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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Unraveling the CO Oxidation Mechanism over Highly Dispersed Pt Single Atom on Anatase TiO 2 (101)

Catalysts with noble metals deposited as single atoms on metal oxide supports have recently been studied extensively due to their maximized metal utilization and potential for performing difficult chemical conversions owing to their unique electronic properties. Understanding of the reaction mechanisms on supported single-metal atoms is still limited but is highly important for designing more efficient catalysts. In this study, we report the complexity of the CO oxidation reaction mechanism on Pt single atoms supported on anatase TiO 2 (Pt SA /a-TiO 2 ) by coupling density functional theory (DFT) calculations and microkinetic analysis with kinetic measurements, in situ/operando infrared, and X-ray absorption spectroscopies. Starting from the adsorbed Pt SA occupying an O vacancy induced by reductive pretreatment, we show that CO oxidation follows a complex mechanism consisting of initiation steps to reorganize the active site and multibranch reactive cycles, with the Pt SA /a-TiO 2 catalyst not returning to its initial configuration. The initiation step consists of CO and O 2 adsorption healing the O vacancy, followed by CO oxidation using gas-phase CO to form Pt(CO). The reactive cycle alternates O 2 adsorption and dissociation to oxidize the catalyst to Pt(O)(O)(CO) and branching pathways of competing Langmuir–Hinshelwood (LH)- or Eley–Rideal (ER)-type CO oxidation steps to reduce it again to Pt(CO). In situ/operando infrared experiments, including cryogenic CO adsorption and isotopic CO exchange, confirm the combined involvement of strongly adsorbed CO and gas-phase CO in an Eley–Rideal step along the reaction cycle. Microkinetic modeling shows that Pt single atoms are present in a mixture of Pt(CO), Pt(CO)(O 2 ), Pt(O)(CO)(O 2 ), and Pt(CO)(CO 3 ) structures as the main intermediates during steady-state CO oxidation, all having the C–O vibrational stretch close to the experimentally observed value of 2115 cm –1 . Microkinetic modeling also shows that the fractional orders of CO and O 2 measured experimentally originate from multiple steps with a high degree of rate control and not from a simple competitive adsorption. The results demonstrate the complex reaction pathways that even CO oxidation on a simple single-atom system can follow, providing mechanistic insights for designing efficient Pt-based single-atom catalysts. Here, we further show that microkinetic modeling results are sensitive to changes in energies of intermediate and transition states within errors of density functional theory, which can ultimately lead to incorrect conclusions regarding the reaction pathways and most abundant reaction intermediates if not accounted for by experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A physics-informed multi-agents model to predict thermo-oxidative/hydrolytic aging of elastomers

This paper introduces a novel physics-informed multi-agents constitutive model to propose prediction in quasi-static constitutive behavior of cross-linked elastomer and the loss of mechanical performance during environmental aging. The presented model is used to simulate the effect of single-mechanism chemical aging (i.e. thermal-inducedor hydrolytic aging) on the behavior of the material in this hybrid framework. Those environmental single-mechanism damages change the polymer matrix over time due to massive chain scission, chain formations, and changing the arrangement of molecules in the polymer matrix. Here we propose a data-driven super-constrained machine-learned engine to represent damage in the polymer matrix and capture the changes in material behavior, including its inelastic features such as Mullins effect and permanent set in the course of aging. We have simplified the 3D stress–strain tensor mapping problem into a small number of super-constrained 1D mapping problems by means of a sequential order reduction. An assembly of multiple replicated conditional neural-network learning-agents (L-agents) is trained to systematically simplify the high-dimensional mapping problem into multiple 1D problems, each represented by a different type of agent. Our hybrid framework is designed to capture the effect of deformation history, aging time, and aging temperature. The model is validated with respect to a comprehensive set of experiments specifically designed to benchmark model capabilities and also against available data in the literature. Thermodynamic consistency and frame independency have been verified. Besides acceptable predictive abilities, a significant reduction of computational cost to predict behavior at multiple states of deformation is the most significant feature of this model.

42 ENGINEERING↗
DOE OSTI2025

High-energy X-ray radiography with apodized petawatt laser beams

We have produced laser wakefield-accelerated electron beams with energies of 2 GeV in Target Area 1 of the ZEUS facility. These electron beams oscillate in the accelerating structure that is produced in the wake of an ultrashort laser pulse (25 fs). These ’betatron’ oscillations result in the emission of a collimated (4 mrad divergence) beam of high-energy X-ray/gamma photons (on the order of several hundred keV). In this work, we demonstrate the capabilities of the ZEUS laser system by showing that apodizing the laser beam in the near field allows for the production of high-quality betatron X-rays for radiography purposes. This demonstrates that a high-power, large-aperture laser beam could be split into multiple parts to drive betatron X-ray sources from multiple angles or temporal delays into a High Energy Density (HED) target to be probed, enabling pump-probe measurements at multiple temporal delays or tomography on a single shot.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Constructing Crown Ether-Based Supramolecular Adhesives with Ambient-Temperature Applicable and Durable Adhesion

Supramolecular adhesives are attracting considerable attentions owing to their dynamic and reversible bonding, while the complex curing conditions and poor performance under extreme conditions severely restrict their applicability. Herein, a series of tetra-crown ether-terminated polyethylene glycol (TCE-PEG) adhesives are presented in which plenty of supramolecular interactions, including Van der Waals, hydrogen bonding, π–π staking, metal coordination, and electrostatic ones, provide strong adhesion on multiple types of substrates. Highly relevant for practical applications, ultra-strong adhesivity of TCE-PEG can be achieved without requiring additional ultraviolet radiation, heat, or chemical treatment at ambient temperature. It also exhibits durable adhesive behavior, excellent underwater stability, and low-temperature tolerance. Especially, by incorporating ionic polymer fragments of polyethyleneimine hydrochloride, additional hydrogen bonding between secondary amines and crown ether rings can be induced to enhance the lap-shear strength to 7.21 MPa due to the formation of supramolecular cross-linked network. Finally, this work pioneers a rather unique supramolecular approach for easily formed, ultra-strong crown-ether-based adhesives bearing great potential for applications in critical environments of limited heat source and unallowed solvent usage.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Monitoring covariance in multivariate time series: Comparing machine learning and statistical approaches

Abstract In complex systems with multiple variables monitored at high‐frequency, variables are not only temporally autocorrelated, but they may also be nonlinearly related or exhibit nonstationarity as the inputs or operation changes. One approach to handling such variables is to detrend them prior to monitoring and then apply control charts that assume independence and stationarity to the residuals. Monitoring controlled systems is even more challenging because the control strategy seeks to maintain variables at prespecified mean levels, and to compensate, correlations among variables may change, making monitoring the covariance essential. In this paper, a vector autoregressive model (VAR) is compared with a multivariate random forest (MRF) and a neural network (NN) for detrending multivariate time series prior to monitoring the covariance of the residuals using a multivariate exponentially weighted moving average (MEWMA) control chart. Machine learning models have an advantage when the data's structure is unknown or may change. We design a novel simulation study with nonlinear, nonstationary, and autocorrelated data to compare the different detrending models and subsequent covariance monitoring. The machine learning models have superior performance for nonlinear and strongly autocorrelated data and similar performance for linear data. An illustration with data from a reverse osmosis process is given.

Weix, Derek↗
DOE OSTI2022

Latticed-Confined Conversion Chemistry of Battery Electrode

The electrochemical conversion reaction, usually featured by multiple redox processes and high specific capacity, holds great promise in developing high energy rechargeable battery technologies. However, the complete structural change accompanied by spontaneous atomic migration and volume variation during the charge/discharge cycle leads to electrode disintegration and performance degradation, therefore severely restricting the application of conventional conversion-type electrodes. Herein, latticed-confined conversion chemistry is proposed, where the “intercalation-like” redox behavior is realized on the electrode with a “conversion-like” high capacity. By delicately formulating the high-entropy compounds, the pristine crystal structure can be preserved by the inert lattice framework, thus enabling an ultra-high initial Coulombic efficiency of 92.5% and a long cycling lifespan over a thousand cycles after the quasistatic charge–discharge cycle. This lattice-confined conversion chemistry unfolds a ubiquitous insight into the localized redox reaction and sheds light on developing high-performance electrodes toward next-generation high-energy rechargeable batteries.

25 ENERGY STORAGE↗
DOE OSTI2021

Search for exotic decays of the Higgs boson into long-lived particles in pp collisions at $\sqrt{s}$ = 13 TeV using displaced vertices in the ATLAS inner detector

A novel search for exotic decays of the Higgs boson into pairs of long-lived neutral particles, each decaying into a bottom quark pair, is performed using 139 fb -1 of $\sqrt{s}$ = 13 TeV proton-proton collision data collected with the ATLAS detector at the LHC. Events consistent with the production of a Higgs boson in association with a leptonically decaying Z boson are analysed. Long-lived particle (LLP) decays are reconstructed from inner-detector tracks as displaced vertices with high mass and track multiplicity relative to Standard Model processes. The analysis selection requires the presence of at least two displaced vertices, effectively suppressing Standard Model backgrounds. The residual background contribution is estimated using a data-driven technique. No excess over Standard Model predictions is observed, and upper limits are set on the branching ratio of the Higgs boson to LLPs. Branching ratios above 10% are excluded at 95% confidence level for LLP mean proper lifetimes cτ as small as 4 mm and as large as 100 mm. For LLP masses below 40 GeV, these results represent the most stringent constraint in this lifetime regime.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Precision test of the muon-Higgs coupling at a high-energy muon collider

We explore the sensitivity of directly testing the muon-Higgs coupling at a high-energy muon collider. This is strongly motivated if there exists new physics that is not aligned with the Standard Model Yukawa interactions which are responsible for the fermion mass generation. We illustrate a few such examples for physics beyond the Standard Model. With the accidentally small value of the muon Yukawa coupling and its subtle role in the high-energy production of multiple (vector and Higgs) bosons, we show that it is possible to measure the muon-Higgs coupling to an accuracy of ten percent for a 10 TeV muon collider and a few percent for a 30 TeV machine by utilizing the three boson production, potentially sensitive to a new physics scale about Λ ~ 30 - 100 TeV.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Large-eddy simulation study on cycle-to-cycle variation of knocking combustion in a spark-ignition engine

The cycle-to-cycle variation in the knock intensity is commonly encountered under abnormal combustion conditions. The severity of these abnormal combustion events can vary significantly, and the efficiency of engines at high loads is limited in practice by heavy knocking phenomena. Since, a thorough analysis of such recurrent but non-cyclic phenomena via experiments alone becomes highly cumbersome, in the present work, a multi-cycle large-eddy simulation study was performed to quantitatively predict cyclic variability in the combustion process and cyclic knock intensity variability in a direct injection spark-ignition engine. To account for the turbulence-chemistry interaction effects on flame propagation, the G-equation combustion model was used. Detailed chemistry was solved outside the flame front with a toluene primary reference fuel skeletal kinetic mechanism. For both the mild knock and heavy knock conditions, the numerical results were validated against experimental measurements. Based on the simulation results, a correlation analysis was performed considering combustion phasing, peak cylinder pressure and maximum amplitude of pressure oscillation. Furthermore, a detailed three-dimensional spatial analysis illustrated the evolution of auto-ignition kernel development and propagation of pressure waves during knocking combustion for three typical cycles with different knock intensities. In this process, it was found that an early occurrence of auto-ignition in the end gas was prone to high knock intensity. Although multiple auto-ignition kernels were observed in different cycles, the degree of coupling between chemical heat release and pressure waves varied, thereby leading to different maximum amplitude of pressure oscillation values.

42 ENGINEERING↗
DOE OSTI2024

Efficient distributed continual learning for steering experiments in real-time

Deep learning has emerged as a powerful method for extracting valuable information from large volumes of data. However, when new training data arrives continuously (i.e., is not fully available from the beginning), incremental training suffers from catastrophic forgetting (i.e., new patterns are reinforced at the expense of previously acquired knowledge). Training from scratch each time new training data becomes available would result in extremely long training times and massive data accumulation. Rehearsal-based continual learning has shown promise for addressing the catastrophic forgetting challenge, but research to date has not addressed performance and scalability. To fill this gap, we propose an approach based on a distributed rehearsal buffer that efficiently complements data-parallel training on multiple GPUs to achieve high accuracy, short runtime, and scalability. It leverages a set of buffers (local to each GPU) and uses several asynchronous techniques for updating these local buffers in an embarrassingly parallel fashion, all while handling the communication overheads necessary to augment input minibatches using unbiased, global sampling. We further propose a generalization of rehearsal buffers to support both classification and generative learning tasks, as well as more advanced rehearsal strategies (notably Dark Experience Replay, leveraging knowledge distillation). We illustrate this approach with a real-life HPC streaming application from the domain of ptychographic image reconstruction. Furthermore, we run extensive experiments on up to 128 GPUs of the ThetaGPU supercomputer to compare our approach with baselines representative of training-from-scratch (the upper bound in terms of accuracy) and incremental training (the lower bound). Results show that rehearsal-based continual learning achieves a top-5 validation accuracy close to the upper bound, while simultaneously exhibiting a runtime close to the lower bound.

Asynchronous data management↗
DOE OSTI2026

Modeling heat pipe startup and noncondensable gases in Sockeye

For this work, a one-dimensional gas mixture flow model was developed and implemented in the heat pipe code Sockeye to model the effects of noncondensable gases. Additionally, a startup model based on the dusty gas model was implemented to model the transition from rarefied gas dynamics to continuum flow, which occurs during the frozen startup of high-temperature heat pipes. Multiple startup and noncondensable gas models were tested against experimental data for sodium heat pipes, showing excellent agreement. Additionally, the newly developed gas mixture model for modeling noncondensable gas is further tested with a theoretical case study with arbitrary heating configurations. Finally, several recommendations and conclusions are made from the studies in this work to guide future heat pipe modeling efforts.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Building Atomic and Plasmonic Devices via Electron Beams: from Desired Structures to Desired Properties

The evolution of the aberration correction in Scanning Transmission Electron Microscopy (STEM) has enabled multiple advances including high-resolution structural imaging, pm-level position detection of atomic columns, and ~3-5 mV resolution electron energy loss spectroscopy of the chemical and physical functionalities. These developments have further enabled the precise manipulation of the atomic structure of materials, including removal and formation of atomic planes in 3D materials, controlled atomic motion, and even direct atomic assembly of homo- and heteroatomic artificial molecules. Here, we will discuss several recent advances in STEM-EELS automated experiments as applied to quantum and nanoplasmonic systems.

42 ENGINEERING↗
DOE OSTI2021

On the Use of Aqueous Metal-Aqua p K a Values as a Descriptor of Lewis Acidity

The behavior of Lewis acidic metal ions in multimetallic systems has become a subject of intense interest in recent years. Parametrizing the behavior of these ions in non-aqueous conditions, commonly used in the field, is challenging due to the lack of direct measures of the Lewis acidity of metal ions in polar organic solvents. Here, we report the use of triphenylphosphine oxide (TPPO) as a 31 P nuclear magnetic resonance (NMR) probe to quantify the Lewis acidity of a library of metal triflate salts using the Gutmann-Beckett method. Plots of the pK a values of the corresponding metal-aqua species, [M(H 2 O) m ] n+ , measured in H 2 O vs. the 31 P NMR shifts of TPPO in the presence of these metals in deuterated acetonitrile (d3-MeCN) and deuterated dichloromethane (CD 2 Cl 2 ) display tightly co-linear relationships, suggesting similar behavior for these ions in water, d 3 -MeCN, and CD 2 Cl 2 . This collinearity reinforces the utility of the common approach of using the aqueous pK a values as a descriptor of Lewis acidity, regardless of the solvent used in the immediate experiments, and provides an insight into the usefulness of this descriptor in wide-ranging applications. Titration studies in d 3 -MeCN suggest 1:1 binding of TPPO with monovalent ions, greater than 1:1 binding with divalent ions, and formation of multiple species with the highly Lewis acidic trivalent ions. Altogether, these data suggest that both aqueous pK a values and other single-measurement descriptors, while useful, provide only a snapshot of the influence of Lewis acidity on multimetallic chemical systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Pairing a Global Optimization Algorithm with EXAFS to Characterize Lanthanide Structure in Solution

Ensemble-average sampling of structures from ab initio molecular dynamics (AIMD) simulations can be used to predict theoretical extended X-ray absorption fine structure (EXAFS) signals that closely match experimental spectra. However, AIMD simulations are time-consuming and resource-intensive, particularly for solvated lanthanide ions, which often form multiple nonrigid geometries with high coordination numbers. Here, to accelerate the characterization of lanthanide structures in solution, we employed the Northwest Potential Energy Surface Search Engine (NWPEsSe), an adaptive-learning global optimization algorithm, to efficiently screen first-shell structures. As case studies, we examine two systems: Eu(NO 3 ) 3 dissolved in acetonitrile with a terpyridine ligand (terpyNO 2 ), and Nd(NO 3 ) 3 dissolved in acetonitrile. The theoretical spectra for structures identified by NWPEsSe were compared to both experimental and AIMD-derived EXAFS spectra. The NWPEsSe algorithm successfully identified the proper solvation structure for both Eu(NO 3 ) 3 (terpyNO 2 ) and Nd(NO 3 )(acetonitrile) 3 , with the calculated EXAFS signals closely matching the experimental spectra for the Eu-ligand complex and showing good similarity for the Nd salt; the better agreement with the ligand-containing structure is attributed to a less dynamic coordination environment due to the rigid ligand. The key advantage of the global optimization algorithm lies in its ability to sample the coordination environment across the potential energy surface and reduce the time required to identify structures from generally a month to within a week. Additionally, this approach is versatile and can be adapted to characterize main-group metal complexes.

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

Direct Extraction of Uniform Lignin Microspheres from Bamboo Using a Polyol-Based Deep Eutectic Solvent

Conventional preparation of lignin micro-/nano-particles (LMNPs) often needs multiple steps accompanied by high cost, low efficiency, and low yield. Herein, this study established a polyhydric alcohol-based deep eutectic solvent (PA-DES) fractionation for direct LMNP assembly from raw lignocellulose. The results indicated that lignin was significantly extracted by 41.55–70.33% depending on the PA-DES varieties. With lignin removal, the substrate showed high glucan enzymatic saccharification from 70.44 to 100%. Specially, over 90% lignin could be recovered in each PA-DES, and the obtained LMNPs showed quite uniform spherical morphology with a tunable size of 550–3254 nm and narrow polydispersity index (PDI) of 0.295–0.398. The proposed DES showed a near theoretical recovery yield, which still featured excellent fractionation performance even after three cycles of reuse. In conclusion, the findings highlight the simultaneous utilization of cellulose and lignin in a one-pot biomass fractionation under mild conditions with sugars and LMNPs as the coproducts.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A single nanobody neutralizes multiple epochally evolving human noroviruses by modulating capsid plasticity

Acute gastroenteritis caused by human noroviruses (HuNoVs) is a significant global health and economic burden and is without licensed vaccines or antiviral drugs. The GII.4 HuNoV causes most epidemics worldwide. This virus undergoes epochal evolution with periodic emergence of variants with new antigenic profiles and altered specificity for histo-blood group antigens (HBGA), the determinants of cell attachment and susceptibility, hampering the development of immunotherapeutics. Here, we show that a llama-derived nanobody M4 neutralizes multiple GII.4 variants with high potency in human intestinal enteroids. The crystal structure of M4 complexed with the protruding domain of the GII.4 capsid protein VP1 revealed a conserved epitope, away from the HBGA binding site, fully accessible only when VP1 transitions to a “raised” conformation in the capsid. Together with dynamic light scattering and electron microscopy of the GII.4 VLPs, our studies suggest a mechanism in which M4 accesses the epitope by altering the conformational dynamics of the capsid and triggering its disassembly to neutralize GII.4 infection.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Drosophila Evi5 is a critical regulator of intracellular iron transport via transferrin and ferritin interactions

Abstract Vesicular transport is essential for delivering cargo to intracellular destinations. Evi5 is a Rab11-GTPase-activating protein involved in endosome recycling. In humans, Evi5 is a high-risk locus for multiple sclerosis, a debilitating disease that also presents with excess iron in the CNS. In insects, the prothoracic gland (PG) requires entry of extracellular iron to synthesize steroidogenic enzyme cofactors. The mechanism of peripheral iron uptake in insect cells remains controversial. We show that Evi5-depletion in the Drosophila PG affected vesicle morphology and density, blocked endosome recycling and impaired trafficking of transferrin-1, thus disrupting heme synthesis due to reduced cellular iron concentrations. We show that ferritin delivers iron to the PG as well, and interacts physically with Evi5. Further, ferritin-injection rescued developmental delays associated with Evi5-depletion. To summarize, our findings show that Evi5 is critical for intracellular iron trafficking via transferrin-1 and ferritin, and implicate altered iron homeostasis in the etiology of multiple sclerosis.

59 BASIC BIOLOGICAL SCIENCES↗