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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

High temperature elastic properties of sub-stoichiometric yttrium dihydrides

Yttrium hydrides are considered as candidate materials for neutron moderation applied in microreactors (akin transportable tiny nuclear reactors) owing to their superior thermal stability and hydrogen retention. The evolution of elastic properties of these materials at elevated temperatures, needed for predicting the thermomechanical response and performance of the moderator during in-service reactor conditions, however, is lacking. Here, we report the Young’s and shear elastic moduli of three stoichiometries of bulk yttrium hydride (YH x , x = 1.61, 1.82, and 1.84) from room temperature to 1000°C. In situ temperature-dependent measurements of the longitudinal and shear wave velocities were performed using a laser ultrasonic technique while heating the sample in a vacuum-pumped heating stage. The elastic moduli increased linearly with increasing hydrogen content and decreased by ~10% during heating from room temperature to 1000°C in the three YH x compositions. The linear relationship between the elastic moduli and the hydrogen content in yttrium hydride was verified by atomistic calculations based on density functional theory (DFT). The absence of abrupt changes in the temperature-dependent measurements of elastic modulus of the YH x samples suggested negligible loss of hydrogen at elevated temperatures. Excellent agreement was found between the measured and calculated dependence of the elastic moduli on the stoichiometry, thereby providing a new approach for investigating the effects of fabrication-induced parameters (such as porosity) on the elastic moduli. Furthermore, this study demonstrates the utility of the combined approach involving DFT-based atomistic calculations and measurements of the elastic moduli for the informative development of metal hydrides and can be used as a metric for novel moderator materials investigations for emerging microreactors and beyond.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

The Q weak high performance LH 2 target

A high-power liquid hydrogen target was built for the Jefferson Lab Q weak experiment, which measured the tiny parity-violating asymmetry in $\vec{e}$ p scattering at an incident energy of 1.16 GeV, and a Q 2 = 0.025 GeV 2 . To achieve the luminosity of 1.7 x 10 39 cm -2 s -1 , a 34.5 cm- long target was used with a beam current of 180 μA. The ionization energy-loss deposited by the beam in the target was 2.1 kW. The target temperature was controlled to within ±0.02 K and the target noise (density fluctuations) near the experiment's beam helicity- reversal rate of 960 Hz was only 53 ppm. The 58 liquid liter target achieved a head of 11.4 m (7.6 kPa) and a mass flow of 1.2 ± 0.3 kg/s (corresponding to a volume flow of 17.4 ± 3.8 l/s) at the nominal 29 Hz rotation frequency of the recirculating centrifugal pump. We describe aspects of the design, operation, and performance of this target, the highest power LH2 target ever used in an electron scattering experiment to date.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Omics-guided metabolic pathway discovery in plants: Resources, approaches, and opportunities

Plants produce a vast array of metabolites, the biosynthetic routes of which remain largely undetermined. Genome-scale enzyme and pathway annotations and omics technologies have revolutionized research to decrypt plant metabolism and produced a growing list of functionally characterized metabolic genes and pathways. However, what is known is still a tiny fraction of the metabolic capacity harbored by plants. Here, in this work, we review plant enzyme and pathway annotation resources and cutting-edge omics approaches to guide discovery and characterization of plant metabolic pathways. We also discuss strategies for improving enzyme function prediction by integrating protein 3D structure information and single cell omics. This review aims to serve as a primer for plant biologists to leverage omics datasets to facilitate understanding and engineering plant metabolism.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Quantum gravity phenomenology at the dawn of the multi-messenger era—A review

The exploration of the universe has recently entered a new era thanks to the multi-messenger paradigm, characterized by a continuous increase in the quantity and quality of experimental data that is obtained by the detection of the various cosmic messengers (photons, neutrinos, cosmic rays and gravitational waves) from numerous origins. They give us information about their sources in the universe and the properties of the intergalactic medium. Moreover, multi-messenger astronomy opens up the possibility to search for phenomenological signatures of quantum gravity. On the one hand, the most energetic events allow us to test our physical theories at energy regimes which are not directly accessible in accelerators; on the other hand, tiny effects in the propagation of very high energy particles could be amplified by cosmological distances. After decades of merely theoretical investigations, the possibility of obtaining phenomenological indications of Planck-scale effects is a revolutionary step in the quest for a quantum theory of gravity, but it requires cooperation between different communities of physicists (both theoretical and experimental). This review, prepared within the COST Action CA18108 “Quantum gravity phenomenology in the multi-messenger approach”, is aimed at promoting this cooperation by giving a state-of-the art account of the interdisciplinary expertise that is needed in the effective search of quantum gravity footprints in the production, propagation and detection of cosmic messengers.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Selective deuteration of an RNA:RNA complex for structural analysis using small-angle scattering

The structures of RNA:RNA complexes regulate many biological processes. Despite their importance, protein-free RNA:RNA complexes represent a tiny fraction of experimentally determined structures. Here, we describe a joint small-angle X-ray and neutron scattering (SAXS/SANS) approach to structurally interrogate conformational changes in a model RNA:RNA complex. Using SAXS, we measured the solution structures of the individual RNAs and of the overall RNA:RNA complex. With SANS, we demonstrate, as a proof of principle, that isotope labeling and contrast matching (CM) can be combined to probe the bound state structure of an RNA within a selectively deuterated RNA:RNA complex. Furthermore, we show that experimental scattering data can validate and improve predicted AlphaFold 3 RNA:RNA complex structures to reflect its solution structure. In conclusion, our work demonstrates that in silico modeling, SAXS, and CM-SANS can be used in concert to directly analyze conformational changes within RNAs when in complex, enhancing our understanding of RNA structure in functional assemblies.

HIV-1 dimerization initiation site↗
DOE OSTI2021

A seamless multiscale operator neural network for inferring bubble dynamics

Modelling multiscale systems from nanoscale to macroscale requires the use of atomistic and continuum methods and, correspondingly, different computer codes. Here, we develop a seamless method based on DeepONet, which is a composite deep neural network (a branch and a trunk network) for regressing operators. In particular, we consider bubble growth dynamics, and we model tiny bubbles of initial size from 100 nm to 10 $\mathrm {\mu }\textrm {m}$ , modelled by the Rayleigh–Plesset equation in the continuum regime above 1 $\mathrm {\mu }\textrm {m}$ and the dissipative particle dynamics method for bubbles below 1 $\mathrm {\mu }\textrm {m}$ in the atomistic regime. After an offline training based on data from both regimes, DeepONet can make accurate predictions of bubble growth on-the-fly (within a fraction of a second) across four orders of magnitude difference in spatial scales and two orders of magnitude in temporal scales. The framework of DeepONet is general and can be used for unifying physical models of different scales in diverse multiscale applications.

Mechanics↗
DOE OSTI2025

Scanning Electrochemical Microscopy: An Evolving Toolbox for Revealing the Chemistry within Electrochemical Processes

The parallel development of ultramicroelectrodes (UMEs) and groundbreaking scanning probe microscopy techniques in the late 1980s led to the development of the scanning electrochemical microscope. Scanning electrochemical microscopy (SECM) was born from the idea of using a tiny electrode to measure the local electrochemical behavior at operating electrodes. From its foundations, the technique displayed an inherent versatility in measuring sample properties beyond topography. It allowed experimenters to measure and map chemical reactions occurring at diverse interfaces, from inspecting the reversibility of redox mediators at metal electrodes, to detecting the hallmarks of cellular respiration on living plant leaves. Related but distinct electrochemical scanning probe techniques, such as electrochemical atomic force microscopy (EC-AFM), scanning ion conductance microscopy (SICM), and scanning electrochemical cell microscopy (SECCM) have developed in parallel. These techniques have demonstrated exquisite spatial resolution down to the nanoscale regime. However, it is the proposition of this review that SECM remains unmatched at revealing the chemical aspects of electrochemistry. Furthermore, it is our intention to review and demonstrate that the versatile architecture of SECM continues to evolve and address fundamental and emerging challenges in the fields of energy storage and conversion, chemical biology, materials science, and environmental chemistry, among others.

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

Quick-and-Easy Validation of Protein–Ligand Binding Models Using Fragment-Based Semiempirical Quantum Chemistry

Electronic structure calculations in enzymes converge very slowly with respect to the size of the model region that is described using quantum mechanics (QM), requiring hundreds of atoms to obtain converged results and exhibiting substantial sensitivity (at least in smaller models) to which amino acids are included in the QM region. As such, there is considerable interest in developing automated procedures to construct a QM model region based on well-defined criteria. However, testing such procedures is burdensome due to the cost of large-scale electronic structure calculations. Here, we show that semiempirical methods can be used as alternatives to density functional theory (DFT) to assess convergence in sequences of models generated by various automated protocols. The cost of these convergence tests is reduced even further by means of a many-body expansion. We use this approach to examine convergence (with respect to model size) of protein–ligand binding energies. Fragment-based semiempirical calculations afford well-converged interaction energies in a tiny fraction of the cost required for DFT calculations. Two-body interactions between the ligand and single-residue amino acid fragments afford a low-cost way to construct a “QM-informed” enzyme model of reduced size, furnishing an automatable active-site model-building procedure. This provides a streamlined, user-friendly approach for constructing ligand binding-site models that needs neither a priori information nor manual adjustments. Extension to model-building for thermochemical calculations should be straightforward.

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

The Singlet–Triplet Gap of Cyclobutadiene: The CIPSI-Driven CC( P ; Q ) Study

An accurate determination of singlet−triplet gaps in biradicals, including cyclobutadiene in the automerization barrier region where one has to balance the substantial nondynamical many-electron correlation effects characterizing the singlet ground state with the predominantly dynamical correlations of the lowest-energy triplet, remains a challenge for many quantum chemistry methods. High-level coupled-cluster (CC) approaches, such as the CC method with a full treatment of singly, doubly, and triply excited clusters (CCSDT), are often capable of providing reliable results, but routine application of such methods is hindered by their high computational costs. We have recently proposed a practical alternative to converging the CCSDT energetics at small fractions of the computational effort, even when electron correlations become stronger and connected triply excited clusters are larger and nonperturbative, by merging the CC(P;Q) moment expansions with the selected configuration interaction methodology abbreviated as CIPSI. We demonstrate that one can accurately approximate the highly accurate CCSDT potential surfaces characterizing the lowest singlet and triplet states of cyclobutadiene along the automerization coordinate and the gap between them using tiny fractions of triply excited cluster amplitudes identified with the help of relatively inexpensive CIPSI Hamiltonian diagonalizations.

Basis sets↗
DOE OSTI2023

Enhancing Nanoparticle Detection in Interferometric Scattering (iSCAT) Microscopy Using a Mask R-CNN

Interferometric scattering microscopy (iSCAT) is a label-free optical microscopy technique that enables imaging of individual nano-objects such as nanoparticles, viruses, and proteins. Essential to this technique is the suppression of background scattering and identification of signals from nano-objects. In the presence of substrates with high roughness, scattering heterogeneities in the background, when coupled with tiny stage movements, cause features in the background to be manifested in background-suppressed iSCAT images. Traditional computer vision algorithms detect these background features as particles, limiting the accuracy of object detection in iSCAT experiments. Here, in this paper, we present a pathway to improve particle detection in such situations using supervised machine learning via a mask region-based convolutional neural network (mask R-CNN). Using a model iSCAT experiment of 19.2 nm gold nanoparticles adsorbing to a rough layer-by-layer polyelectrolyte film, we develop a method to generate labeled datasets using experimental background images and simulated particle signals and train the mask R-CNN using limited computational resources via transfer learning. We then compare the performance of the mask R-CNN trained with and without inclusion of experimental backgrounds in the dataset against that of a traditional computer vision object detection algorithm, Haar-like feature detection, by analyzing data from the model experiment. Results demonstrate that including representative backgrounds in training datasets improved the mask R-CNN in differentiating between background and particle signals and elevated performance by markedly reducing false positives. The methodology for creating a labeled dataset with representative experimental backgrounds and simulated signals facilitates the application of machine learning in iSCAT experiments with strong background scattering and thus provides a useful workflow for future researchers to improve their image processing capabilities.

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

Energetics and Structure of Nickel Atoms and Nanoparticles on MgO(100)

The growth morphology and interfacial energetics of vapor deposited Ni on the MgO(100) surface at 300 and 100 K have been studied using single crystal adsorption calorimetry (SCAC), He + low-energy ion scattering spectroscopy (LEIS), X-ray photoelectron spectroscopy (XPS), and low-energy electron diffraction (LEED). At 300 K, the Ni atoms grow as three-dimensional nanoparticles with a saturation number density of 5 × 10 16 particles/m 2 . The differential heat of adsorption at 300 K increases rapidly with coverage, from 276 (initially) to 311 kJ/mol by 0.4 ML. Thereafter, it slowly increases asymptotically to the sublimation enthalpy of bulk Ni (430 kJ/ml) by 9 ML. The Ni 2p 3/2 XPS peak binding energy at 300 K is initially (i.e., at 0.16 ML) 1.4 eV higher than that for bulk Ni(solid), but it decreases to that value at high coverage. The Ni atoms form a metastable phase at 300 K when in nanoparticles with diameter <2.5 nm, and the adhesion energy of such Ni nanoparticles to MgO(100) was found to be 3.05 J/m 2 . At 100 K, the Ni atoms form single adatoms and then 0.17 nm thick 2D islands at low coverage with fewer Ni-Ni bonds compared to the Ni nanoparticles formed at 300 K. Thus, the initial heat (i.e., for the first ~0.03 ML) is 148 kJ/mol at 100 K, 128 kJ/mol lower than at 300 K, and remains lower for the 2D islands. With increasing coverage at 100 K, the tiny 2D Ni islands grow in size to cover nearly the entire surface before thickening. Lastly, the XPS Ni 2p 3/2 peak binding energy for 0.21 ML Ni on MgO(100) at 100 K is 2.2 eV higher than that for bulk Ni(solid), suggesting charge transfer from Ni to MgO(100) and formation of Ni 2+ at very low coverage.

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

CO 2 Hydrogenation to Methanol over Inverse ZrO 2 /Cu(111) Catalysts: The Fate of Methoxy under Dry and Wet Conditions

Understanding the surface chemistry of CH 3 O species is essential for the production of methanol by CO 2 hydrogenation over Cu-based heterogeneous catalysts, as it facilitates the rational design of more efficient conversion processes. Recent research has identified inverse ZrO 2 /Cu catalysts as highly active and selective systems for the transformation of CO 2 to methanol with a performance that can be better than that of commercial Cu/ZnO catalysts. Here, we employed synchrotron-based ambient pressure X-ray photoelectron spectroscopy (AP-XPS) and calculations based on density functional theory (DFT) to understand the fate of CH 3 O groups under dry and wet environments. AP-XPS spectra revealed that under CO 2 hydrogenation conditions, formate and methoxy are two key intermediates to produce methanol. Furthermore, there are three different types of reactive sites on the surface: One is active for methoxy adsorption, which is stable and responsible for the methanol synthesis; Another one transforms CO 2 into CO; and a third one is active for CO 2 and methoxy dissociation, leading to C and methane formation. The theoretical calculations indicate that CH 3 OH readily dissociates to CH 3 O species following a highly exothermic (ΔE = -20.99 kcal/mol) and barrierless process. The water produced by the reverse water-gas shift reaction (CO 2 + H 2 → H 2 O + CO) can prevent the decomposition of CH 3 O species. We discovered that by introducing a tiny amount of water vapor (2 × 10 -6 Torr) into the reaction chamber, the energy barrier for the reaction CH 3 O(ads) + H(ads) → CH 3 OH(gas) is dramatically reduced. AP-XPS and computational modelling showed that water is quite capable of extracting adsorbed methoxy to form gaseous methanol. With this in mind, one could boost the methanol selectivity by adding appropriate amounts of water or steam, which is an inexpensive and feasible solution for industrial operations.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Monte Carlo Explicitly Correlated Second-Order Many-Body Green’s Function Calculations of Semiconductor Band Gaps

A systematically converging series of ab initio, post-density-functional, size-consistent, electron-correlated approximations is desired for predictive computing of felectronic band structures of insulating, semiconducting, and metallic solids. A series that meets all of these desiderata (except the applicability to metals) is ab initio many-body Green's function theory based on Gaussian-type-orbital (GTO) basis sets. Here, its leading-order approximation, the second-order Green's function (GF2) method in the diagonal and frequency-independent approximations with the aug-cc-pVDZ basis set, is applied to the fundamental band gaps of three semiconductors (diamond, silicon, and silicon carbide in the zincblende structure) using cluster models. Corrections are made to the basis-set-incompleteness errors by the explicit-correlation (F12) ansatz (GF2-F12) for the valence band edges. The crystals are modeled as surface-passivated clusters of increasing sizes, whose wave functions are expanded by up to 2709 GTO basis functions. Immense computational costs of these calculations are overcome by the highly scalable stochastic algorithm of the Monte Carlo GF2-F12 method, whose operation cost per state increases only as a cubic power of system size, which has a tiny memory footprint and easily achieves near-perfect parallel efficiency on thousands of CPUs or on hundreds of GPUs. The correlated, F12-corrected highest-occupied and lowest-unoccupied molecular-orbital energy (HOMO-LUMO) gap is 5.78 ± 0.07 eV for C 87 H 76 as compared with the experimental value of the fundamental (indirect) band gap of bulk diamond at 5.48 eV. The correlated, F12-corrected HOMO-LUMO gaps for Si 75 H 76 and Si 32 C 43 H 76 are 2.56 ± 0.15 eV and 3.50 ± 0.12 eV, respectively, which are expected to decrease further with increasing cluster sizes. As a result, the experimental fundamental (indirect) band gaps of bulk silicon and silicon carbide are 1.17 eV and 2.42 eV, respectively.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

DeFault: DEep‐Learning‐Based FAULT Delineation Using the IBDP Passive Seismic Data at the Decatur CO2 Storage Site

Abstract The carbon capture, utilization, and storage (CCUS) framework is an essential component in reducing greenhouse gas emissions, with its success hinging on the comprehensive knowledge of subsurface geology and geomechanics. Passive seismic event relocation and fault detection offer vital insights into subsurface structures and the ability to monitor fluid migration pathways. Accurate identification and localization of seismic events, however, face significant challenges, including the necessity for high‐quality seismic data and advanced computational methods. To address these challenges, we introduce a novel deep learning method, , specifically designed for passive seismic source relocation and fault delineating for passive seismic monitoring projects. By leveraging data domain‐adaptation, allows us to train a neural network with labeled synthetic data and apply it directly to field data. Using , the passive seismic sources are automatically clustered based on their recording time and spatial locations, and subsequently, faults and fractures are delineated accordingly. We demonstrate the efficacy of on a field case study involving injection related microseismic data from Decatur, Illinois area. Our approach accurately and efficiently relocated passive seismic events, identified faults and could aid in potential damage induced by seismicity. Our results highlight the potential of as a valuable tool for passive seismic monitoring, emphasizing its role in ensuring CCUS project safety. This research bolsters the understanding of subsurface characterization in CCUS, illustrating machine learning’s capacity to refine these methods. Ultimately, our work has significant implications for CCUS technology deployment, an essential strategy in combating climate change. Plain Language Summary In our quest to tackle climate change, we use a strategy known as carbon capture, utilization, and storage (CCUS) to keep greenhouse gases out of the atmosphere. This strategy relies heavily on our ability to understand what's happening deep under the earth's surface. To make sure we store super critical safely, we need to accurately map out the geological structure, especially faults, but this is tough without high‐quality data and complex computer programs. We've developed a new tool called “DeFault,” which uses advanced machine learning to improve how we find and map these underground features. “DeFault” is smart enough to learn from numerically simulated data and then apply what it’s learned to real‐world situations. It groups together seismic activity—tiny tremors and shifts in the earth—based on when and where they happen, which helps us spot where there might be cracks or faults. We tested “DeFault” in Illinois, where CO 2 is injected underground, and it successfully pinpointed where these tremors occurred and mapped out the faults, helping to prevent accidents accurately in the future. Our study shows that “DeFault” will be a powerful ally in making CCUS safer and more effective, especially for the Illinois Basin Decatur Project. Key Points Faults and fractures introduced by carbon storage can be monitored by passive seismicity DeFault algorithm enables an automatic process for accurate and efficient passive seismic event locating and clustering

58 GEOSCIENCES↗
DOE OSTI2021

Isolated copper–tin atomic interfaces tuning electrocatalytic CO 2 conversion

Direct experimental observations of the interface structure can provide vital insights into heterogeneous catalysis. Examples of interface design based on single atom and surface science are, however, extremely rare. Here, we report Cu–Sn single-atom surface alloys, where isolated Sn sites with high surface densities (up to 8%) are anchored on the Cu host, for efficient electrocatalytic CO 2 reduction. The unique geometric and electronic structure of the Cu–Sn surface alloys (Cu 97 Sn 3 and Cu 99 Sn 1 ) enables distinct catalytic selectivity from pure Cu 100 and Cu 70 Sn 30 bulk alloy. The Cu 97 Sn 3 catalyst achieves a CO Faradaic efficiency of 98% at a tiny overpotential of 30 mV in an alkaline flow cell, where a high CO current density of 100 mA cm –2 is obtained at an overpotential of 340 mV. Density functional theory simulation reveals that it is not only the elemental composition that dictates the electrocatalytic reactivity of Cu–Sn alloys; the local coordination environment of atomically dispersed, isolated Cu–Sn bonding plays the most critical role.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Mechanism for fluctuating pair density wave

Abstract In weakly coupled BCS superconductors, only electrons within a tiny energy window around the Fermi energy, E F , form Cooper pairs. This may not be the case in strong coupling superconductors such as cuprates, FeSe, SrTiO 3 or cold atom condensates where the pairing scale, E B , becomes comparable or even larger than E F . In cuprates, for example, a plausible candidate for the pseudogap state at low doping is a fluctuating pair density wave, but no microscopic model has yet been found which supports such a state. In this work, we write an analytically solvable model to examine pairing phases in the strongly coupled regime and in the presence of anisotropic interactions. Already for moderate coupling we find an unusual finite temperature phase, below an instability temperature T i , where local pair correlations have non-zero center-of-mass momentum but lack long-range order. At low temperature, this fluctuating pair density wave can condense either to a uniform d -wave superconductor or the widely postulated pair-density wave phase depending on the interaction strength. Our minimal model offers a unified framework to understand the emergence of both fluctuating and long range pair density waves in realistic systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗