–country pairs and whose edges encode observed trade and within-country value-chain relationships. Statistical estimation and constrained optimization recover latent production, final demand, and product input dependency coefficients while enforcing economic accounting constraints. Graph-derived exposure measures quantify direct, transshipment, value-chain, and multi-hop supply chain dependencies independently of the forecasting model. A temporal graph forecasting architecture based on a relational graph neural network then forecasts the evolution of the graph under mass-balance constraints with distribution-free conformal uncertainty quantification. Validation on the global aluminum supply chain shows that the learned graph representations recover economically meaningful supply chain structure, accurately forecast out-of-sample trade relationships, and produce well-calibrated prediction intervals. Subsequent papers apply this computational foundation to exposure assessment, disruption analysis, and scenario-based policy analysis, and extend the framework to multimaterial supply chain modeling and decision support.

36 MATERIALS SCIENCE↗
DOE OSTI2023

X-ray Absorption Spectroscopy Studies of a Molecular CO 2 -Reduction Catalyst Deposited on Graphitic Carbon Nitride

Metal-ligand complexes have been extensively explored as well-defined molecular catalysts in small molecule activation reactions such as carbon dioxide (CO 2 ) reduction. Many hybrid photocatalysts have been prepared by coupling such complexes with photoactive surfaces for use in solar CO 2 reduction. In this work, we employ X-ray absorption near edge structure (XANES) and extended X-ray absorption fine structure (EXAFS) spectroscopies, density functional theory (DFT) and computational XANES modeling to interrogate the structure of a hybrid photocatalyst consisting of a macrocyclic cobalt complex deposited on graphitic carbon nitride (C 3 N 4 ). Results show that the cobalt complex binds on C 3 N 4 through surface OH or NH 2 groups. By refining the local geometry and binding sites of this well-defined molecular cobalt complex on C 3 N 4 , here we established an important benchmark for modeling a large class of molecular catalysts that can be adapted to in situ/operando studies and further enhanced by applying chemometrics-based approaches and machine learning methods of XANES data analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Investigation of Electrocatalytic CO 2 Reduction on MXene Materials via First-Principles Simulations

Computational studies of CO 2 reduction to yield various products were carried out on the basal plane and edges of different MXene materials. The impact of vacancies upon Mo 2 TiC 2 T x , W 2 TiC 2 T x , and Ti 3 C 2 T x (T x = O and OH) was also examined for both edge and basal sites. Initial calibrations were carried out to generate surfaces with optimal oxide/hydroxide ratios and proper termination sites upon which various vacancy sites were explored to ensure an accurate model of the surfaces’ resting states. From this work, Mo 2 TiC 2 O was determined to exhibit the lowest theoretical overpotential for methane as determined by volcano plot analyses. At a large enough vacancy concentration, the CO 2 reduction reaction (CO 2 RR) is predicted to outcompete the hydrogen evolution reaction (HER) as the predominant reaction on the surface. In conclusion, when examining the edge of Mo 2 TiC 2 O, stronger CO 2 binding was exhibited to such an extent that the reaction was predicted to terminate after the generation of formate on the edge.

Lund, Colton [Argonne National Laboratory (ANL), A↗
DOE OSTI2022

Atomically Dispersed Platinum in Surface and Subsurface Sites on MgO Have Contrasting Catalytic Properties for CO Oxidation

Atomically dispersed metals on metal oxide supports are a rapidly growing class of catalysts. Developing an understanding of where and how the metals are bonded to the supports is challenging because support surfaces are heterogeneous, and most reports lack a detailed consideration of these points. In this work, we report two atomically dispersed CO oxidation catalysts having markedly different metal–support interactions: platinum in the first layer of crystalline MgO powder and platinum in the second layer of this support. Structural models have been determined on the basis of data and computations, including those determined by extended X-ray absorption fine structure and X-ray absorption near edge structure spectroscopies, infrared spectroscopy of adsorbed CO, and scanning transmission electron microscopy. The data demonstrate the transformation of surface to subsurface platinum as the temperature of sample calcination increased. Catalyst performance data demonstrate the lower activity but greater stability of the subsurface platinum than of the surface platinum.

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

Tuning the Chern number in quantum anomalous Hall insulators

A quantum anomalous Hall (QAH) state is a two-dimensional topological insulating state that has a quantized Hall resistance of h/(Ce 2 ) and vanishing longitudinal resistance under zero magnetic field (where h is the Planck constant, e is the elementary charge, and the Chern number C is an integer). The QAH effect has been realized in magnetic topological insulators and magic-angle twisted bilayer graphene. However, the QAH effect at zero magnetic field has so far been realized only for C = 1. Here we realize a well quantized QAH effect with tunable Chern number (up to C = 5) in multilayer structures consisting of alternating magnetic and undoped topological insulator layers, fabricated using molecular beam epitaxy. The Chern number of these QAH insulators is determined by the number of undoped topological insulator layers in the multilayer structure. Moreover, we demonstrate that the Chern number of a given multilayer structure can be tuned by varying either the magnetic doping concentration in the magnetic topological insulator layers or the thickness of the interior magnetic topological insulator layer. We develop a theoretical model to explain our experimental observations and establish phase diagrams for QAH insulators with high, tunable Chern number. Furthermore, the realization of such insulators facilitates the application of dissipationless chiral edge currents in energy-efficient electronic devices, and opens up opportunities for developing multi-channel quantum computing and higher-capacity chiral circuit interconnects.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Unsupervised machine learning for unbiased chemical classification in X-ray absorption spectroscopy and X-ray emission spectroscopy

Here we report a comprehensive computational study of unsupervised machine learning for extraction of chemically relevant information in X-ray absorption near edge structure (XANES) and in valence-to-core X-ray emission spectra (VtC-XES) for classification of a broad ensemble of sulphorganic molecules. By progressively decreasing the constraining assumptions of the unsupervised machine learning algorithm, moving from principal component analysis (PCA) to a variational autoencoder (VAE) to t-distributed stochastic neighbour embedding (t-SNE), we find improved sensitivity to steadily more refined chemical information. Surprisingly, when embedding the ensemble of spectra in merely two dimensions, t-SNE distinguishes not just oxidation state and general sulphur bonding environment but also the aromaticity of the bonding radical group with 87% accuracy as well as identifying even finer details in electronic structure within aromatic or aliphatic sub-classes. We find that the chemical information in XANES and VtC-XES is very similar in character and content, although they unexpectedly have different sensitivity within a given molecular class. We also discuss likely benefits from further effort with unsupervised machine learning and from the interplay between supervised and unsupervised machine learning for X-ray spectroscopies. Our overall results, i.e., the ability to reliably classify without user bias and to discover unexpected chemical signatures for XANES and VtC-XES, likely generalize to other systems as well as to other one-dimensional chemical spectroscopies.

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

Predicted Heat Flux Performance of Actively Cooled Tungsten-Armored Graphitic Foam Monoblocks

Tungsten (W)–armored graphitic foam monoblocks were developed for applications requiring high-Z plasma-facing material in long-pulse fusion experiments and ultimately deuterium-tritium fusion reactors. The monoblocks are an integrated material system combining the advantages of a chemical vapor deposited (CVD) W coating with a high-conductivity graphitic foam. The W is a high-melting-point, high-Z material with low tritium retention. The graphitic foam coupled to a swirl tube serves as a high-thermal-conductivity heat sink that cannot melt, although it can sublime at much higher temperatures than copper melts. Together, they comprise a robust plasma-facing component (PFC) weighing roughly 5% of an all-W component or 17% of a traditional W-coated copper heat sink.A single-channel mock-up consisting of four graphitic foam monoblocks equipped with a water-cooled swirl tube was fabricated for eventual testing in the 60-kW, EB-60, rastered electron beam at the Applied Research Laboratory of The Pennsylvania State University. Two monoblocks have a thin 50-μm-thick coating of pure W chemically vapor deposited over NbC and pure Nb interlayers. Two others have a 2-mm-thick pure W coating CVD on graphitic monoblocks using the same interlayers. The mock-up will be cooled with available 10 m/s, 0.7 MPa water with a 22°C inlet temperature and subjected to varying uniform heat loads up to 20 MW/m 2 . It is equipped with type-K thermocouples at various depths, and calibrated infrared thermography and spot pyrometry will be used to characterize the heated surface. Real-time water calorimetry will be used to ascertain the absorbed steady-state power and infer the heat flux during testing. Since testing cannot be done under prototypic divertor flow conditions, it is necessary to predict the thermal response of this novel PFC system and investigate the power sharing between radiation and convection at divertor heat flux levels and its inherent ability to avoid critical heat flux. Results are reported for predictions obtained from computational fluid dynamics models up to 30 MW/m 2 of steady-state uniform heat flux. Leading-edge heat loads of 30 MW/m 2 on a 2-mm-wide side strip were also investigated to ascertain if coating delamination is likely.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Disruption-Robust Community Detection Using Consensus Clustering in Complex Networks

Topological (graph-theoretic) analysis of critical infrastructure networks provides insight on several aspects of resilience. Graph clustering or community detection, which identifies densely connected components in a graph, has been employed for analysis. In this paper, we propose employing consensus clustering, which is a technique to determine consensus from a collection of different clusters on an input, such that the resulting clustering is robust to disruptions, where a disruption is represented as loss of one or more vertices or edges in the graph. Using two critical infrastructure networks as case studies, we empirically demonstrate the need to compute consensus clustering in order to address the drastic changes in the topology due to disruptions in the network.

Hussain, Md Taufique↗
DOE OSTI2020

Design and Analysis of the Staggered Double Grating Slow Wave Circuit for 263 GHz Sheet Beam TWT

The complete design process of a high-frequency circuit for a 263 GHz staggered double grating, sheet beam traveling wave tube is presented in this article. Here, this device, for use in a pulsed electron paramagnetic resonance spectrometer instrument, requires >30 dB gain and a 20 GHz bandwidth centered at 263 GHz. In addition to the standard Pierce gain analysis and point by point particle in cell (PIC) simulation, a novel fast pulse response analysis technique was utilized to determine the accurate synchronization voltage and test the ``hot” feature of the high-frequency circuit. Since this new method can evaluate the entire bandwidth in a single simulation, it can significantly reduce the computational resources normally required for the conventional PIC analysis while also illustrating the potential instabilities near the band edge. Good agreement between the fast pulse response and PIC simulation results validate the accuracy of this novel method. According to the analysis results and given the manufacturing limits of nano/micro-computer numerical control (CNC) machining, an appropriate SWS, coupler, and sever designs were chosen to complement the simulated design. Finally, a prototype circuit was manufactured and cold tested to verify the design and inspect the machining tolerances.

42 ENGINEERING↗
DOE OSTI2025

pnnl/openstudio-building-energy-standard-measures-gem

The OpenStudio Building Energy Standards Measure Repository is a comprehensive collection of innovative OpenStudio measures designed to facilitate energy code analysis. OpenStudio is a cutting-edge platform brings together physics-based building energy modeling (BEM), BEM process automation, and large-scale computing capabilities. Contains OpenStudio Standards Performance Rating Method Measure (IPID 32936) and OpenStudio Measure to Generate IPLV-specific Chiller Performance Curves for Chillers (IPID 32940)

Xu, Weili [PNNL]↗
DOE OSTI2022

Asymptotic Expansion of the Impedance Per Unit Length for Rectangular Conductors

An iteration method is introduced to obtain the asymptotic form of the impedance per unit length of a rectangular conductor when the half side lengths are large compared to the skin depth. The first terms of the asymptotic expansion are extracted in closed form. The manner in which the corner corrections fit into the expansion are illustrated. The asymptotic results are compared to a numerical solution in the square limit. The odd corner correction for a right angle edge is also discussed.

42 ENGINEERING↗
DOE OSTI2024

Matching Curved Lattices to Anisotropic Tangent Planes

Radial quantization would be the ideal formalism for studying strongly-coupled near-conformal quantum field theories but it requires the ability to perform lattice calculations on static, curved manifolds, specifically a very long cylinder whose cross section is a sphere. Smoothly discretizing the surface of a sphere requires a graph with unequal edge lengths. The geometry of such graphs is well understood since 1961 using Regge Calculus. But, lattice quantum field theories are defined in terms of couplings which appear in the action rather than edge lengths and so the relationship between couplings and lengths must be determined dynamically. A simple example is computing the ratio of spatial to temporal lattice spacings in anisotropic lattice QCD. I will discuss our conjecture that computing anisotropic lattice spacing ratios on affine transformations of regular flat lattices is sufficient to determine coupling assignments on curved lattices.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Defect identification in simulated Bragg coherent diffraction imaging by automated AI

X-ray Bragg coherent diffraction imaging is a powerful technique for operando and in situ materials characterization and provides a unique means of quantifying the influence of one-dimensional (1D) and two-dimensional (2D) material defects on material response. However, obtaining full images from raw x-ray diffraction data is nontrivial and computationally intensive, precluding real-time experimental feedback. Here, we present a machine learning approach to identify the presence of crystalline line defects (edge and screw) in samples from the raw, 2D, coherent diffraction data without the need for image reconstruction through iterative phase retrieval. Further, we compare different approaches to designing neural networks for this application and demonstrate the potential of automated ML (autoML) approaches.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Blueprint for DOE Quantum Supercomputing: Ensuring U.S. Leadership in the Quantum Decade

Quantum computing stands at the threshold of a transformative decade, where the field will evolve from small-scale demonstrations toward practical scientific computing at scale. This Blueprint identifies fault-tolerant quantum computers (FTQCs) as a viable, scalable, and broadly applicable path to achieving “quantum scientific utility,” defined as solving scientifically valuable problems beyond the reach of conventional, classical computers. This capability is expected to show scientific demonstrations in the late 2020s and to mature in the early-to-mid 2030s. This Blueprint outlines a strategy to prepare the U.S. Department of Energy (DOE) for FTQCs and their integration into the U.S. national scientific computing infrastructure. Its purpose is to identify the steps, milestones, and research directions necessary for DOE to enable initial deployment of FTQCs in 2028 as a scientific tool for the nation and mature this capability into the 2030s. DOE has a long history of supporting quantum information science and technology, contributing significantly to research advancements, training a quantum-ready workforce, and providing access to early small-scale quantum hardware. Given recent demonstrations of logical operations on error-corrected logical qubits and the advancement of commercial hardware roadmaps, DOE should begin preparations for large-scale, fault-tolerant quantum computing deployment for DOE science missions. This Blueprint proposes that DOE focus on (1) deploying first-generation scientifically relevant quantum computers with at least 100 logical qubits and performing at least 10,000 to 100,000 hard logical operations in scientifically relevant calculations; (2) developing essential FTQC programming competencies, system software, and facility readiness; and (3) investing in cutting edge focused R&D that fosters breakthroughs in scientific applications, algorithms, and logical architectures needed to accelerate the advent of scientific utility. This effort will position DOE to transition to larger systems: production-scale quantum computers that comprise 1,000 to 10,000 logical qubits, perform 1 to 10 billion hard logical operations, and execute scientifically useful computations at scale. Achieving these goals will require DOE facilities to evolve with urgency to support scientific campaigns that integrate quantum and classical computing resources into efficient workflows, novel software and firmware environments for compiling and routing quantum programs on FTQC machines, and suitable infrastructure for quantum hardware. It will also require further development and optimization of scientific applications from the fields of materials science, quantum chemistry, and high-energy and nuclear physics. The Blueprint calls for transformative R&D and collective action to accelerate the advent of scientific quantum utility and bring it within reach by 2028.

97 MATHEMATICS AND COMPUTING↗