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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Chemical Imaging of Atmospheric Biomass Burning Particles from North American Wildfires

The effects of biomass burning aerosols (BBA) on radiative forcing and cloud formation depend on chemical composition and the internal structures of individual particles within smoke plumes. To improve our understanding of the chemical and physical properties of BBA emitted at different times of the day and their evolution during atmospheric aging, we conducted a study as a part of the Fire Influence on Regional to Global Environments and Air Quality field campaign. Particle samples were collected onboard a research aircraft from smoke plumes from a wildfire in eastern Oregon during late afternoon and nighttime flights on August 28, 2019. A time-resolved aerosol collector was used to collect samples on substrates for offline spectromicroscopic imaging to investigate the single-particle characteristics of BBA particles. Approximately 20,400 individual particles from 10 selected samples were analyzed using computer-controlled scanning electron microscopy coupled with energy-dispersive X-ray microanalysis, revealing their elemental composition, morphology, and viscosity. Elemental microanalysis indicated that aged potassium is likely found in the form of K 2 SO 4 , KNO 3 , and possible K-organic salts. Further chemical speciation and carbon bonding mapping within individual particles were conducted using synchrotron-based scanning transmission X-ray microscopy (STXM) coupled with near edge X-ray absorption fine structure (NEXAFS) spectroscopy. Real-time, water-soluble light absorption measurements were acquired using a particle-into-liquid sampler instrument coupled to a liquid waveguide capillary cell and total organic analyzer. In the late afternoon samples, 65% of the total particle number population consisted entirely of organic components, compared to 46% in the nighttime particles. These differences were attributed to discrepancies in composition at the time of emission and to the daytime condensation and accumulation of photochemically formed secondary organic material on existing BBA particles, a process that halts at night. Microscopy images indicated that particle viscosity was lower in the nighttime particles (<10 1 Pa·s), likely due to increased relative humidity and a higher contribution from hygroscopic inorganic components. The chemical heterogeneity of individual particles was quantified using STXM-derived mixing state parameters. The nature of carbon bonding within individual particles was inferred from the extent of carbon sp 2 hybridization derived from NEXAFS spectra. Average percentages of sp 2 hybridization range between 40% and 60%, with no noticeable differences between late afternoon and nighttime flights. These findings were compared with the online optical properties of both late afternoon and nighttime smoke plumes, providing valuable insights into the complex relationship between chemical composition and optical properties of BBA particles at different times of the day.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Computational simulation of asymmetric phase transformation in cracked Li₇La₃Zr₂O₁₂: Variant selection and chemo-mechanical implications

Coupling between microcracks and phase transformation in ion-conducting ceramics can jointly affect mechanical responses and ion transport. In this work we investigate the cubic-to-tetragonal phase transformation in Li₇La₃Zr₂O₁₂ in the presence of a microcrack under hydrostatic loading and quantify its implications for crack-tip stress concentration and Li-ion transport using phase-field and molecular dynamics simulations. The phase transformation exhibits a strong asymmetry between hydrostatic tension and compression. Under tension, the crack edge nucleates one tetragonal variant that amplifies the crack-tip stress intensity and promotes crack opening. Under compression, the crack tip nucleates a different tetragonal variant that enhances the stress-induced crack-closure tendency. Effective Li diffusivity analysis shows faster transport degradation under compression due to accelerated transformation kinetics, exposing a trade-off between mechanical stability and ionic conductivity. These results highlight the intertwined nature of cracking, phase transformation, and ionic transport in ion-conducting oxides and provide mechanistic insights into chemo-mechanical degradation of solid electrolytes.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A Combined Computational and Mathematical Analysis of Interconnect Fatigue Potential in Photovoltaic Modules

A finite element model of a 60-cell monocrystalline silicon glass-polymer photovoltaic module was simulated with ±1.0 kPa and ±2.4 kPa loads applied to the glass to calculate the deformation under load. Cell-to-cell displacements were used to approximate interconnect strain and stress. A mathematical fatigue cycle life relation was fitted to data for the interconnect material (copper), to generate a life prediction at each interconnect location based on the local stress means, reversal extents, and amplitudes. Interconnect stress was found to be significantly asymmetric about zero despite symmetric positive and negative module loads due to laminate thickness offsets about the neutral plane and the effects of module framing. Cycle life results indicated that interconnect fatigue failure was unlikely to occur over a 30-year lifetime of conservative wind and snow load cycles since the typical cell design feature of leaving some unconstrained length between the cell edge and first solder pad increases the effective gauge length and decreases the stress levels below the material endurance limit. Follow-up analyses found that 3.6 mm and 6.4 mm were the minimum unconstrained lengths required to survive the assumed lifetime of wind and snow cycles, respectively, confirming that typical industrial module constructions with 8–15 mm unconstrained lengths should survive conservatively. Notably, large magnitude, low-cycle snow loading was consistently the limiting factor requiring a longer unconstrained interconnect length. Finally, insights and workflows from this study inform module interconnection design limits for survival against mechanical fatigue in deployment environments.

14 SOLAR ENERGY↗
DOE OSTI2025

Demonstration Trials of AI/ML Edge+Cloud Suite (CRADA Final Report)

PACE AI and LBNL partnered under this CRADA to test and evaluate the PACE5 edge node prototype, an AI/ML edge and cloud-based suite, at FLEXLAB.The objective of the test was to evaluate the PACE5 edge node prototype's ability to perform demand shed and take to dynamic price signals, and to demonstrate advanced fault detection and microgrid monitoring capabilities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Codiscovering graphical structure and functional relationships within data: A Gaussian Process framework for connecting the dots

Most problems within and beyond the scientific domain can be framed into one of the following three levels of complexity of function approximation. Type 1: Approximate an unknown function given input/output data. Type 2: Consider a collection of variables and functions, some of which are unknown, indexed by the nodes and hyperedges of a hypergraph (a generalized graph where edges can connect more than two vertices). Given partial observations of the variables of the hypergraph (satisfying the functional dependencies imposed by its structure), approximate all the unobserved variables and unknown functions. Type 3: Expanding on Type 2, if the hypergraph structure itself is unknown, use partial observations of the variables of the hypergraph to discover its structure and approximate its unknown functions. These hypergraphs offer a natural platform for organizing, communicating, and processing computational knowledge. While most scientific problems can be framed as the data-driven discovery of unknown functions in a computational hypergraph whose structure is known (Type 2), many require the data-driven discovery of the structure (connectivity) of the hypergraph itself (Type 3). We introduce an interpretable Gaussian Process (GP) framework for such (Type 3) problems that does not require randomization of the data, access to or control over its sampling, or sparsity of the unknown functions in a known or learned basis. Its polynomial complexity, which contrasts sharply with the super-exponential complexity of causal inference methods, is enabled by the nonlinear ANOVA capabilities of GPs used as a sensing mechanism.

Science & Technology - Other Topics↗
DOE OSTI2026

Fingerprinting Uranium Oxides with Electron Energy Loss Spectroscopy Supported by Theoretical Computations

Uranium oxides occur in a variety of phases that differ in crystal structure and uranium oxidation states. Electron energy loss spectroscopy (EELS) is one of the few techniques that has sufficient spatial resolution and sensitivity to electronic structure to distinguish amongst phases at the nanoscale. However, beam-sensitive materials such as uranium oxides are subject to spectral modification due to interactions with the electron beam. Therefore, theory support is essential to reliably exclude the impact of beam damage and generate true reference datasets. Here we use a comparison of theoretical and experimental spectra to probe the impact of beam damage on O K-edge and U N-edge (N6,7 and N4,5) EELS spectra of various single-valent and mixed-valence uranium oxide bulk phases. Using a low-dose experimental set-up, we show that the O K-edge theoretical spectra are in excellent agreement with experiment for both peak positions and relative intensities of respective peaks. In contrast, U N-edge features are less distinguishing due to the partially localized nature of the U 5f orbitals and overlapping multiplet and spin–orbit coupling effects. This work demonstrates that O K-edge EELS is sufficiently diagnostic to distinguish a wide range of uranium oxides and that the experimental approach used here minimizes beam damage and allows valence state discrimination across the U(IV), U(V) and U(VI) series. When combined with imaging modes available in electron mi-croscopy, the work enables detailed investigation and characterization of uranium redox transformations at the nanoscale.

Carbone, Jacopo↗
DOE OSTI2025

Exploring the Coexistence of Spin States in [Fe(tpy-Ph) 2 ] 2+ Complexes on Au(111) Using DFT Calculations

In this work, we systematically study the electronic structure and stability of spin states of the [Fe-(tpy-ph) 2 ] 2+ molecule in both the gas phase and on a Au(111) substrate using density functional theory + U (DFT+ U ) calculations. We find that the stability of the Fe 2+ ion’s spin states predicted by the computations is significantly influenced by the Hubbard U parameter. In the gas phase, the low-spin (LS, S = 0) state is found to be energetically favorable for U (Fe) ≤ 3 eV, whereas the high-spin (HS, S = 2) state is stabilized for U (Fe) > 3 eV. Interaction with the Au(111) substrate is found to elevate the critical U for the spin-state transition to 3.5 eV. Additionally, we perform L-edge X-ray absorption spectroscopy (XAS) calculations for both HS and LS states. The calculated XAS suggests that the HS state more closely aligns with the experimental observations, indicating the potential coexistence of the HS state as the initial state during the X-ray excitation process. These findings enrich our understanding of spin-state dynamics in [Fe(tpy-Ph) 2 ] 2+ .

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

Efficient frequency allocation for superconducting quantum processors using improved optimization techniques

Building on previous research on frequency allocation optimization for superconducting circuit quantum processors, this work incorporates several techniques to improve overall solution quality. Here, we introduce constraints and imposed edgewise differences help to improve the optimization results. We also introduce optimization variables for the orientation of each edge, defined as the direction from the control qubit to the target qubit, to be chosen during optimization. To scale up to larger processors, multimodule designs are employed with various boundary conditions, thereby enhancing the collective yield. These enhancements allow for greater flexibility in processor design by eliminating the need for handpicked orientations. We support the efficient assembly of large processors with dense connectivity by choosing the best boundary conditions. Examples demonstrate that, at low computational cost, this optimization approach finds a frequency configuration for a square chip with over 1000 qubits and over 10% yield at much larger dispersion levels than required by previous approaches.

Zhang, Zewen [Argonne National Laboratory (ANL), A↗
DOE OSTI2026

Metal additively manufactured wavy fin cold-plate architecture for improved thermal-hydraulic performance

Rapid growth in artificial intelligence and data center workloads demands high-performance liquid cooling to manage increasing chip power. This study presents two metal-additive-manufactured cold plates with sinusoidal fins, constant-amplitude wavy fins and linearly variable-amplitude wavy fins and compares them against metal-additive-manufactured straight fins using experiments conducted at 1 kW heat dissipation as well as high-fidelity 3D conjugate computational fluid dynamic simulations. The cold plates were printed in AlSi10Mg material and underwent design using a Python-automated workflow prior to manufacture and testing. The experiments show that wavy fins reduce the normalized thermal resistance by 35 to 45 % at water flow rates from 1 to 4 LPM. At a fixed 20 kPa pressure drop, the variable-waviness design lowered peak surface temperature by 9 °C and thermal resistance by 51 %, while edge-channel maldistribution in the constant wavy fin design limited gains. A thermal resistance breakdown revealed that 55–63 % of the total thermal resistance in wavy designs comes from base heat conduction, 27–33 % from fin heat conduction, and 9–13 % from fin heat convection, indicating the need to address conduction bottlenecks. Parametric sweeps identify a 3 mm fin pitch as optimal, and that horizontal inlet/outlet manifolds further reduce pressure drop by 30–60 % and thermal resistance by 9–16 % relative to vertical inlet-outlet manifolds. The results yield comprehensive guidelines for fin geometry, manifold alignment, material selection and additive-manufacturing constraints to realize high-performance liquid-cooled cold plates for power-dense electronics.

3d printing↗
DOE OSTI2026

Ensemble Federated Machine Learning‐Based Cybersecurity Situational Awareness in Microgrid Network

Cyber-physical microgrids are vulnerable to stealthy cybersecurity threats that disguise their actions through the exploitation of system knowledge. Such actions can severely impacts microgrids deployed in defense bases, slowing the response time of military forces during national emergencies. Several machine-learning algorithms have been proposed to detect intrusions in the grid networks; however, these traditional machine-learning algorithms lack data privacy and are subject to several adversarial machine-learning threats. This paper proposes a novel federated machine learning (FML)-based three-model framework to detect and identify stealthy data-integrity attacks while ensuring data privacy in microgrid networks. The proposed architecture uses a variational mode decomposition technique to extract derived features from incoming measurement and control datasets. The extraction of these derived features allows FML models to learn minute variations in data patterns that allow them to perform significantly better than the models trained with generic datasets consisting of raw features. Our experimental results show the efficient performance of the proposed methodology against different types of data integrity attacks while considering primary and secondary controllers in microgrids. Further, the applied FML-integrated random forest ensemble algorithm outperforms the existing generic FML algorithms during noisy and noise-free datasets with prediction latencies of only 91–134 µs per sample within the 0.1 s sampling interval and requires communication bandwidth of around ∼8.25 KB/s at the control center and ∼2.7 KB/s per edge client for communication.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

District-Scale Analysis of Electricity Load and Strategies to Improve Energy Reliability Using Prototype District Models

Projected increases in electricity demand in the U.S. highlight the urgent need for effective load management to ensure grid reliability. As the building sector accounts for approximately 75% of electricity usage, enhancing energy efficiency and flexibility in this sector is crucial. Adopting district-level approaches offers significant advantages over traditional individual building analyses by enabling shared infrastructure and economies of scale. To navigate the data and computational challenges associated with modeling energy at the district level, prototype district models have been proposed as holistic, system-level solutions that capture complex interactions within typical configurations. This study presents these models as a reference tool for analyzing district-scale energy systems across various climate zones in the U.S. Developed with input from stakeholders, these models integrate varied building characteristics, inter-building connections, and energy system interactions. A case study utilizing the Urban Edge prototype district model, implemented on the URBANopt™ platform, evaluates multiple demand scenarios and the impact of distributed energy resources such as fuel-fired backup generators, photovoltaic systems, and batteries. Findings suggest that while new electric systems can significantly reduce annual energy use, they may also elevate peak electricity loads, with a notable 43% increase in heating-dominant climate zone 5B. The optimal backup power solutions vary based on location, influenced by factors such as utility rates and incentives. For example, PV and batteries perform well in high-cost regions like New York City, while diesel backup generators are more suitable for backup needs in climate zone 3A, such as Atlanta. Thus, this research highlights the importance of prototype district models for future district-scale energy planning.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Defect Properties, Anion Ordering, and Photochromic Mechanism in Yttrium Oxyhydride

Yttrium oxyhydride (YHO) undergoes a reversible photochromic transition when exposed to ultraviolet light. However, the mechanism for this transformation is not fully understood, and the structure and precise chemical composition of YHO remain under debate. Here, we use first-principles density functional theory calculations with a hybrid functional to study the structure, chemical stability, and point defect properties of YHO. As experiments have shown, we find that YHO prefers a cubic structure, with H and O anions present in equal concentrations and located on tetrahedral sites. Stoichiometric and ordered YHO is chemically stable, but it has a wide band gap of 5.01 eV, considerably larger than that measured in experiments (2.4–3.8 eV). On the other hand, Y4H10O has a smaller band gap of 2.97 eV and also has a region of chemical stability; thus, the actual material may include some fraction of this H-rich structure. The defect chemistry of YHO is dominated by anionic antisite species (H O and O H ), with hydrogen interstitials (H i ) and vacancies (V H ) also present in reasonably high concentrations. We show that antisite disorder lowers the band gap relative to the perfectly ordered structure, bringing the magnitude of the gap into closer agreement with experiment. Based on our calculations of defect migration and the positions of defect states relative to the band edges, we link the onset of photochromic behavior to the reaction H O – → V O 0 + H i – , which follows photoexcitation of a H O + defect. H i – can subsequently migrate away and be trapped by additional H O + defects, contributing to the persistence of the reaction, while the resultant oxygen vacancy, V O 0 , introduces an occupied defect state that leads to optical absorption at visible wavelengths. Our results can explain reported discrepancies between experimental and computational results for YHO, and they allow us to propose specific atomic-scale processes that can lead to photochromism. In conclusion, understanding these mechanisms is key for unlocking YHO’s application in devices ranging from smart windows and optoelectronics to electrochemical synapses for neural networks.

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

Simulations of Radiation Hydrodynamics in Strongly Coupled Plasmas (Final Report)

Radiation propagates throughout the Universe and is often the only information we have from distant astronomical objects. Additionally, radiation plays a key role in systems ranging from the interior of stars to the establishment of shocks at the edges of forming galaxies to neutron star mergers. Here radiative processes, absorption, emission, and scattering, are fundamental to transport of momentum and energy, as well as, the overall evolution of these and other astrophysical systems. In some cases, radiation-dominant systems are also sufficiently dense and ionized, such that, the ions are strongly coupled (ie electrostatic energy >> thermal energy or Γ >> 1), which will affect radiative processes and the transport of radiation. During this project, we developed a computational ray-tracing algorithm to study radiation transport through a simulated neutron star capsule implosion under conditions relevant to Omega-60 laser experiments.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

QPatLib v1.0 — Measurement-based quantum simulation Pauli string unitary pattern collections

This Zenodo record accompanies the paper “Scalable Measurement-Based Quantum Simulation Patterns for Benchmarking” arXiv.2605.12502 and provides QPatLib v1.0 measurement-pattern datasets in human-readable JSONL together with a ZIP archive of OpenQASM 3.0 circuits used for validation and reproducibility. The patterns and circuits implement Pauli string unitaries for benchmark cases. Cases include all possible string combinations for less than 6 qubits and strings used in Hamiltonians for certain diatomic molecules for 6 or more qubits. Format: Each pattern_*.jsonl file is containins measurement patterns for all subsets for a given model/instance and subset strategy: it begins with a preamble containing model metadata, subset definitions, provenance, and (when feasible) full-pattern test results, followed by one pattern entry per subset. Each subset entry includes a required pattern_ascii field storing the measurement pattern in the measurement-calculus/Graphix standard with signal shifting, written left-to-right in the canonical order nodes → edges → measurements (with signal dependencies) → byproduct corrections (X/Z). The circuits are included as circuit_files.zip. Patterns in this record were validated against the corresponding circuits and checked for causal flow. Codes for generating these patterns can be found at QPatLib repository on Github

Graphix↗
DOE OSTI2025

Fast Machine Learning for Quantum Control of Microwave Qudits on Edge Hardware

Quantum optimal control is a promising approach to improve the accuracy of quantum gates, but it relies on complex algorithms to determine the best control settings. CPU or GPU-based approaches often have delays that are too long to be applied in practice. It is paramount to have systems with extremely low delays to quickly and with high fidelity adjust quantum hardware settings, where fidelity is defined as overlap with a target quantum state. Here, we utilize machine learning (ML) models to determine control-pulse parameters for preparing Selective Number-dependent Arbitrary Phase (SNAP) gates in microwave cavity qudits, which are multi-level quantum systems that serve as elementary computation units for quantum computing. The methodology involves data generation using classical optimization techniques, ML model development, design space exploration, and quantization for hardware implementation. Our results demonstrate the efficacy of the proposed approach, with optimized models achieving low gate trace infidelity near $10^{-3}$ and efficient utilization of programmable logic resources.

Sanders, Flor [Columbia U.]↗
DOE OSTI2026

Multimodal Approaches for Leveraging Domain Knowledge with State-of-the-Art Machine Learning to Engineer Biocatalysts

This grant aimed to accelerate the development of specialized enzymes—biological catalysts essential for sustainable manufacturing and medicine—by integrating traditional laboratory evolution with cutting-edge artificial intelligence. To achieve this, we developed a suite of high-throughput sequencing tools and a centralized database to bridge the gap between a protein’s genetic "code" and its physical function. By training machine learning models on large datasets, we also demonstrated the ability to move beyond slow, trial-and-error testing to a "generative" approach, where AI can independently design new, versatile enzymes like tryptophan synthases. Ultimately, these findings demonstrate that combining laboratory data with computer-guided design enables the engineering of highly efficient biological tools with unprecedented speed and precision.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Cybersecurity Considerations and Research Pathways for Grid-Interactive Efficient Buildings

Federal facilities serve critical missions and functions that require safe, reliable, and efficient operations. Digitization of several facility operations has increased the cost-effectiveness of energy usage and optimization of energy system performance. As the building controls landscape shifts to become more connected and smarter, building operators now face unique opportunities and challenges to adopt smart enabled devices that can lower energy usage while also optimizing building system performance. The grid-interactive efficient buildings (GEB) initiative aims to make buildings cleaner and more flexible through these smart devices. Smart enabled devices allow greater connectivity and control through remote operations and provide crucial data for analytics and increased efficiency. GEBs enable demand flexibility that has the potential to reduce electrical costs and transform the grid edge where buildings connect to power grids. This operation of interconnected systems, if not designed with cybersecurity practices, causes security gaps and introduces potential attack paths by adversarial and non-adversarial entities leading to disruption of operations.

building controls↗
DOE OSTI2025

Multimode turbulent flow measurements using magnetic resonance imaging- and laser-based techniques and computational fluid dynamics simulations

We studied the flow field characteristics of a turbulent flow over a regularized cube array with a perpendicular injection flow through the floor between the second and third cubical elements, representing the complex flow interactions of a 3D jet and the wake flows behind cubical obstacles. Four different experimental measurements were performed: two magnetic resonance imaging-based measurements for three-dimensional three-component velocity (MRV) and concentration (MRC) and two laser-based techniques, particle image velocimetry (PIV) and planar laser-induced fluorescence (PLIF), for two-dimensional two-component velocity and concentration measurement, respectively. The mainstream Reynolds number is Re = 15 000⁠, based on the primary inlet velocity U m and channel height D H ⁠, whereas the injector Reynolds number is Re j = 3400⁠, based on the injector velocity U j and the injector's exit width D j ⁠. Numerical simulations were performed for the studied flow configuration of turbulent flow over a regularized cube array using Reynolds-averaged Navier–Stokes (RANS) and large-eddy simulation (LES) approaches. Results obtained from experimental measurements—including MRV, MRC, PIV, and PLIF—as well as RANS and LES simulations are discussed and compared along several horizontal and vertical planes of the studied configuration. In addition, 3D turbulent flow structures, such as leading-edge vortex, horseshoe vortex, and jet shear-layer vortex, and the isosurfaces of scalar concentration successfully revealed by MRV and MRC techniques were found to be in very good agreement with those 3D features extracted from RANS and LES simulations. In conclusion, the high-resolution experimental and numerical database obtained from this study could be useful for validation and verification of numerical codes.

Computational fluid dynamics↗