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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 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
NASA NTRS1981

Effect of wind turbine generator model and siting on wind power changes out of large WECS arrays

Methods of reducing the WECS generation change through selection of the wind turbine model for each site, selection of an appropriate siting configuration, and wind array controls are discussed. An analysis of wind generation change from an echelon and a farm for passage of a thunderstorm is presented. Reduction of the wind generation change over ten minutes is shown to reduce the increase in spinning reserve, unloadable generation and load following requirements on unit commitment when significant WECS generation is present and the farm penetration constraint is satisfied. Controls on the blade pitch angle of all wind turbines in an array or a battery control are shown to reduce both the wind generation change out of an array and the effective farm penetration in anticipation of a storm so that the farm penetration constraint may be satisfied.

Schleuter, R. A.↗
NASA NTRSDate not supplied

Approximating the Lidar Ratio of Modeled Aerosol Species to Help Inform Lidar Ratio Selection for Marine Aerosols in the Forthcoming Version 5 CALIPSO Data Products

- After 16+ years, the NASA Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission will soon retire. - Narrow window of opportunity exists to improve quality of CALIPSO aerosol retrievals by advancing knowledge of aerosol lidar ratios for different aerosol types; - For CALIOP, the elastic backscatter lidar aboard CALIPSO, an aerosol lidar ratio (i.e., extinction-to-backscatter ratio) is generally assumed to retrieve vertical profiles of aerosol extinction and subsequently column-integrated aerosol optical depth (AOD). - Current CALIPSO lidar ratio selection process uses a single lidar ratio for each of the 7 CALIPSO tropospheric aerosol types, with large uncertainties for most aerosol types. Goal of this study: creation of regional and seasonal climatological lidar ratio maps for marine aerosols by leveraging passive aerosol retrievals (MODIS) and global model simulations (GEOS/GOGART).

Travis D Toth↗
DOE OSTI2022

Highly Efficient Organosulfur and Lithium‐Metal Hosts Enabled by C@Fe 3 N Sponge

Abstract Lithium‐organosulfur (Li‐OS) batteries, despite possessing high theoretical specific capacity, encounter a few practical challenges, including unsatisfactory lifespan and low active material utilization under realistic conditions. Here, diisoropyl xanthogen polysulfide (DIXPS) has been selected as a model organosulfur compound to investigate the practical feasibility of Li‐OS batteries under realistic conditions. A well‐designed freestanding carbon sponge decorated with Fe 3 N nanoparticles (C@Fe 3 N) is introduced into the Li‐OS cells as a scaffold for both Li and DIXPS. The lithiophilic property of the C@Fe 3 N host guides uniform lithium deposition at the anode, and the catalysis of the DIXPS conversion reaction promotes the kinetics at the cathode. Impressively, the synergistic effect of C@Fe 3 N leads to an extremely stable cycling performance over 1 000 cycles in a Li‐OS full cell under realistic conditions.

He, Jiarui↗
DOE OSTI2024

“Off‐Label Use” of the Siderophore Enterobactin Enables Targeted Imaging of Cancer with Radioactive Ti (IV)

Abstract The development of inert, biocompatible chelation methods is required to harness the emerging positron emitting radionuclide 45 Ti for radiopharmaceutical applications. Herein, we evaluate the Ti (IV) ‐coordination chemistry of four catechol‐based, hexacoordinate chelators using synthetic, structural, computational, and radiochemical approaches. The siderophore enterobactin (Ent) and its synthetic mimic TREN‐CAM readily form mononuclear Ti (IV) species in aqueous solution at neutral pH. Radiolabeling studies reveal that Ent and TREN‐CAM form mononuclear complexes with the short‐lived, positron‐emitting radionuclide 45 Ti (IV) , and do not transchelate to plasma proteins in vitro and exhibit rapid renal clearance in naïve mice. These features guide efforts to target the 45 Ti isotope to prostate cancer tissue through the design, synthesis, and evaluation of Ent‐DUPA, a small molecule conjugate composed of a prostate specific membrane antigen (PSMA) targeting peptide and a monofunctionalized Ent scaffold. The [ 45 Ti][Ti(Ent‐DUPA)] 2− complex forms readily at room temperature. In a tumor xenograft model in mice, selective tumor tissue accumulation (8±5 %, n =5), and low off‐target uptake in other organs is observed. Overall, this work demonstrates targeted imaging with 45 Ti (IV) , provides a foundation for advancing the application of 45 Ti in nuclear medicine, and reveals that Ent can be repurposed as a 45 Ti‐complexing cargo for targeted nuclear imaging applications.

Koller, Angus J.↗
DOE OSTI2022

Highly Efficient Organosulfur and Lithium-Metal Hosts Enabled by C@Fe 3 N Sponge

Lithium-organosulfur (Li-OS) batteries, despite possessing high theoretical specific capacity, encounter a few practical challenges, including unsatisfactory lifespan and low active material utilization under realistic conditions. Here, diisoropyl xanthogen polysulfide (DIXPS) has been selected as a model organosulfur compound to investigate the practical feasibility of Li-OS batteries under realistic conditions. A well-designed freestanding carbon sponge decorated with Fe 3 N nanoparticles (C@Fe 3 N) is introduced into the Li-OS cells as a scaffold for both Li and DIXPS. The lithiophilic property of the C@Fe 3 N host guides uniform lithium deposition at the anode, and the catalysis of the DIXPS conversion reaction promotes the kinetics at the cathode. Impressively, the synergistic effect of C@Fe 3 N leads to an extremely stable cycling performance over 1000 cycles in a Li-OS full cell under realistic conditions.

25 ENERGY STORAGE↗
DOE OSTI2024

“Off‐Label Use” of the Siderophore Enterobactin Enables Targeted Imaging of Cancer with Radioactive Ti (IV)

The development of inert, biocompatible chelation methods is required to harness the emerging positron emitting radionuclide 45 Ti for radiopharmaceutical applications. Herein, we evaluate the Ti (IV) -coordination chemistry of four catechol-based, hexacoordinate chelators using synthetic, structural, computational, and radiochemical approaches. The siderophore enterobactin (Ent) and its synthetic mimic TREN-CAM readily form mononuclear Ti (IV) species in aqueous solution at neutral pH. Radiolabeling studies reveal that Ent and TREN-CAM form mononuclear complexes with the short-lived, positron-emitting radionuclide 45 Ti (IV) , and do not transchelate to plasma proteins in vitro and exhibit rapid renal clearance in naïve mice. These features guide efforts to target the 45 Ti isotope to prostate cancer tissue through the design, synthesis, and evaluation of Ent-DUPA, a small molecule conjugate composed of a prostate specific membrane antigen (PSMA) targeting peptide and a monofunctionalized Ent scaffold. Additionally, the [ 45 Ti][Ti(Ent-DUPA)] 2− complex forms readily at room temperature. In a tumor xenograft model in mice, selective tumor tissue accumulation (8±5 %, n=5), and low off-target uptake in other organs is observed. Overall, this work demonstrates targeted imaging with 45 Ti (IV) , provides a foundation for advancing the application of 45 Ti in nuclear medicine, and reveals that Ent can be repurposed as a 45 Ti-complexing cargo for targeted nuclear imaging applications.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

Brain‐age prediction: Systematic evaluation of site effects, and sample age range and size

Abstract Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain‐age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain‐age has highlighted the need for robust and publicly available brain‐age models pre‐trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain‐age model. Here we expand this work to develop, empirically validate, and disseminate a pre‐trained brain‐age model to cover most of the human lifespan. To achieve this, we selected the best‐performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain‐age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5–90 years; 53.59% female). The pre‐trained models were tested for cross‐dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8–80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9–25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age‐bins (5–40 and 40–90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain‐age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain ( https://centilebrain.org/#/brainAGE2 ), an open‐science, web‐based platform for individualized neuroimaging metrics.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Effects of stereoisomeric structure and bond location on the ignition and reaction pathways of hexenes

The current work presents new experimental autoignition and speciation data on the two cis-hexene isomers: cis-2-hexene and cis-3-hexene. The new data provide insights on the effects of carbon-carbon double bond location and stereoisomeric structures on ignition delay times and reaction pathways for linear hexene isomers. Experiments were performed using the University of Michigan rapid compression facility to determine ignition delay times from pressure-time histories. Stoichiometric (Φ = 1.0) mixtures at dilution levels of inert gas to O 2 = 7.5:1 (mole basis) were investigated at an average pressure of 11 atm and temperatures from 809 to 1052 K. Speciation experiments were conducted at T = 900 K for the two cis-hexene isomers, where fast-gas sampling and gas chromatography were used to identify and quantify the two cis-hexene isomers and stable intermediate species. The ignition delay time data showed negligible sensitivity to the location of the carbon-carbon double bond and the stereoisomeric structure (cis-trans), and the species data showed no correlation with the stereoisomeric structure, but there was a strong correlation of some of the measured species with the location of the double bond in the hexene isomer. In particular, 2-hexene showed strong selectivity to propene, acetaldehyde, and 1,3-butadiene, and 3-hexene showed selectivity to propanal. Model predictions of ignition delay times were in excellent agreement with the experimental data. Furthermore, there was generally good agreement for the model predictions of the species data for 2-hexene; however, the mechanism overpredicted some of the small aldehyde (C 2 -C 4 ) species for 3-hexene. Reaction pathway analysis indicates the hexenes are almost exclusively consumed by H-atom abstraction reactions at the conditions studied (P = 11 atm, T > 900 K), and not by C 3 -C 4 scission as observed in high-temperature (>1300 K) hexene ignition studies. Improved estimates for 3-hexene + OH reactions may improve model predictions for the species measured in this work.

42 ENGINEERING↗
DOE OSTI2022

Universally Accessible Structural Data on Macromolecular Conformation, Assembly, and Dynamics by Small Angle X-Ray Scattering for DNA Repair Insights.

Structures provide a critical breakthrough step for biological analyses, and small angle X-ray scattering (SAXS) is a powerful structural technique to study dynamic DNA repair proteins. As toxic and mutagenic repair intermediates need to be prevented from inadvertently harming the cell, DNA repair proteins often chaperone these intermediates through dynamic conformations, coordinated assemblies, and allosteric regulation. By measuring structural conformations in solution for both proteins, DNA, RNA, and their complexes, SAXS provides insight into initial DNA damage recognition, mechanisms for validation of their substrate, and pathway regulation. Here, we describe exemplary SAXS analyses of a DNA damage response protein spanning from what can be derived directly from the data to obtaining super resolution through the use of SAXS selection of atomic models. We outline strategies and tactics for practical SAXS data collection and analysis. Making these structural experiments in reach of any basic and clinical researchers who have protein, SAXS data can readily be collected at government-funded synchrotrons, typically at no cost for academic researchers. In addition to discussing how SAXS complements and enhances cryo-electron microscopy, X-ray crystallography, NMR, and computational modeling, we furthermore discuss taking advantage of recent advances in protein structure prediction in combination with SAXS analysis.

Chinnam, Naga Babu↗
DOE OSTI2023

Understanding of three different polyvinylpyrrolidone (PVP) based battery binders blends on graphene surfaces from first principles via DFT simulations

Binders play a crucial role in binding electrodes, current collectors, and conductive agents during battery manufacturing. The selection of the binders and their blends considerably affect various properties such as mechanical durability, adhesion performance, ionic/electronic conductivities, and solid electrolyte interface stability for lithium-ion batteries. In this work, polyvinylidene di-fluoride (PVDF), polyacrylic acid (PAA), polyvinylpyrrolidone (PVP), and lithiated polyacrylic acid (Li-PAA) were selected as the model binders, and their blends were studied on graphene surfaces. Further, among them, PVDF is hardly soluble in water unlike the other binders, so in practice, the blend of PVP:PVDF is not thought to be a proper combination. However, the PVP:PVDF binder has also been included because the purpose of this work is to calculate several electronic properties such as binding energies, intermolecular interaction energies, bond critical points, electron density, and the Laplacian of electron density at critical bonding regions of all these blends at the molecular level via density functional theory (DFT) simulations in order to evaluate and compare how is the interaction strength and bonding type of three different lithium-ion batteries (LIBs) binder blends on graphene surfaces. Most stable binder pairs and their binding mechanisms on graphene surfaces were studied. The delocalization of lithium in Li-PAA was studied with the presence of water.

25 ENERGY STORAGE↗
DOE OSTI2023

Heterogenization of Homogeneous Ruthenium(II) Catalysts for Carbon-Neutral Dehydrogenation of Polyalcohols

Liquid organic hydrogen carrier (LOHC) systems are an excellent alternative to pressurized gas and liquid hydrogen storage technologies due to their high volumetric storage capacities and straightforward adaptation to existing infrastructure. Homogeneous catalysts are promising for the selective and reversible release of hydrogen from LOHC. However, separation from product mixtures and recycling inhibit their use, particularly when comprised of costly low-abundance elements, motivating the development of heterogeneous versions that are more easily recovered and reused. Here, we describe two methods for the heterogenization of molecular Ru catalysts that efficiently dehydrogenate the polyalcohol LOHCs ethylene glycol (EG) and 1,2-propanediol (1,2-PDO). The heterogeneous versions of these catalysts maintain catalytic activity for hydrogen production comparable to the homogeneous complexes, with up to 81% conversion and 99% selectivity. Further, DFT modeling indicates mechanistic similarities for the dehydrogenations of EG and 1,2-PDO, with the rate-limiting steps associated with protonation of the Ru–H bond to form H 2 and the alkoxide species coordinated to Ru(II), followed by β-hydride elimination to regenerate the Ru–H bond. Overall, the data suggest these heterogenized molecular catalysts have potential for practical use in polyalcohol-based LOHC systems.

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

Splitting Mono- and Di-Branched Alkane Isomers by a Robust Aluminum-Based Metal-Organic Framework Material with Optimal Pore Dimensions

The separation of alkanes with different degrees of branching, particularly mono- and dibranched isomers, represents a challenging yet important industrial process for the production of premium gasoline blending components with high octane number. We report here the separation of linear/monobranched and dibranched alkanes through complete molecular sieving by a robust aluminum-based MOF material, Al-bttotb (H 3 bttotb = 4,4',4"-(benzene-1,3,5-triyltris(oxy))tribenzoicacid). Single- and multicomponent adsorption experiments reveal that the material adsorbs linear and monobranched alkanes, but fully excludes their dibranched isomers. Adsorption sites of alkanes within the MOF channels have been identified by single-crystal X-ray diffraction studies, and the adsorption mechanism was explored through computational calculations and modeling. The highly selective adsorption of the MOF should be attributed to its optimal pore dimensions.

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

Towards accurate prediction of configurational disorder properties in materials using graph neural networks

Abstract The prediction of configurational disorder properties, such as configurational entropy and order-disorder phase transition temperature, of compound materials relies on efficient and accurate evaluations of configurational energies. Previous cluster expansion methods are not applicable to configurationally-complex material systems, including those with atomic distortions and long-range orders. In this work, we propose to leverage the versatile expressive capabilities of graph neural networks (GNNs) for efficient evaluations of configurational energies and present a workflow combining attention-based GNNs and Monte Carlo simulations to calculate the disorder properties. Using the dataset of face-centered tetragonal gold copper without and with local atomic distortions as an example, we demonstrate that the proposed data-driven framework enables the prediction of phase transition temperatures close to experimental values. We also elucidate that the variance of the energy deviations among configurations controls the prediction accuracy of disorder properties and can be used as the target loss function when training and selecting the GNN models. The work serves as a fundamental step toward a data-driven paradigm for the accelerated design of configurationally-complex functional material systems.

Chemistry↗
DOE OSTI2021

Machine learning assisted prediction of the Young’s modulus of compositionally complex alloys

We identify compositionally complex alloys (CCAs) that offer exceptional mechanical properties for elevated temperature applications by employing machine learning (ML) in conjunction with rapid synthesis and testing of alloys for validation to accelerate alloy design. The advantages of this approach are scalability, rapidity, and reasonably accurate predictions. ML tools were implemented to predict Young’s modulus of refractory-based CCAs by employing different ML models. Our results, in conjunction with experimental validation, suggest that average valence electron concentration, the difference in atomic radius, a geometrical parameter λ and melting temperature of the alloys are the key features that determine the Young’s modulus of CCAs and refractory-based CCAs. The Gradient Boosting model provided the best predictive capabilities (mean absolute error of 6.15 GPa) among the models studied. Our approach integrates high-quality validation data from experiments, literature data for training machine-learning models, and feature selection based on physical insights. It opens a new avenue to optimize the desired materials property for different engineering applications.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Temperature-mediated molecular ladder self-assembly employing Diels–Alder cycloaddition

Dynamic covalent self-assembly processes often exhibit poor capacities for error-correction owing to the relatively low connectivity rearrangement rates of dynamic covalent interactions and the common use of reaction conditions where the equilibrium state remains fixed. Here, we report a dynamic covalent self-assembly technique employing temperature, a conventional, externally-applied stimulus, to mediate the hybridization of peptoid oligomers bearing maleimide- and furan-based pendant groups to afford molecular ladders incorporating Diels–Alder adduct-based rungs. By raising or lowing the reaction temperature, this system enables the equilibrium state to be readily varied without altering reagent concentrations. Both triethylamine and the Lewis acidic scandium triflate were examined as candidate reaction catalysts; however, only scandium triflate increased the rate of single strand conversion. Additionally, as the Diels–Alder cycloaddition reaction does not liberate a small molecule, a registry-dependent mass change was effected by employing a base-catalyzed thiol-Michael addition reaction between any un-reacted maleimide pendant groups and a low molecular weight thiol to enable the number of Diels–Alder adduct rungs to be readily determined by mass spectrometry. Finally, by employing a slow temperature ramp from high to low temperature, approximating the thermal cycle employed for nucleic acid hybridization, sequence-selective hybridization between model, tetra-functional precursor strands was demonstrated.

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

Computational catalyst discovery: Active classification through myopic multiscale sampling

We report the recent boom in computational chemistry has enabled several projects aimed at discovering useful materials or catalysts. We acknowledge and address two recurring issues in the field of computational catalyst discovery. First, calculating macro-scale catalyst properties is not straightforward when using ensembles of atomic-scale calculations [e.g., density functional theory (DFT)]. We attempt to address this issue by creating a multi-scale model that estimates bulk catalyst activity using adsorption energy predictions from both DFT and machine learning models. The second issue is that many catalyst discovery efforts seek to optimize catalyst properties, but optimization is an inherently exploitative objective that is in tension with the explorative nature of early-stage discovery projects. In other words, why invest so much time finding a “best” catalyst when it is likely to fail for some other, unforeseen problem? We address this issue by relaxing the catalyst discovery goal into a classification problem: “What is the set of catalysts that is worth testing experimentally?” Here, we present a catalyst discovery method called myopic multiscale sampling, which combines multiscale modeling with automated selection of DFT calculations. It is an active classification strategy that seeks to classify catalysts as “worth investigating” or “not worth investigating” experimentally. Our results show an ~7–16 times speedup in catalyst classification relative to random sampling. These results were based on offline simulations of our algorithm on two different datasets: a larger, synthesized dataset and a smaller, real dataset.

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

Application of the van der Pauw method for electrical conductivity measurements at high temperatures using an insulating compressing ring

High quality data on the high temperature electrical properties of ceramics, particularly oxides, is of great value for material selection, design, and modeling for a broad range of emerging applications. Utilizing the mismatch in the coefficient of thermal expansion between two materials, a purely mechanical method for establishing electrical contact in the van der Pauw geometry to measure the bulk resistivity of ceramic disks at high temperatures is presented. Measurements of a reference material, 20 mol. % Gd-doped cerium oxide, are presented up to 1000 °C. The viability of electrical measurements up to a maximum temperature of 1600 °C is also considered. Measurements are performed using multiple techniques and compared to literature values finding excellent agreement. Finally, the approach described in this work enables the van der Pauw method to be applied to many ceramic materials over a wide range of temperatures and environments.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Conductivity space isotherm behavior in quantum anomalous Hall devices

The quantum Hall effect (QHE) has enhanced accessibility to measure and disseminate electrical units, owed in part to the recently redefined International System of Units in 2019. Graphene remains one of the preferred options to realize the ohm despite the limitations of high magnetic fields to produce a robust QHE. Topological insulators, on the other hand, show promise in providing quantized resistance via the quantum anomalous Hall effect, a phenomenon that removes the need for magnetic fields during operation. To optimize future devices for metrological applications, it is important to gain a better understanding of magnetically doped topological insulators like Cr-doped bismuth antimony telluride. The application of differential conductivity space analyses offers a more sensitive way to analyze the data and distinguish between 2D and 3D transport behaviors. This is particularly important in thin films, where the transition between 2D and 3D behavior can be subtle. The ability to confidently determine the dimensionality of the transport is crucial for selecting appropriate theoretical models for future device optimization. Furthermore, this work identifies variable range hopping as the dominant transport mechanism in the 2D regime using a rigorous statistical analysis (via the Bayes factor). These elements assist in the understanding of microscopic processes that govern charge transport in these materials.

Tran, N. T. M. [National Inst. of Standards and Te↗