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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Effects of Borate and Organics on U(VI) Solubility in WIPP Brine

The solubility of uranium (VI) in Waste Isolation Pilot Plant (WIPP)-relevant brine was determined to support ongoing WIPP recertification activities. This research was performed by the Los Alamos National Laboratory Carlsbad Operations (LANL-CO) Actinide Chemistry and Repository Science Program (ACRSP). The WIPP Actinide Source Term Program (ASTP) did not develop a model for the solubility of actinides in the VI oxidation state. The solubility of UO 2 2+ , in the absence of WIPP specific data, is presently set to be equal to a conservatively high 1 mM within the WIPP Performance Assessment (PA) for all expected WIPP conditions (SOTERM, 2019) as selected at the recommendation of the Environment Protection Agency (EPA) (EPA, 2005). According to the current WIPP chemistry model assumptions and conditions, the expected pC H+ is about 9.5 and controlled by MgO buffering CO 3 2- . The goal of this study is to perform screening experiments that account for the contributions of organics and borate on uranium solubility. In this report, the solubility of U(VI) was determined at pC H+ 9 WIPP brine in the absence or presence of borate and organics at under-saturation approach. Experiments were equilibrated for about 135 days. Organic compounds present in WIPP waste can form strong complexes with actinides and can affect the oxidation states of actinides. The organic compounds addressed in WIPP performance assessment include EDTA (Ethylenediaminetetraacetic Acid), oxalate, citrate, and acetate (SOTERM, 2019). These data quantify the effects of WIPP-relevant concentrations of borate and organics effects on the solubility of U(VI) to challenge the predictions of the WIPP actinide model and inform decisions and recommendations made in the upcoming recertification of the WIPP (CRA-2024). The experiments performed were done according to the U.S. Department of Energy (DOE) approved Test Plan entitled “Experimental Strategy to Challenge Actinide Solubility Predictions” and designated LCO-ACP-26. All data reported were obtained under the LANL-CO Quality Assurance Program, which is compliant with the DOE Carlsbad Field Office, Quality Assurance Program Document (CBFO/QAPD) (QAPD, 2017).

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

Accelerated Discovery of Solar Thermochemical Hydrogen Production Materials via High-Throughput Computational and Experimental Methods

In this project, combinatorial synthesis and testing methods were combined with high-throughput materials theory calculations to greatly accelerate the discovery of thermodynamically suitable candidates for green hydrogen production via a two-stage solar thermochemical water splitting (STCH) process. Over the course of the project, more than 8000 quinary and higher oxide compositions were computationally screened for STCH viability, and detailed stability calculations were performed for more than 30 of the most promising identified compositional archetypes. As a result, three new STCH capable compositional families were discovered and experimentally verified. The first, Ce x Sr 2-x MnO 4 (CSM), represents the first known Ruddlesden-Popper compound to show STCH activity, and thus demonstrates that perovskite-related structures may hold promise for this application. The second family, Sr 1-x Ce x MnO 3 (SCM), is the simple perovskite sister-analog to CSM. Sr 0.7 Ce 0.3 MnO 3 (SCM30), a member of this compositional family, was found to produce the highest hydrogen yields of any compound tested in this project, exceeding the end of project milestone target of > 150 μmol H 2 /gram oxide at a reduction temperature of 1350 °C, although only at steam-to-hydrogen ratios greater than 1000:1. Finally, we proved that a third novel Sr-and Mn-containing family, Sr 1-x Ca x Ti 1-y Mn y O 3 (SCTM), which was identified by Materials Project tools, also splits water. The behavior of the SCTM system was found to be similar to the previously discovered Sr 1-x La x Al 1-y Mn y O 3 (SLMA) family, albeit with lower H 2 yields. Across the three thrusts of the project (computational, combinatorial, and bulk testing), five journal articles were published. As part of Program End Analysis and Data Dissemination, relevant data used for the publications was uploaded to the HydroGEN Data Hub for public access, and in certain cases, results were added to public materials databases.

08 HYDROGEN↗
DOE OSTI2021

Computational studies reveal Fluorine based quinolines to be potent inhibitors for proteins involved in SARS-CoV-2 assembly

World is witnessing one of the worst pandemics of this century caused by SARS-CoV-2 virus which has affected millions of individuals. Despite rapid efforts to develop vaccines and drugs for COVID-19, the disease is still not under control. Chloroquine (CQ) and Hydroxychloroquine (HCQ) are two very promising inhibitors which have shown positive effect in combating the disease in preliminary experimental studies, but their use was reduced due to severe side-effects. Here, we performed a theoretical investigation of the same by studying the binding of the molecules with SARS-COV-2 Spike protein, the complex formed by Spike and ACE2 human receptor and a human serine protease TMPRSS2 which aids in cleavage of the Spike protein to initiate the viral activation in the body. Both the molecules had shown very good docking energies in the range of -6kcal/mol. Subsequently, we did a high throughput screening for other potential quinoline candidates which could be used as inhibitors. From the large pool of ligand candidates, we shortlisted the top three ligands (binding energy -8kcal/mol). We tested the stability of the docked complexes by running Molecular Dynamics (MD) simulations where we observed the stability of the quinoline analogues with the Spike-ACE2 and TMPRSS2 nevertheless the quinolines were not stable with the Spike protein alone. Thus, although the inhibitors bond very well with the protein molecules their intrinsic binding affinity depends on the protein dynamics. Moreover, the quinolines were stable when bound to electronegative pockets of Spike-ACE2 or TMPRSS2 but not with Viral Spike protein. We also observed that a Fluoride based compound: 3-[3-(Trifluoromethyl)phenyl]quinoline helps the inhibitor to bind with both Spike-ACE2 and TMPRSS2 with equal probability. The molecular details presented in this study would be very useful for developing quinoline based drugs for COVID-19 treatment.

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

Trichoderma species isolated from grapevine with tolerance towards common copper fungicides used in viticulture for plant protection

Abstract BACKGROUND Copper‐containing fungicides are applied broadly in organic viticulture against downy mildew caused by Plasmopara viticola . Although long‐term application of copper‐based fungicides is associated with ecotoxic effects on the environment, their use in viticulture is required until sustainable alternatives are available. Trichoderma spp. might be a promising approach to fungicide reduction while promoting plant growth and development and displaying biocontrol activity. This study aims to examine the tolerance and compatibility of Trichoderma spp. to copper fungicides. This work contributes to the development of a spray application consisting of a copper‐tolerant Trichoderma sp. combined with a downscaled copper fungicide rate against P. viticola . RESULTS Trichoderma spp. isolated from grapevine wood in vineyards were identified and used for tolerance screening in various concentrations of copper fungicides. Copper hydroxide was identified as being highly compatible with Trichoderma . Two Trichoderma candidates, T. koningiopsis and T. harzianum , showed high copper tolerance in mycelial growth and germination tests, and were adapted to 2.85 g Cu L −1 of the selected fungicide. Microscopic investigations showed the attachment of copper compounds to fungal cell walls and copper uptake within the cytoplasm. In the case of high tolerance, large‐scale copper uptake was prevented. CONCLUSION Our findings identified two highly copper‐tolerant Trichoderma isolates with natural adaptation to the vineyard ecosystem, which could be further tested as biostimulants and biocontrol agents, combined with a reduced fungicide rate for sustainable plant protection. © 2022 Society of Chemical Industry.

Küpper, Verena↗
DOE OSTI2024

Automated AI-driven Molecular Design for Therapeutic Discovery

In recent years, artificial intelligence and machine learning (AI/ML) approaches have revolutionized the process of designing new therapeutics, enabling scientists to rapidly respond to emerging threats from various pathogens. A prime example is the SARS-CoV-2 main protease, a key target for the development of antiviral inhibitors. In this study, we employed a novel, integrated approach that combines AI-driven iterative design of inhibitor candidates, screening based on physio-chemical properties and toxicity, physics-based computational modeling of protein-inhibitor interactions, and AI-assisted analysis of Native MS biophysical assay and characterization of designed candidates. Our deep learning 3D-scaffold model, which uses an input scaffold as a starting point, generated tens of thousands of compounds while preserving the key scaffold. To optimize these candidates, we calculated a comprehensive set of 136 descriptors, including both 2D and 3D molecular features, for compounds targeting the SARS-CoV-2 Main protease (Mpro) and a neurodegenerative disease-associated protein, cyclophilin (Cyp). The generated compounds were initially filtered based on their properties and then ranked according to their predicted binding affinity using our automated modeling and ML methods. Experimental validation of the Mpro candidates showing inhibitory activity demonstrates that our workflow can expedite the therapeutic discovery.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1977

Preliminary results of accelerated exposure testing of solar cell system components

Plastic samples and solar cell sub modules were exposed to an accelerated outdoor environment in Arizona and an accelerated simulated environment in a cyclic ultraviolet exposure tester which included humidity exposure. These tests were for preliminary screening of materials suitable for use in the manufacture of solar cell modules which are to have a 20-year lifetime. The samples were exposed for various times up to six months, equivalent to a real time exposure of four years. Suitable materials were found to be FEP-A, FEP-C, PFA, acrylic, silicone compounds and adhesives and possibly parylene. The method of packaging the sub modules was also found to be important to their performance.

Anagnostou, E.↗
NASA NTRS1980

Advanced screening of electrode couples

The chromium (Cr(3+)/Cr(2+)) redox couple (electrolyte and electrode) was investigated to determine its suitability as negative electrode for the iron (Fe(3+)/Fe(2+))-chromium (Cr(3+)/Cr(2+)) redox flow battery. Literature search and laboratory investigation established that the solubility and stability of aqueous acidic solutions of chromium(3) chloride and chromium(2) chloride are sufficient for redox battery application. Four categories of electrode materials were tested; namely, metals and metalloid materials (elements and compounds), alloys, plated materials, and Teflon-bonded materials. In all, the relative performance of 26 candidate electrode materials was evaluated on the basis of slow scan rate linear sweep voltammetry in stirred solution. No single material tested gave both acceptable anodic an acceptable cathodic performance. However, the identification of lead as a good cathodic electrocatalyst and gold as a good anodic electrocatalyst led to the invention of the lead/gold combination electrocatalyst. This type of catalyst can be fabricated in several ways and appears to offer the advantages of each metal without the disadvantages associated with their use as single materials. This lead/gold electrocatalyst was tested by NASA-Lewis Research Center in complete, flowing, redox batteries comprising a stack of several cells. A large improvement in the battery's coulombic and energy efficiency was observed.

Giner, J. D.↗
DOE OSTI2024

Catabolism of β-5 linked aromatics by Novosphingobium aromaticivorans

ABSTRACT Aromatic compounds are an important source of commodity chemicals traditionally produced from fossil fuels. Aromatics derived from plant lignin can potentially be converted into commodity chemicals through depolymerization followed by microbial funneling of monomers and low molecular weight oligomers. This study investigates the catabolism of the β-5 linked aromatic dimer dehydrodiconiferyl alcohol (DC-A) by the bacterium Novosphingobium aromaticivorans . We used genome-wide screens to identify candidate genes involved in DC-A catabolism. Subsequent in vivo and in vitro analyses of these candidate genes elucidated a catabolic pathway composed of four required gene products and several partially redundant dehydrogenases that convert DC-A to aromatic monomers that can be funneled into the central aromatic metabolic pathway of N. aromaticivorans . Specifically, a newly identified γ-formaldehyde lyase, PcfL, opens the phenylcoumaran ring to form a stilbene and formaldehyde. A lignostilbene dioxygenase, LsdD, then cleaves the stilbene to generate the aromatic monomers vanillin and 5-formylferulate (5-FF). We also showed that the aldehyde dehydrogenase FerD oxidizes 5-FF before it is decarboxylated by LigW, yielding ferulic acid. We found that some enzymes involved in the β-5 catabolism pathway can act on multiple substrates and that some steps in the pathway can be mediated by multiple enzymes, providing new insights into the robust flexibility of aromatic catabolism in N. aromaticivorans . A comparative genomic analysis predicted that the newly discovered β-5 aromatic catabolic pathway is common within the order Sphingomonadales. IMPORTANCE In the transition to a circular bioeconomy, the plant polymer lignin holds promise as a renewable source of industrially important aromatic chemicals. However, since lignin contains aromatic subunits joined by various chemical linkages, producing single chemical products from this polymer can be challenging. One strategy to overcome this challenge is using microbes to funnel a mixture of lignin-derived aromatics into target chemical products. This approach requires strategies to cleave the major inter-unit linkages of lignin to release monomers for funneling into valuable products. In this study, we report newly discovered aspects of a pathway by which the Novosphingobium aromaticivorans DSM12444 catabolizes aromatics joined by the second most common inter-unit linkage in lignin, the β-5 linkage. This work advances our knowledge of aromatic catabolic pathways, laying the groundwork for future metabolic engineering of this and other microbes for optimized conversion of lignin into products.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Discovery of Ternary Antimonides A–Al–Sb (A = Rb or Cs) with Desired Structural Motifs Guided by Machine Learning

Specific structural motifs in inorganic solids are often related to their targeted physical properties. For many classes of solids, such as Zintl phases and polar intermetallics, the crystal structures are diverse and not easy to predict. Various antimonides that are potential thermoelectric materials were proposed to be synthesizable on the basis of their estimated formation energies. Their structures were broadly classified as clathrate, channel, layered, or network through a machine learning model trained on existing ternary phases and features based on elemental properties using the sure independence screening and sparsifying operator algorithm. Through experimental validation, three new ternary antimonides were synthesized and confirmed to form layered structures: tetragonal RbAlSb 2 and CsAlSb 2 , which are isopointal but not isotypic to LiBSi 2 ; and monoclinic Rb 2 Al 2 Sb 3 , which adopts the Na 2 Al 2 Sb 3 -type structure. Finally, reinvestigation of the related compound Cs 2 In 2 Sb 3 revealed a low thermal conductivity and p-type semiconducting behavior.

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

Multicriteria-Based Selection of Microalgae Biorefineries: Biomass Composition Defines the Most Suitable Product Portfolios

The supply of microalgae-derived biofuels and bioproducts will be vital in a global-scale bioeconomy. As the diversity of microalgae strains and their compositional plasticity may yield a wide array of products of market interest, the design of effective biorefineries is a central aspect in the path to making microalgae-based products available in the market. In this way, the conversion of microalgae biomass should be planned to employ mature technologies, while maximizing economic and environmental benefits obtained from a diverse product portfolio. This study applied sequential, hybrid Multicriteria Decision Analyses (MCDA) to aid the decision-making process of outlining the most suitable biorefining pathways for specific compositional profiles. For this, the methodology simultaneously considered multiple technical, economic, and environmental criteria, such as market aspects for the main algae-derived products, potential reduction in greenhouse gas (GHG) emissions provided by the biobased alternatives, and technology readiness level of conversion routes, among others. The framework was tested using productivity and compositional data for 13 high-productivity summer strains cultivated under varying nutrient availability. The analysis pointed to compositional profile being a key driver in defining the core biorefining strategy of algae biomass, with lipid-to-carbohydrate ratios higher than roughly 1 warranting the preferential processing of lipids into hydrocarbon fuels with the remainder of the algae biomass compounds being sent to higher-value applications, such as carboxylic acids and renewable thermoplastic substitutes. An in-depth process simulation, techno-economic assessment (TEA), and life-cycle analysis (LCA) effort was carried out as a closing step to corroborate the results stemming from the proposed framework. This study validates the use of MCDAs as a screening method prior to implementing more time-consuming analysis techniques and makes a compelling case for this approach as a product selection tool on a wide range of algae species, thus helping establish species-agnostic (but composition-driven) biorefineries.

biofuels↗
DOE OSTI2020

Characterization of rattling in relation to thermal conductivity: Ordered half-Heusler semiconductors

We report that the factors that affect the thermal conductivity of semiconductors is a topic of great scientific interest, especially in relation to thermoelectrics. Key developments have been the concept of the phonon-glass-electron-crystal (PGEC) and the related idea of rattling to achieve this. We use first-principles phonon and thermal conductivity calculations to explore the concept of rattling for stoichiometric-ordered half-Heusler compounds. These compounds can be regarded as filled zinc blende materials, and the filling atom could be viewed as a rattler if it is weakly bound. We use two simple metrics, one related to the frequency and the other to bond frustration and anharmonicity. We find that both measures correlate with thermal conductivity. Lastly, this suggests that both may be useful in screening materials for low thermal conductivity.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Phonon Screening of Excitons in Semiconductors: Halide Perovskites and Beyond

The ab initio Bethe-Salpeter equation (BSE) approach, an established method for the study of excitons in materials, is typically solved in a limit where only static screening from electrons is captured. Here, we generalize this framework to include dynamical screening from phonons at lowest order in the electron-phonon interaction. We apply this generalized BSE approach to a series of inorganic lead halide perovskites, CsPbX 3 , with X=Cl, Br, and I. We find that inclusion of screening from phonons significantly reduces the computed exciton binding energies of these systems. By deriving a simple expression for phonon screening effects, we reveal general trends for their importance in semiconductors and insulators, based on a hydrogenic exciton model. We demonstrate that the magnitude of the phonon screening correction in isotropic materials can be reliably predicted using four material specific parameters: the reduced effective mass, static and optical dielectric constants, and frequency of the most strongly coupled longitudinal-optical phonon mode. Finally, this framework helps to elucidate the importance of phonon screening and its relation to excitonic properties in a broad class of semiconductors.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Cross-Platform Classifier of Chemical Stability for Charged Redoxmers

The development of new redox flow battery chemistries is hampered by time-consuming organic syntheses and electrochemical characterization of candidate redoxmer molecules. Here, we use Sure Independence Screening and Sparsifying Operator (SISSO) to demonstrate a cross-platform classifier for chemical stability of charged redoxmers in electrolyte solutions. This SISSO model yields a single formula to separate positively charged dialkoxyarene catholytes and negatively charged pyridinium anolytes into stable and unstable species in acetonitrile-based electrolytes as probed through cyclic voltammetry measurements. Using such classifiers, the stability of new compounds across different synthetic platforms can be rapidly assessed.

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

Synthetic accessibility and stability rules of NASICONs

In this paper we develop the stability rules for NASICON-structured materials, as an example of compounds with complex bond topology and composition. By first-principles high-throughput computation of 3881 potential NASICON phases, we have developed guiding stability rules of NASICON and validated the ab initio predictive capability through the synthesis of six attempted materials, five of which were successful. A simple two-dimensional descriptor for predicting NASICON stability was extracted with sure independence screening and machine learned ranking, which classifies NASICON phases in terms of their synthetic accessibility. This machine-learned tolerance factor is based on the Na content, elemental radii and electronegativities, and the Madelung energy and can offer reasonable accuracy for separating stable and unstable NASICONs. This work will not only provide tools to understand the synthetic accessibility of NASICON-type materials, but also demonstrates an efficient paradigm for discovering new materials with complicated composition and atomic structure.

25 ENERGY STORAGE↗
DOE OSTI2023

Advanced Analytical Methodologies Enable Feedstock Screening and Correlation to Pyrolysis Yields and Catalytic Upgrading

Pyrolysis of lignocellulosic biomass can serve as a powerful pathway for the generation of renewable fuels and industrial products. However, procurement of some biomass may be costly, especially when there is competition for alternative uses. Forestry and agricultural byproducts may serve as valuable pyrolysis feedstocks due to their high availability and low cost. Although waste products may fill demand for affordable pyrolysis feedstocks, they are often high in ash and extractives which contribute to unfavorable changes in pyrolysis vapor composition, as well as end products. The work presented here investigates the correlations between various pine anatomical fractions to understand their effect on pyrolysis oil composition for forest residues. Analytical pyrolysis coupled to direct analysis by molecular beam mass spectrometry (MBMS) provides real-time monitoring of pyrolysis vapor composition, even for compounds that are not amenable to gas chromatography. Statistical analysis of MBMS data from the fast pyrolysis (FP) of clean pine, cambium, twigs and branches, needles, bark, and a forest residue samples revealed that bark and needles made the greatest differences in the vapor composition of forest residues during pyrolysis and were enriched in resin acids, furfural derivatives, and ions that have been previously observed from catalysis corresponding to ash content. These results were also compared to catalytic fast pyrolysis (CFP) using a platinum on titania catalyst, and correlations between FP and CFP products were observed for the anatomical fractions. The forest residues and corresponding anatomical fractions from a stand of 23-year-old pine will be converted by fast pyrolysis in a bench-scale 2-inch fluidized-bed reactor, and statistical correlations between the bench-scale products and analytical-scale pyrolysis vapors will be shown. This work highlights the power of advanced analytical methodologies in determining the contributions of vapor components to catalytic pyrolysis outcomes for mixed feedstocks and demonstrates the use of analytical methodologies to better screen feedstocks prior to pyrolysis.

analytical↗
DOE OSTI2026

Random forest models accurately classify synthetic opioids using high-dimensionality mass spectrometry datasets

Detection of novel threat agents presents several challenges, a principle one being the development of untargeted methods to screen an increasing number of threat chemicals whose exact structures are unknown. With the use of Machine Learning (ML) tools, we can guide the development of analytical methods for broad-spectrum detection of unbounded threat chemical families in complex mixtures. Toward this goal, we used nominal mass and high-resolution mass spectrometry data for hundreds of synthetic opioids and non-opioid compounds. We tested two ML techniques, logistic regression and random forest, to develop models towards a practical, implementable method for opioid detection. We found that of these tested ML methods, random forest models resulted in the highest validation accuracy (95+%) for both nominal mass and high-resolution classification of opioids versus non-opioids, with low false positive and false negative rates. The RF models were then used to successfully predict the classification of 10 compounds—five opioids and five non-opioids not part of the training and validation analysis. This application of ML is a critical step towards the development of field-deployable nominal mass spectrometers with ML-driven analyses for classification of emergent threats.

Chemistry↗
DOE OSTI2025

Random forest models accurately classify synthetic opioids using high-dimensionality mass spectrometry datasets

Detection of novel threat agents presents several challenges, a principle one being the development of untargeted methods to screen an increasing number of threat chemicals whose exact structures are unknown. With the use of Machine Learning (ML) tools, we can guide the development of analytical methods for broad-spectrum detection of unbounded threat chemical families in complex mixtures. Toward this goal, we used nominal mass and high-resolution mass spectrometry data for hundreds of synthetic opioids and non-opioid compounds. We tested two ML techniques, logistic regression and random forest, to develop models towards a practical, implementable method for opioid detection. We found that of these tested ML methods, random forest models resulted in the highest validation accuracy (95+%) for both nominal mass and high-resolution classification of opioids versus non-opioids, with low false positive and false negative rates. The RF models were then used to successfully predict the classification of 10 compounds—five opioids and five non-opioids not part of the training and validation analysis. This application of ML is a critical step towards the development of field-deployable nominal mass spectrometers with ML-driven analyses for classification of emergent threats.

Arasteh, Kourosh [Lawrence Livermore National Labo↗