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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Tuning hydrogenation chemistry of Pd-based heterogeneous catalysts by introducing homogeneous-like ligands

Abstract Noble metals have been extensively employed in a variety of hydrotreating catalyst systems for their featured functionality of hydrogen activation but may also bring side reactions such as undesired deep hydrogenation. It is crucial to develop a viable approach to selectively inhibit side reactions while preserving beneficial functionalities. Herein, we present modifying Pd with alkenyl-type ligands that forms homogeneous-like Pd-alkene metallacycle structure on the heterogeneous Pd catalyst to achieve the selective hydrogenolysis and hydrogenation. Particularly, a doped alkenyl-type carbon ligand on Pd-Fe catalyst is demonstrated to donate electrons to Pd, creating an electron-rich environment that elongates the distance and weakens the electronic interaction between Pd and unsaturated C of the reactants/products to control the hydrogenation chemistry. Moreover, high H 2 activation capability is maintained over Pd and the activated H is transferred to Fe to facilitate C-O bond cleavage or directly participate in the reaction on Pd. The modified Pd-Fe catalyst displays comparable C-O bond cleavage rate but much higher selectivity (>90%) than the bare Pd-Fe (<50%) in hydrotreating of diphenyl ether (DPE, modelling the strongest C-O linkage in lignin) and enhanced ethene selectivity (>90%) in acetylene hydrogenation. This work sheds light on the controlled synthesis of selective hydrotreating catalysts via mimicking homogeneous analogues.

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

Mapping the cosmic gamma-ray horizon: the 1CGH catalogue of Fermi -LAT detections above 10 GeV

ABSTRACT We present the First Cosmic Gamma-ray Horizon (1CGH) catalogue, featuring $\gamma$-ray detections above 10 GeV based on 16 yr of observations with the Fermi Large Area Telescope (Fermi-LAT) satellite. After carefully selecting a sample of blazars and blazar candidates from catalogues in the literature, we performed a binned likelihood analysis and identified 2791 $\gamma$-ray emitters above 10 GeV, at >3$\sigma$ level, including 62 that are new $\gamma$-ray detections. For each source, we estimated the mean energy of the highest energy bin and analysed them in the context of the cosmic gamma-ray horizon. By adopting a reference model for the extragalactic background light (EBL), we identified a subsample of 525 sources where moderate to severe $\gamma$-ray absorption could be detected across the redshift range of 0–3. This work provides the most up-to-date compilation of detections above 10 GeV, along with their redshift information. We condense extensive results from the literature, including reports on observational campaigns dedicated to blazars and $\gamma$-ray sources, thereby delivering an unprecedented review of the redshift information for sources detected above 10 GeV. Additionally, we highlight key 1CGH sources where redshift information remains incomplete, offering guidance for future optical observation campaigns. The 1CGH catalogue aims to track the most significant sources for studying the $\gamma$-ray transparency of the Universe. Furthermore, it provides a targeted subsample where the EBL optical depth, $\tau _{(E,z)}$, can be robustly measured using Fermi-LAT data.

Arsioli, Bruno (ORCID:0000000181666602)↗
DOE OSTI2024

Catalyst design for small molecule activation of energy consequence

This project targets the conversion of ubiquitous small molecules (e.g. NO, CO, H 2 O) into viable precursors to synthetic fuels. Current state of the art catalyst design has not directly targeted transition metal complexes capable of mediating the multi-electron redox processes necessary to reduce the overpotential (energy loss) required achieve efficient activation of small molecule substrates. In this vein, a new strategy has been developed for the assembly of polynuclear architectures; allowing for the construction of tunable polymetallic centers that assemble easily within a pre-organized template (conferring stability, selectivity and tunability) that can effect multi-electron redox processes for reactions. Catalyst development has commenced with the following target design elements: (1) catalysts featuring multiple transition metal ions in the same reaction space to greatly expand accessible molecular redox capabilities; (2) catalysts are assembled in a polynucleating ligand framework that permits control over the cluster morphology as well as the local steric and electronic environment of the transition metal ions within the cluster. The high-tunability of the catalyst composition (metal content) and geometric flexibility has permitted a rigorous assessment of electronic-structure-to-function relationship to be developed, further guiding synthetic efforts to realize more potent catalysts. The numerous permutations possible showcase the high degree of generality to this approach with many synthetic handles to tune redox and reaction chemistry. Trinuclear complexes have been synthesized featuring homo- and hetero-trinuclear cores featuring a variety of first row transition metal ions (Cr→Ni). The molecular clusters have been shown to successfully mediate multi-electron redox processes in a cooperative fashion without requiring strong chemical reductants or oxidants. The reactive molecular complexes are being utilized to activate and breakdown the robust bonds within typical waste stream small molecules (e.g., greenhouse gases) and convert them into value-added commodity chemicals. Ultimately, the catalysts developed by this approach will be required to convert energy acquired via renewable resources (e.g., solar or wind) into synthetic fuels as an energy storage mechanism.

10 SYNTHETIC FUELS↗
DOE OSTI2023

Reducing the matrix effect in mass spectral imaging of biofilms using flow-cell culture

The interactions between soil microorganisms and soil minerals play a crucial role in the formation and evolution of minerals and the stability of soil aggregates. Due to the heterogeneity and diversity of the soil environment, the under-standing of the functions of bacterial biofilms in soil minerals at the microscale is limited. A soil mineral-bacterial biofilm system was used as a model in this study, and it was analyzed by time-of-flight secondary ion mass spectrometry (ToF-SIMS) to acquire molecular level information. Static culture in multi-wells and dynamic flow-cell culture in microfluidics of biofilms were investigated. Our results show that more characteristic molecules of biofilms can be observed in SIMS spectra of the flow-cell culture. In contrast, biofilm signature peaks are buried under the mineral components in SIMS spectra in the static culture case. Spectral overlay was used in peak selection prior to performing Principal component analysis (PCA). Comparisons of the PCA results between the static and flow-cell culture show more pronounced molecular features and higher loadings of organic peaks of the dynamic cultured specimens. For example, fatty acids secreted from bacterial biofilm extracellular polymeric substance are likely to be responsible for biofilm dispersal due to mineral treatment up to 48 h. Such findings suggest that the use of microfluidic cells to dynamically culture biofilms be a more suitable method for reducing the matrix effect arisen from the growth medium and minerals as a perturbation fac-tor for improved spectral and multivariate analysis of complex mass spectral data in ToF-SIMS. These results show that the interaction mechanism between biofilms and soil minerals at the molecular level can be better studied using the flow-cell culture and advanced mass spectral imaging techniques like ToF-SIMS.

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

BETO 2021 Peer Review - Inverse Bioproduct Design Through Machine Learning and Molecular Simulation

This work aims to identify performance advantaged bioproducts (PABPs) through property prediction, which will guide experimental synthesis. The impact of this work will be faster market adoption of bioproducts with greater performance relative to incumbent products. We have identified >106 bioproduct candidates, but only some will have superior performance to create a market pull. High-throughput property prediction, enabled by machine learning, and elucidation of structure-function relationships, enabled by molecular simulation, provide a hypothesis driven approach for down selection of candidate biomolecules to pursue experimentally. To enable machine learning and molecular simulation for bioproduct discovery, automated structure generation and embedding must capture relevant features for prediction, databases must cover domains applicable to biobased products, and best practices for simulation of polymer systems must be developed. To address these challenges, we have established bioproduct relevant datasets, developed high-throughput polymer structure generation, and built end-to-end neural networks that have predicted 8 properties for >1.4 x 106 biopolymers. A molecular simulation pipeline for building, running, and analyzing polymers and polymer additives is being used to predict performance and develop design principles of biobased products. In collaboration with the PABP synthesis project, these computational tools are guiding synthesis and informing design of PABPs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Tracking transactions using extended memory features

An approach is disclosed that tracks memory transactions by a node. The approach establishes a transaction processing state corresponding to common virtual addresses accessed by a processing threads. Transactions are executed by the threads. A selected transaction is allowed to complete. In response to detecting a conflict in the transaction processing state, completion of a non-selected transaction is inhibited.

Kahle, James Allan↗
DOE OSTI2025

Cross-reactive sarbecovirus antibodies induced by mosaic RBD nanoparticles

Broad immune responses are needed to mitigate viral evolution and escape. To induce antibodies against conserved receptor-binding domain (RBD) regions of SARS-like betacoronavirus (sarbecovirus) spike proteins that recognize SARS-CoV-2 variants of concern and zoonotic sarbecoviruses, we developed mosaic-8b RBD nanoparticles presenting eight sarbecovirus RBDs arranged randomly on a 60-mer nanoparticle. Mosaic-8b immunizations protected animals from challenges from viruses whose RBDs were matched or mismatched to those on nanoparticles. Here, we describe neutralizing mAbs isolated from mosaic-8b-immunized rabbits, some on par with Pemgarda, the only currently FDA-approved therapeutic mAb. Deep mutational scanning, in vitro selection of spike resistance mutations, and single-particle cryo-electron microscopy structures of spike–antibody complexes demonstrated targeting of conserved RBD epitopes. Rabbit mAbs included critical D-gene segment RBD-recognizing features in common with human anti-RBD mAbs, despite rabbit genomes lacking an equivalent human D-gene segment, thus demonstrating that the immune systems of humans and other mammals can utilize different antibody gene segments to arrive at similar modes of antigen recognition. These results suggest that animal models can be used to elicit anti-RBD mAbs with similar properties to those raised in humans, which can then be humanized for therapeutic use, and that mosaic RBD nanoparticle immunization coupled with multiplexed screening represents an efficient way to generate and select broadly cross-reactive therapeutic pan-sarbecovirus and pan-SARS-CoV-2 variant mAbs.

Science & Technology - Other Topics↗
DOE OSTI2020

Hierarchical, rotation‐equivariant neural networks to select structural models of protein complexes

Abstract Predicting the structure of multi‐protein complexes is a grand challenge in biochemistry, with major implications for basic science and drug discovery. Computational structure prediction methods generally leverage predefined structural features to distinguish accurate structural models from less accurate ones. This raises the question of whether it is possible to learn characteristics of accurate models directly from atomic coordinates of protein complexes, with no prior assumptions. Here we introduce a machine learning method that learns directly from the 3D positions of all atoms to identify accurate models of protein complexes, without using any precomputed physics‐inspired or statistical terms. Our neural network architecture combines multiple ingredients that together enable end‐to‐end learning from molecular structures containing tens of thousands of atoms: a point‐based representation of atoms, equivariance with respect to rotation and translation, local convolutions, and hierarchical subsampling operations. When used in combination with previously developed scoring functions, our network substantially improves the identification of accurate structural models among a large set of possible models. Our network can also be used to predict the accuracy of a given structural model in absolute terms. The architecture we present is readily applicable to other tasks involving learning on 3D structures of large atomic systems.

Eismann, Stephan↗
DOE OSTI2024

Quantitative analysis of three-dimensional fatigue crack path selection in Mg alloy WE43 using high-energy X-ray diffraction microscopy

Fatigue short-cracks in Mg alloys display complex growth behavior due to high plastic anisotropy and crack path dependence on local microstructural features. In this study, the three-dimensional crystallography of short-crack paths in Mg alloy WE43 was characterized by mapping near-field high-energy X-ray diffraction microscopy (HEDM) reconstructed grain maps to high-resolution X-ray CT reconstructions of the fracture surfaces in the crack initiation and short-crack growth regions of six ultrasonic fatigue specimens. Crack–grain–boundary intersections were analyzed at 81 locations across the six crack paths. The basal intragranular, non-basal intragranular, or intergranular character of short-crack growth following each boundary intersection was correlated to crystallographic and geometric parameters of the trailing and leading grains, three-dimensional grain boundary plane, and advancing crack front. The results indicate that crack paths are dependent on the combined crystallographic and geometric character of the local microstructure, and crack path prediction can be improved by use of dimensionality reduction on subsets of high-linear-correlation microstructural parameters.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Bragg edge imaging (BEI) of B12W and M8N socket sections of the Arecibo telescope

From neutron user principal investigator: We kindly request the public release of three neutron imaging datasets through ONCat. All datasets were collected from two forensic specimens, B12W and M8N, sectioned from zinc-filled steel-wire sockets recovered from the collapsed Arecibo Telescope. The dataset titled “Neutron radiographs of B12W and M8N socket sections of the Arecibo telescope” contains normalized two-dimensional (2D) neutron radiographs of the specimens, showing the geometry and spatial distribution of the steel wires embedded within the zinc matrix, as well as internal features such as voids and cracks. The dataset titled “Neutron computed tomography of B12W and M8N socket sections of the Arecibo telescope” contains normalized 2D neutron projection images acquired over a range of specimen rotation angles for one selected region of each specimen. These projection images were used to reconstruct three-dimensional (3D) tomographic volumes that reveal the embedded-wire geometry and internal defects. The dataset titled “Bragg edge imaging (BEI) of B12W and M8N socket sections of the Arecibo telescope” contains six time-of-flight (TOF) neutron imaging datasets, three from each specimen, acquired at regions of interest selected based on the radiographs. The spatially resolved 2D TOF images show the zinc matrix and embedded steel wires, and the wavelength-dependent neutron transmission data were used to characterize crystallographic texture within the zinc. All components and their condition are in the public domain as they are the property of the National Science Foundation (NSF). The neutron imaging data, part geometries, and detailed forensic information have been widely published in the Arecibo Telescope Collapse Forensic Report by Thornton Tomasetti Engineers and others (NASA report and NASEM report).

Bilheux, Hassina↗
DOE OSTI2023

Learning-based demand-supply-coupled charging station location problem for electric vehicle demand management

We present a learning-based, demand-supply-coupled optimization model for the charging station location problem (CSLP), aiming to integrate the concept of electric vehicle (EV) charging demand management into the planning of charging infrastructures. In stage one, a gradient boosting-based learning model is developed to predict the charging demand of a charging station based on 15 defined features. Next, in stage two, a demand–supply-coupled CSLP model is developed to optimize the total charging usage rates of both existing and newly selected charging stations. We design a gradient-based stochastic spatial search algorithm to solve the proposed model. A case study with 6-year charging event data from Kansas City Missouri is performed. Results show that the proposed method can generate satisfactory charging demand predictions, and can increase charging usage rates by 14%, outperforming two benchmark approaches. Furthermore, the results of this research are poised to guide agencies in identifying optimal locations for new charging stations.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Solution-phase sample-averaged single-particle spectroscopy of quantum emitters with femtosecond resolution

Here, the development of many optical quantum technologies depends on the availability of solid-state single quantum emitters with near-perfect optical coherence. However, a standing issue that limits systematic improvement is the significant sample heterogeneity and lack of mechanistic understanding of microscopic energy flow at the single emitter level and ultrafast timescales. Here we develop solution-phase single-particle pump-probe spectroscopy with photon correlation detection that captures sample-averaged dynamics in single molecules and/or defect states with unprecedented clarity at femtosecond resolution. We apply this technique to single quantum emitters in two-dimensional hexagonal boron nitride, which suffers from significant heterogeneity and low quantum efficiency. From millisecond to nanosecond timescales, the translation diffusion, metastable-state-related bunching shoulders, rotational dynamics, and antibunching features are disentangled by their distinct photon-correlation timescales, which collectively quantify the normalized two-photon emission quantum yield. Leveraging its femtosecond resolution, spectral selectivity and ultralow noise (two orders of magnitude improvement over solid-state methods), we visualize electron-phonon coupling in the time domain at the single defect level, and discover the acceleration of polaronic formation driven by multi-electron excitation. Corroborated with results from a theoretical polaron model, we show how this translates to sample-averaged photon fidelity characterization of cascaded emission efficiency and optical decoherence time. Our work provides a framework for ultrafast spectroscopy in single emitters, molecules, or defects prone to photoluminescence intermittency and heterogeneity, opening new avenues of extreme-scale characterization and synthetic improvements for quantum information applications.

36 MATERIALS SCIENCE↗
DOE OSTI2020

On the possibility of baryon acoustic oscillation measurements at redshift z > 7.6 with the Roman space telescope

ABSTRACT The Nancy Grace Roman Space Telescope (RST), with its field of view and high sensitivity will make surveys of cosmological large-scale structure possible at high redshifts. We investigate the possibility of detecting baryon acoustic oscillations (BAO) at redshifts z > 7.6 for use as a standard ruler. We use data from the hydrodynamic simulation bluetides in conjunction with the gigaparsec-scale Outer Rim simulation and a model for patchy reionization to create mock RST High Latitude Survey grism data for Lyman α emission line selected galaxies at redshifts z = 7.4 to z = 10, covering 2280 deg2. We measure the monopoles of galaxies in the mock catalogues and fit the BAO features. We find that for a line flux of $L = 7\times 10^{-17} \ {\rm erg\, s^{-1}\, cm}^{-2}$, the 5σ detection limit for the current design, the BAO feature is partially detectable (measured in three out of four survey quadrants analysed independently). The resulting root mean square error on the angular diameter distance to z = 7.7 is 7.9 ${{\ \rm per\ cent}}$. If we improve the detection sensitivity by a factor of two (i.e. $L = 3.5\times 10^{-17} \ {\rm erg\, s^{-1}\, cm}^{-2}$), the distance error reduces to $1.4{{\ \rm per\ cent}}$. We caution that many more factors are yet to be modelled, including dust obscuration, the damping wing due to the intergalactic medium, and low redshift interlopers. If these issues do not strongly affect the results, or different observational techniques (such as use of multiple lines) can mitigate them, RST, or similar instruments may be able to constrain the angular diameter distance to the high redshift universe.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Electronic spectroscopy and ionization potentials for YbOH and YbOCH 3

The polyatomic molecules YbOH and YbOCH 3 have been recognized as being of potential value for spectroscopic experiments that explore charge-parity and time reversal symmetry violation effects. These measurements require very high precision, which, in turn, will necessitate that the molecules be manipulated at ultra-cold temperatures. Both YbOH and YbOCH 3 have electronic transitions that appear suitable for laser cooling ($\tilde{A}$ 2 Π 1/2 -$\tilde{X}$ 2 Σ + and $\tilde{A}$2E 1/2 -$\tilde{X}$ 2 A 1 , respectively) but the currently available spectroscopic data is not sufficient to determine the extent to which population leaks may compromise the optical cooling processes. A further complication is that the quantum states of interest for these measurements will need to be selectively populated. The $\tilde{A}$-$\tilde{X}$ band systems of both YbOH and YbOCH 3 show evidence of vibronic perturbations, such that there are unassigned vibronic features at energies that are just above the origin bands. Here, in the present study we have recorded spectra for the $\tilde{A}$ 2 Π 1/2 -$\tilde{X}$ 2 Σ + transition of jet-cooled YbOD to facilitate the vibronic assignments. In addition, spectra for the $\tilde{B}$ 2 Σ + - $\tilde{X}$ 2 Σ + transition of YbOH were recorded, establishing the origin band at 20473.8 cm -1 . Previously, the reaction of Yb with CH 3 OH has been used to generate gas-phase YbOCH 3 . As this reaction also yields YbOH, there have been complications in spectroscopic studies of YbOCH 3 due to overlap of the $\tilde{A} -\tilde{X}$ band systems. To identify specific regions of overlap, resonantly enhanced two-photon ionization spectra were recorded using mass-resolved detection of the YbOH + and YbOCH 3 + ions. These data confirmed the overlap of vibronic bands near 17640 and 17680 cm -1 . Two-photon ionization spectroscopy also provided accurate ionization energies of IE(YbOH)=45788(10) and IE(YbOCH 3 )=45283(10) cm -1 . The IE for YbOH is relevant to problems encountered in previous attempts to determine the bond dissociation energy of YbOH + .

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

Interpretable machine learning-guided design of Fe-based soft magnetic alloys

Here, we present a machine learning (ML) guided approach to predict saturation magnetization (𝑀 S ) and coercivity (𝐻 C ) in Fe-rich soft magnetic alloys, particularly Fe-Si-B systems. ML models trained on experimental data reveal that increasing Si and B content reduces 𝑀 S from 1.81 T (DFT ≈ 2.04 T) to ≈1.54 T (DFT ≈ 1.56T) in Fe-Si-B, which is attributed to decreased magnetic density and structural modifications. Experimental validation of ML predicted magnetic saturation on Fe-1Si-1B (2.09 T), Fe-5Si-5B (2.01 T), and Fe-10Si-10B (1.54 T) alloy compositions further supports our findings. These trends are consistent with density functional theory predictions, which link increased electronic disorder and band broadening to lower 𝑀 S values. Experimental validation on selected alloys confirms the predictive accuracy of the ML model, with good agreement across compositions. Beyond predictive accuracy, detailed uncertainty quantification and model interpretability including through feature importance and partial dependence analysis reveal that 𝑀 S is governed by a nonlinear interplay between Fe content and early transition metal ratios, while 𝐻 C is more sensitive to processing conditions such as ribbon thickness and thermal treatment windows. The ML framework was further applied to Fe-Si-B/Cr/Cu/Zr/Nb alloys in a pseudoquaternary compositional space, which shows comparable magnetic properties to NANOMET (Fe 84.8 ⁢Si 0.5 ⁢B 9.4 ⁢Cu 0.8⁢ P 3.5 ⁢C 1 ), FINEMET (Fe 73.5 ⁢Si 13.5 ⁢B 9 Cu 1 ⁢Nb 3 ), NANOPERM (Fe 88 ⁢Zr 7⁢ B 4 ⁢Cu 1 ), and HITPERM (Fe 44 ⁢Co 44 ⁢Zr 7⁢ B 4 ⁢Cu 1 . Our findings demonstrate the potential of the ML framework for accelerated search of high-performance soft magnetic materials.

density functional theory↗
DOE OSTI2023

A Review of Visualization Methods for Cyber-Physical Security: Smart Grid Case Study

Cyber-Physical Systems (CPSs) are becoming increasingly complex and interconnected as they attempt to meet the demands of evolving society. As a result, monitoring and maintaining them becomes a more complex and demanding task for control system operators and cyber defenders. While the literature on visualization techniques in the context of cybersecurity is extensive, the same cannot be said for studies on visualization for the security of cyber-physical systems. This paper aims to fill that gap by: 1) defining the main features of a visualizations workflow for security visualizations in cyber-physical systems. The workflow includes the acquisition of cyber and physical data, processing of data, selection, and configuration of both visualization tools and end-user interactions. 2) Providing an overview of cyber-physical security visualization systems, with a focus on smart grids as a case study. Finally, we use the perspectives gained from this analysis to provide insights and directions for future research and design of cyber-physical visualization techniques.

24 POWER TRANSMISSION AND DISTRIBUTION↗