SEARCH · Search NASA
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Capabilities fact sheet for the National Center for Photovoltaics: Process Technology and Advanced Concepts -- High-Throughput Combi Material Science and Atmospheric Processing that includes scope, core competencies and capabilities, and contact/web information.
Vera C. Rubin Observatory is a ground-based astronomical facility under construction, a joint project of the National Science Foundation and the U.S. Department of Energy, designed to conduct a multipurpose 10 yr optical survey of the Southern Hemisphere sky: the Legacy Survey of Space and Time. Significant flexibility in survey strategy remains within the constraints imposed by the core science goals of probing dark energy and dark matter, cataloging the solar system, exploring the transient optical sky, and mapping the Milky Way. The survey’s massive data throughput will be transformational for many other astrophysics domains and Rubin’s data access policy sets the stage for a huge community of potential users. To ensure that the survey science potential is maximized while serving as broad a community as possible, Rubin Observatory has involved the scientific community at large in the process of setting and refining the details of the observing strategy. The motivation, history, and decision-making process of this strategy optimization are detailed in this paper, giving context to the science-driven proposals and recommendations for the survey strategy included in this Focus Issue.
Histone lysine methyltransferases (KMTs) represent an important class of epigenetic enzymes that play essential roles in regulation of gene expression in humans. Members of the KMT family catalyze the transfer of the methyl group from S-adenosylmethionine (SAM) to lysine residues in histone tails and core histones. Here we report combined MALDI-TOF MS experiments, NMR analyses and quantum mechanical/molecular dynamics studies on human KMT-catalyzed methylation of the most related shorter and longer lysine analogues, namely ornithine and homolysine, in model histone peptides. Our experimental work demonstrates that while lysine is an excellent natural substrate for KMTs, ornithine and homolysine are not. This study reveals that ornithine does not undergo KMT-catalyzed methylation reactions, whereas homolysine can be methylated by representative examples of human KMTs. The results demonstrate that the specificity of KMTs is highly sensitive to the side chain length of the residue to be methylated. The origin for the degree of the observed activities of KMTs on ornithine and homolysine is discussed.
Stress relaxation in aluminum micron-scale particles covered by alumina shell after pre-stressing by thermal treatment and storage was measured using X-ray diffraction with synchrotron radiation. Pre-stressing was produced by annealing Al particles at 573K followed by fast cooling. While averaged dilatational strain in Al core was negligible for untreated particles, it was measured at 4.40×10 -5 and 2.85×10 -5 after 2 and 48 days of storage. Consistently, such a treatment leads to increase in flame propagation speed for Al+CuO mixture by 37% and 25%, respectively. Analytical model for creep in alumna shell and stress relaxation in Al core-alumina shell structure is developed and activation energy and pre-exponential multiplier are estimated. The effect of storage temperature and annealing temperature on the kinetics of stress relaxation was evaluated theoretically. These results provide estimates for optimizing Al reactivity with the holding time at annealing temperature and allowable time for storage of Al particles for various environmental temperatures.
Au38(SR)24 is one of the most extensively investigated gold nanomolecules along with Au25(SR)18 and Au144(SR)60. However, so far it has only been prepared using aliphatic-like ligands, where R = –SC6H13, -SC12H25 and –SCH2CH2Ph. Au38(SCH2CH2Ph)24 when reacted with HSPh undergoes core-size conversion to Au36(SPh)24, and existing literature suggest that Au38(SPh)24 cannot be synthesized. Here, contrary to prevailing knowledge, we demonstrate that Au38(SPh)24 can be prepared if the ligand exchanged conditions are optimized, without any formation of Au36(SPh)24. Conclusive evidence is presented in the form of MALDI-MS, ESI-MS characterization, and optical spectra of Au38(SPh)24 in a solid glass form showing distinct differences from that of Au38(S-aliphatic)24. Theoretical analysis confirms experimental assignment of the optical spectrum and shows that the stability of Au38(SPh)24 is comparable to that of its aliphatic analogues, but results from different physical origins, with a significant component of ligand-ligand attractive interactions.
We report J π = 0 + ground-state energies and point-proton radii of 4 He, 8 Be, 12 C, 16 O, and 20 Ne nuclei calculated by the ab initio no-core Monte Carlo shell model with the JISP16 and Daejeon16 nonlocal NN interactions. Ground-state energies are obtained in the basis spaces up to seven oscillator shells (N shell =7) with several oscillator energies (ℏω) around the optimal oscillator energy for the convergence of ground-state energies. These energy eigenvalues are extrapolated to obtain estimates of converged ground-state energies in each basis space using energy variances of computed energy eigenvalues. We further extrapolate these energy-variance-extrapolated energies obtained in the finite basis spaces to infinite basis-space results with an empirical exponential form. This form features a dependence on the basis-space size but is independent of the value of ℏω used for the harmonic-oscillator basis functions. Point-proton radii for these states of atomic nuclei are also calculated following techniques employed for the energies. From these results, it is found that the Daejeon16 NN interaction provides good agreement with experimental data up to approximately 16 O, while the JISP16 NN interaction provides good agreement with experimental data up to approximately 12 C. Beyond these nuclei, the interactions produce overbinding accompanied by radii that are too small. These findings suggest and encourage further revisions of nonlocal NN interactions towards the investigation of nuclear structure in heavier-mass regions.
Emerging wide-bandgap (WBG) semiconductor devices such as silicon carbide (SiC) metal–oxide semiconductor field-effect transistors (MOSFETs) and gallium nitride high-electron-mobility transistors can handle high power in reduced semiconductor areas better than conventional Si-based devices owing to superior material properties. With increased power loss density in a WBG-based converter and reduced die size in power modules, thermal management of power devices must be optimized for high performance. Here, we present a graphite-embedded insulated metal substrate (thermally-annealed-pyrolytic-graphite-embedded insulated metal substrate—IMSwTPG) designed for WBG power modules. Theoretical thermal performance analysis of graphite-embedded metal cores is presented, with design details for IMSwTPG with embedded graphite to replace a direct-bonded copper (DBC) substrate. The proposed IMSwTPG is compared with an aluminum nitride-based DBC substrate using finite-element thermal analysis for steady-state and transient thermal performance. The solutions’ thermal performances are compared under different coolant temperature and thermal loading conditions, and the proposed substrate's electrical performance is validated with static and dynamic characterization. Using graphite-embedded substrates, junction-to-case thermal resistance of SiC MOSFETs can be reduced up to 17%, and device current density can be increased by 10%, regardless of the thermal management strategy used to cool the substrate. Reduced transient thermal impedance of up to 40% of dies owing to increased heat capacity is validated in transient thermal simulations and experiments. The half-bridge power module's electrical performance is evaluated for on -state resistance, switching performance, and switching loss at three junction temperature conditions. The proposed substrate solution has minimal impact on conduction and switching performance of SiC MOSFETs.
Pattern matching is a fundamental tool for answering complex graph queries. Unfortunately, existing solutions have limited capabilities: They do not scale to process large graphs and/or support only a restricted set of search templates or usage scenarios. Moreover, the algorithms at the core of the existing techniques are not suitable for today’s graph processing infrastructures relying on horizontal scalability and shared-nothing clusters, as most of these algorithms are inherently sequential and difficult to parallelize. In this article we present an algorithmic pipeline that bases pattern matching on constraint checking. The key intuition is that each vertex and edge participating in a match has to meet a set of constraints implicitly specified by the search template. These constraints can be verified independently and typically are less expensive to compute than searching the full template. The pipeline we propose generates these constraints and iterates over them to eliminate all the vertices and edges that do not participate in any match, thus reducing the background graph to a subgraph that is the union of all template matches—the complete set of all vertices and edges that participate in at least one match. Additional analysis can be performed on this annotated, reduced graph, such as full match enumeration, match counting, or computing vertex/edge centrality. Furthermore, a vertex-centric formulation for constraint checking algorithms exists, and this makes it possible to harness existing high-performance, vertex-centric graph processing frameworks. This technique (i) enables highly scalable pattern matching in metadata (labeled) graphs; (ii) supports arbitrary patterns with 100% precision; (iii) enables tradeoffs between precision and time-to-solution, while always selects all vertices and edges that participate in matches, thus offering 100% recall; and (iv) supports a set of popular data analytics scenarios. We implement our approach on top of HavoqGT, an open-source asynchronous graph processing framework, and demonstrate its advantages through strong and weak scaling experiments on massive scale real-world (up to 257 billion edges) and synthetic (up to 4.4 trillion edges) labeled graphs, respectively, and at scales (1,024 nodes / 36,864 cores), orders of magnitude larger than used in the past for similar problems. This article serves two purposes: First, it synthesises the knowledge accumulated during a long-term project. Second, it presents new system features, usage scenarios, optimizations, and comparisons with related work that strengthen the confidence that pattern matching based on iterative pruning via constraint checking is an effective and scalable approach in practice. The new contributions include the following: (i) We demonstrate the ability of the constraint checking approach to efficiently support two additional search scenarios that often emerge in practice, interactive incremental search and exploratory search. (ii) We empirically compare our solution with two additional state-of-the-art systems, Arabsque and TriAD. (iii) We show the ability of our solution to accommodate a more diverse range of datasets with varying properties, e.g., scale, skewness, label distribution, and match frequency. (iv) We introduce or extend a number of system features (e.g., work aggregation, load balancing, and the ability to cap the generated traffic) and design optimizations and demonstrate their advantages with respect to improving performance and scalability. (v) We present bottleneck analysis and insights into artifacts that influence performance. (vi) We present a theoretical complexity argument that motivates the performance gains we observe.
This project enhanced our theoretical capabilities geared towards establishing the basic science of a high-throughput protocol for the development of advanced nuclear fuel that should couple modern computational materials modeling and simulation tools, fabrication and characterization capabilities, and targeted high throughput performance testing experiments. The successful conclusion of this ER project allowed us to upgrade state-of-the-art modeling codes, and apply these modeling tools to ab initio energetics and thermodynamic assessments of phase diagrams of various mixtures of actinide alloys, propose a tool for optimizing composition of complex alloys for specific properties, predict diffusion behavior in diffusion couples made of actinide and transition metals, include one new equation in the LLNL phase-field AMPE code, and predict microstructure evolution during alloy coring. In FY11, despite limited funding, the team also initiated an experimental activity, with collaboration from Texas A&M University by preparing samples of nuclear fuels in bulk forms and for diffusion couple studies and metallic matrices, and performing preliminary characterization.
This report covers the technical progress on the program “Novel Modified Optical Fibers for High Temperature In-Situ Miniaturized Gas Sensors in Advanced Fossil Energy Systems”, funded by the National Energy Technology Laboratory of the U.S. Department of Energy, and performed by the Materials Science & Engineering and Electrical & Computer Engineering Departments at Virginia Tech, and summarizes technical progress from July 1st, 2005 –June 30th, 2014. The objective of this program was to develop novel fiber materials for high temperature gas sensors based on evanescent wave absorption in optical fibers. This project focused on two primary areas: the study of a sapphire photonic crystal fiber (SPCF) for operation at high temperature and long wavelengths, and a porous glass based fiber optic sensor for gas detection. The sapphire component of the project focused on the development of a sapphire photonic crystal fiber, modeling of the new structures, fabrication of the optimal structure, development of a long wavelength interrogation system, testing of the optical properties, and gas and temperature testing of the final sensor. The fabrication of the 6 rod SPCF gap bundle (diameter of 70μm) with a hollow core was successfully constructed with lead-in and lead-out 50μm diameter fiber along with transmission and gas detection testing. Testing of the sapphire photonic crystal fiber sensor capabilities with the developed long wavelength optical system showed the ability to detect CO 2 at or below 1000ppm at temperatures up to 1000°C. Work on the porous glass sensor focused on the development of a porous clad solid core optical fiber, a hollow core waveguide, gas detection capabilities at room and high temperature, simultaneous gas species detection, suitable joining technologies for the lead-in and lead-out fibers and the porous sensor, sensor system sensitivity improvement, signal processing improvement, relationship between pore structure and fiber geometry to optical properties, and the development of a sensor packaging prototype for laboratory testing. Analysis and experiments determined that a bonding technique using a CO 2 laser is the most suitable joining technique. Pore morphology alteration showed that transmission improved with increasing annealing temperature (producing smaller pores), while the sensor response time increased and the mechanical strength decreased with increasing annealing temperature. Software was developed for data acquisition and signal processing to collect and interpret spectral gas absorption data. Gas detection on porous glass sensors was completed and the detection limit was evaluated using acetylene and was found to be around 1- 200ppm. A complete materials package for porous glass sensors was manufactured for testing.
Large-scale underground hydrogen storage (UHS) is a critical component in the emerging hydrogen economy. Knowledge of multiphase flow behavior involving hydrogen in storage reservoir formations is crucial to characterizing hydrogen transport properties and essential for the deliverability and storage operations of UHS. There are still many gaps in fully understanding hydrogen–methane–brine multiphase phase flow that require further investigation. In this work, H 2 and CH 4 were injected through brine-saturated sandstone cores using a tri-axial core holder system fitted with flow rate meters and pressure transducers, while the effluent gas concentrations were analyzed using an online micro gas chromatograph. Brine displacement, permeability, and gas breakthrough curves were measured. We studied the flow behavior of hydrogen and methane in sandstone cores through testing brine displacement by gas injection and comparing the hydrogen displacement of methane with the methane displacement of hydrogen. We also tested the differences between horizontal and vertical flow in brine displacement. The results showed that brine displacement was more efficient in a core with higher permeability and porosity, resulting in a higher initial gas saturation. A higher gas injection rate brought about faster gas breakthrough measured by pore volume and sharper concentration curves. Hydrogen did not exhibit abnormal flow in the sandstone when the flow was horizontal and downward vertical. Gas overriding was observed in brine displacements when the flow was horizontal, with hydrogen showing this behavior more profoundly compared to methane. Downward vertical gas injection induced higher efficiency brine displacement compared to horizontal displacement and resulted in a higher initial gas saturation in the sandstone cores. These findings address critical knowledge gaps regarding gas flow patterns and displacement behaviors during hydrogen injection and recovery phases in UHS facilities using methane as the cushion gas. The insights from this research offer valuable guidance for optimizing UHS systems, ensuring operational efficiency, and advancing sustainable energy solutions in alignment with decarbonization goals.
This paper introduces the extreme theory of functional connections (X-TFC), a physics-informed machine learning algorithm, and tailors it to estimate the remaining useful life (RUL) of wind turbine gearbox bearings experiencing fatigue crack growth. Unlike purely data-driven methods, X-TFC embeds a physics model, based on Head's theory in this work, into its training objective. The core of X-TFC is a random-projection single-layer neural network trained via an extreme learning machine, which requires only limited damage progression data and solves for output weights with a least-squares optimization algorithm. A composite loss function balances the network's fit to observed degradation data against the residuals of the governing crack growth differential equation, ensuring the learned damage trajectory remains physically plausible. When applied to a vibration-based health-index (HI) dataset measured during the growth of a crack on the inner ring of a high-speed bearing in a wind turbine gearbox (Bechhoefer and Dubé, 2020), X-TFC achieves near-zero prediction bias. Even when trained on only the first 10 %–20 % of the damage progression data, with sufficient physics weighting its predictions remain monotonic and smooth, delivering high prognosability and trendability. To quantify the epistemic uncertainty, we employ a Monte Carlo ensemble of independently initialized X-TFC models trained on noise-perturbed data, which yields confidence intervals around each RUL estimate and captures both model-parameter and epistemic uncertainty. In addition to a vibration-based HI, we demonstrate that the proposed framework can be directly applied to a supervisory control and data acquisition (SCADA) data-based HI (Eftekhari Milani et al., 2026) measured during similar wind turbine gearbox bearing crack faults, preserving its accuracy and interpretability. This extension shows the versatility of our approach, which is applicable to bearings of multiple gearbox manufacturers, models, and ratings using only SCADA data. By integrating domain knowledge with machine learning, X-TFC offers a rapid, reliable tool for crack prognostics. Its adaptability to other bearing failure modes, such as pitch bearing ring cracks, positions X-TFC as a powerful enabler of data-driven, physics-informed asset management in the wind energy sector and beyond.
The International Criticality Safety Benchmark Evaluation Project (ICSBEP) has continued its work generating evaluations of new and historical benchmark experiments since the last update to the nuclear criticality safety (NCS) community at the 12th International Conference on Nuclear Criticality Conference held in 2023. One additional version of the ICSBEP Handbook has been published since that update, and the Technical Review Group (TRG) held two in-person meetings to review and approve additional benchmarks. The 2022 and 2023 editions of the handbook were combined into one release (published in November 2024) and contained 13 new evaluations with 46 different configurations and two major revisions to existing evaluations. The 2024 version of the handbook, currently under publication review, will contain two new evaluations with 15 new configurations and one major revision to HEU-MET-FAST-028, the evaluation of Flattop with a uranium core. The ICSBEP TRG met again in person in April 2025 to review benchmarks for the 2025 ICSBEP Handbook and final comment resolution is currently ongoing. Many of the new benchmarks represent contemporaneous experiments that have been specifically optimized to provide validation cases relevant to the NCS community. One major area of focus for new critical experiments is to target the sparsely populated intermediate energy (or resonance) region. Another focus of many of the new benchmarks is to provide experiments sensitive to different materials, such as chlorine, hafnium, tantalum, titanium, molybdenum, chromium, and polymethyl methacrylate (PMMA, or Lucite). The ICSBEP continues to deliver high-quality, peer reviewed evaluations of integral experiments relevant to the nuclear data community.
Lower hybrid current drive (LHCD) is a potential source of non-inductive off-axis current drive (CD) for tokamaks. Although LHCD has been successfully deployed on a number of tokamaks, it is highly sensitive to the scrape-off layer (SOL) conditions local to the LHCD launcher. Large gaps between the launcher and plasma core, SOL turbulence, or edge density perturbations due to edge-localized modes can hamper CD or cause large reflected power. These coupling issues in part motivated the installation of an LHCD launcher on the high-field side (HFS) of DIII-D. On the HFS, the SOL is less turbulent and more controllable compared to the low-field side. This quiescence may result in more predictable edge conditions and thus a more predictable CD. Here, in this work, HFS SOL reflectometry measurements are predicted from global plasma parameters using machine learning models. The SOL predictions coupled with the full-wave simulation of the LHCD launcher allow for the prediction of reflected power, directivity, and arcing risk before the discharge. Launcher performance is then optimized using multi-objective Bayesian optimization, finding the shot parameters that result in an optimal SOL density that maximizes CD while minimizing the risk of arcing. The predictions and optimizations of LHCD performance are then accelerated using a surrogate model of the full-wave LHCD simulation.
As part of our effort to discover drugs that target HIV-1 entry, we report the antiviral activity and crystal structures of two novel inhibitors in a complex with a gp120 core. NBD-14204 showed similar antiviral activity against all the clinical isolates tested. The IC50 values were in the range of 0.24–0.9 µM with an overall mean of 0.47 ± 0.03 µM, showing slightly better activity against the clinical isolates than against the lab-adapted HIV-1 HXB2 (IC 50 = 0.96 ± 0.1 µM). Moreover, the antiviral activity of NBD-14208 was less consistent, showing a wider range of IC 50 values (0.66–5.7 µM) with an overall mean of 3 ± 0.25 µM and better activity against subtypes B and D (Mean IC 50 2.2–2.5 µM) than the A, C and Rec viruses (Mean IC 50 2.9–3.9 µM). SI of NBD-14204 was about 10-fold higher than NBD-14208, making it a better lead compound for further optimization. In addition, we tested these compounds against S375Y and S375H mutants of gp120, which occurred in some clades and observed these to be sensitive to NBD-14204 and NBD-14208. These inhibitors also showed modest activity against HIV-1 reverse transcriptase. Furthermore, we determined the crystal structures of both inhibitors in complexes with gp120 cores. As expected, both NBD-14204 and NBD-14208 bind primarily within the Phe43 cavity. It is noteworthy that the electron density of the thiazole ring in both structures was poorly defined due to the flexibility of this scaffold, suggesting that these compounds maintain substantial entropy, even when bound to the Phe43 cavity.
Not provided.
Reduction kinetics and stability of 20 wt% Na 2 MoO 4 -promoted CaMn 0.9 Ti 0.1 O 3 were investigated for its applications in Chemical Looping – Oxidative Dehydrogenation (CL-ODH) of ethane, a potential alternative for ethylene production with higher efficiency and lower emissions. Here, the present work reports a kinetics model and parameters for a Na 2 MoO 4 -promoted, Ti-doped CaMnO 3 (CaMn 0.9 Ti 0.1 O 3 ) redox catalyst under H 2 and C 2 H 4 . A first-order reaction model provides the best fit for the reduction of Na 2 MoO 4 /CaMn 0.9 Ti 0.1 O 3 under H 2 , while the C 2 H 4 reduction is well described by an Avrami–Erofe’ev model. The activation energy for C 2 H 4 oxidation is approximately three times higher than that for H 2 conversion, showing that the activation of C 2 H 4 is significantly more difficult on the surface of the redox catalyst. The reduction rate of Na 2 MoO 4 /CaMn 0.9 Ti 0.1 O 3 under H 2 at 750 °C is more than two orders of magnitude greater than that under C 2 H 4 , while the reduction rate of unpromoted CaMn 0.9 Ti 0.1 O 3 is comparable under H 2 and C 2 H 4 , showing that the addition of Na 2 MoO 4 effectively suppresses C 2 H 4 combustion relative to H 2 oxidation. The kinetics results for Na 2 MoO 4 /CaMn 0.9 Ti 0.1 O 3 confirm its excellent selectivity towards hydrogen combustion, making it a promising candidate under CL-ODH. Additionally, the stability of the CaMn 0.9 Ti 0.1 O 3 @ Na 2 MoO 4 core-shell structure, which was the underlying reason for the excellent selectivity, was examined under both shallow and deep reductions. It was determined that deep reduction of the redox catalyst, e.g. higher than 80% solid conversion, would lead to loss of sodium and hence to decreased selectivity for hydrogen combustion. In contrast, the core-shell structure was well-maintained, exhibiting excellent performance after 50 redox cycles when deep reduction of the redox catalyst was avoided. This study offers a basis for both the CL-ODH reactor design and redox catalyst optimizations.
Diffusion Monte Carlo is the most accurate widely used Quantum Monte Carlo method for the electronic structure of materials, but it requires frequent load balancing or population redistribution steps to maintain efficiency and avoid accumulation of systematic errors on parallel machines. The load balancing step can be a significant factor affecting performance, and will become more important as the number of processing elements increases. We propose a new dynamic load balancing algorithm, the Alias Method, and evaluate it theoretically and empirically. An important feature of the new algorithm is that the load can be perfectly balanced with each process receiving at most one message. It is also optimal in the maximum size of messages received by any process. We also optimize its implementation to reduce network contention, a process facilitated by the low messaging requirement of the algorithm. Empirical results on the petaflop Cray XT Jaguar supercomputer at ORNL showing up to 30% improvement in performance on 120,000 cores. The load balancing algorithm may be straightforwardly implemented in existing codes. The algorithm may also be employed by any method with many near identical computational tasks that requires load balancing.