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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS2002

Chemistry-Climate Interactions in the Goddard Institute for Space Studies General Circulation Model: New Insights into Modeling the Pre-Industrial Atmosphere - 2

We investigate the chemical (hydroxyl and ozone) and dynamical response to changing from present day to pre-industrial conditions in the Goddard Institute for Space Studies General Circulation Model (GISS GMC). We identify three main improvements not included by many other works. Firstly, our model includes interactive cloud calculations. Secondly we reduce sulfate aerosol which impacts NOx partitioning hence Ox distributions. Thirdly we reduce sea surface temperatures and increase ocean ice coverage which impact water vapor and ground albedo respectively. Changing the ocean data (hence water vapor and ozone) produces a potentially important feedback between the Hadley circulation and convective cloud cover. Our present day run (run 1, control run) global mean OH value was 9.8 x 10(exp 5) molecules/cc. For our best estimate of pre-industrial conditions run (run 2) which featured modified chemical emissions, sulfate aerosol and sea surface temperatures/ocean ice, this value changed to 10.2 x 10(exp 5) molecules/cc. Reducing only the chemical emissions to pre-industrial levels in run 1 (run 3) resulted in this value increasing to 10.6 x 10(exp 5) molecules/cc. Reducing the sulfate in run 3 to pre-industrial levels (run 4) resulted in a small increase in global mean OH (10.7 x 10(exp 5) molecules/cc). Changing the ocean data in run 4 to pre-industrial levels (run 5) led to a reduction in this value to 10.3 x 10(exp 5) molecules/cc. Mean tropospheric ozone burdens were 262, 181, 180, 180, and 182 Tg for runs 1-5 respectively.

Grenfell, J. Lee↗
DOE OSTI2023

ReaLigands: A Ligand Library Cultivated from Experiment and Intended for Molecular Computational Catalyst Design

Computational catalyst design requires identification of a metal and ligand that together result in the desired reaction reactivity and/or selectivity. A major impediment to translating computational designs to experiments is evaluating ligands that are likely to be synthesized. Here we provide a solution to this impediment with our ReaLigands library that contains >30,000 monodentate, bidentate (didentate), tridentate, and larger ligands cultivated by dismantling experimentally reported crystal structures. Individual ligands from mononuclear crystal structures were identified using a modified depth-first search algorithm and charge was assigned using a machine learning model based on quantum-chemical calculated features. In the library ligands are sorted based on direct ligand-to-metal atomic connections and on denticity. Representative principal component analysis (PCA) and uniform manifold approximation and projection (UMAP) analyses were used to analyze several tridentate ligand categories, which revealed both the diversity of ligands and connections between ligand categories. Furthermore, we also demonstrated the utility of this library by implementing it with our building and optimization tools, which resulted in the very rapid generation of barriers for 750 bidentate ligands for Rh-hydride ethylene migratory insertion.

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

Multifunctional textured graphene-based coatings on elastomeric gloves for chemical protection

Nanotechnology offers a variety of new tools for the design of next-generation personal protective equipment (PPE). One example is the use of two-dimensional materials as coatings that enhance the performance and ergonomics of elastomeric gloves designed to protect users from hazardous chemicals. Desirable features in such coatings may include molecular barrier function, liquid droplet repellency, stretchability for compatibility with the elastomer, breathability, and an ultrathin profile that preserves the user's manual dexterity and tactile sensation. The present work explores the potential of engineered graphene-based films with out-of-plane texturing as a novel platform to meet these multifold requirements. Graphene-based films in different formulations were fabricated from water-borne inks by vacuum filtration and solution casting methods on glove-derived nitrile rubber substrates. The various coatings were then subjected to tests of molecular permeation by model volatile organic compounds, droplet contact angle, breathability, and mechanical stability during stretching and solvent immersion. The films dramatically improve the barrier properties of glove-derived nitrile. The out-of-plane graphene texturing imparts stretchability through microscale folding/unfolding, while also enhancing droplet repellency in some cases through a lotus-like roughening effect. The combined results suggest that engineered textured graphene-based films are a promising platform for creating multifunctional coatings for a next generation of chemically protective gloves and other elastomer-based PPE.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Examination of coal combustion management sites for microbiological and chemical signatures of groundwater impacts

Coal combustion accounts for 40% of the world’s electricity and generates more than a billion tons of coal combustion products (CCP) annually, half of which end up in landfills and impoundments. CCP contain mixtures of chemicals that can be mobile in the environment and impact the quality of surface water and potable groundwater. In this investigation, water samples from 14 coal combustion management sites across 4 physiographic regions in the United States, paired with background and down-gradient groundwater samples, were analyzed for water chemistry and microbiology. The objective was to determine if microbiology data alone, or supported by chemistry data, could reliably differentiate source waters and identify sites where CCP is known or expected to be influencing groundwater. Two percent of the total amplicons showed genus level conservation across CCP management sites, regions, and sample types; corresponding to ubiquitous, facultatively aerobic proteobacterial taxa that are generally recognized for the potential to respire using different terminal electron acceptors. Ordination plots did not reveal significant differences ( p > 0.05) in 16S rRNA gene amplicon diversity by CCP management site, water sample types, or physiographic regions. Contrastingly, chemistry distinguished sample types by standard water quality metrics (total dissolved solids, Ca:SO 4 ratio), alkali earth metals (K, Na, Li), selenium, boron, and fluoride. A focused evaluation of 16S rRNA gene amplicons for a subset of CCP management sites revealed microbiological features and chemical drivers (F, Ca, temperature) that positively identified the single CCP management site confirmed to have groundwater impacted by CCP leachate. At this site, 9 genera (>0.5% relative abundance) were exclusive to CCP porewater and downgradient groundwater. Inferred metabolisms for these taxa indicates potential for N and S biogeochemical transformations and 1-C metabolism that are consistent with a reducing environment, as evidenced by low ORP and depleted SO 4 2− . This research contributes to a growing understanding of conditions where these data types, analyses, and interpretation methods could be applied for distinguishing influence from CCP on the surrounding environment, as well as practical limitations.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2021

Importance of Engineered and Learned Molecular Representations in Predicting Organic Reactivity, Selectivity, and Chemical Properties

Machine-readable chemical structure representations are foundational in all attempts to harness machine learning for the prediction of reactivities, selectivities, and chemical properties directly from molecular structure. The featurization of discrete chemical structures into a continuous vector space is a critical phase undertaken before model selection, and the development of new ways to quantitatively encode molecules is an active area of research. Here, we highlight the application and suitability of different representations, from expert-guided “engineered” descriptors to automatically “learned” features, in different prediction tasks relevant to organic and organometallic chemistry, where differing amounts of training data are available. These tasks include statistical models of stereo- and enantioselectivity, thermochemistry, and kinetics developed using experimental and quantum chemical data. The use of expert-guided molecular descriptors provides an opportunity to incorporate chemical knowledge, domain expertise, and physical constraints into statistical modeling. In applications to stereoselective organic and organometallic catalysis, where data sets may be relatively small and 3D-geometries and conformations play an important role, mechanistically informed features can be used successfully to obtain predictive statistical models that are also chemically interpretable. We provide an overview of several recent applications of this approach to obtain quantitative models for reactivity and selectivity, where topological descriptors, quantum mechanical calculations of electronic and steric properties, along with conformational ensembles, all feature as essential ingredients of the molecular representations used. Alternatively, more flexible, general-purpose molecular representations such as attributed molecular graphs can be used with machine learning approaches to learn the complex relationship between a structure and prediction target. This approach has the potential to out-perform more traditional representation methods such as “hand-crafted” molecular descriptors, particularly as data set sizes grow. One area where this is particularly relevant is in the use of large sets of quantum mechanical data to train quantitative structure–property relationships. A general approach toward curating useful data sets and training highly accurate graph neural network models is discussed in the context of organic bond dissociation enthalpies, where this strategy outperforms regression using precomputed descriptors. Finally, we describe how graph neural network predictions can be incorporated into mechanistically informed statistical models of chemical reactivity and selectivity. Once trained, this approach avoids the expensive computational overhead associated with quantum mechanical calculations, while maintaining chemical interpretability. We illustrate examples for which fast predictions of bond dissociation enthalpy and of the identities of radicals formed through cleavage of a molecule’s weakest bond are used in simple physical models of site-selectivity and reactivity.

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

Single‐Atom Electrocatalysts from Multivariate Metal–Organic Frameworks for Highly Selective Reduction of CO 2 at Low Pressures

Abstract Single‐atom catalysts (SACs) are of great interest because of their ultrahigh activity and selectivity. However, it is difficult to construct model SACs according to a general synthetic method, and therefore, discerning differences in activity of diverse single‐atom catalysts is not straightforward. Herein, a general strategy for synthesis of single‐atom metals implanted in N‐doped carbon (M 1 ‐N‐C; M=Fe, Co, Ni and Cu) has been developed starting from multivariate metal–organic frameworks (MOFs). The M 1 ‐N‐C catalysts, featuring identical chemical environments and supports, provided an ideal platform for differentiating the activity of single‐atom metal species. When employed in electrocatalytic CO 2 reduction, Ni 1 ‐N‐C exhibited a very high CO Faradaic efficiency (FE) up to 96.8 % that far surpassed Fe 1 ‐, Co 1 ‐ and Cu 1 ‐N‐C. Remarkably, the best‐performer, Ni 1 ‐N‐C, even demonstrated excellent CO FE at low CO 2 pressures, thereby representing a promising opportunity for the direct use of dilute CO 2 feedstock.

Jiao, Long↗
DOE OSTI2020

Single-Atom Electrocatalysts from Multivariate MOFs for Highly Selective Reduction of CO 2 at Low Pressures

Single-atom catalysts (SACs) are of great interest because of their ultrahigh activity and selectivity. However, it is difficult to construct model SACs according to a general synthetic method, and therefore, discerning differences in activity of diverse single-atom catalysts is not straightforward. Herein, a general strategy for synthesis of single-atom metals implanted in N-doped carbon (M 1 -N-C; M=Fe, Co, Ni and Cu) has been developed starting from multivariate metal–organic frameworks (MOFs). The M 1 -N-C catalysts, featuring identical chemical environments and supports, provided an ideal platform for differentiating the activity of single-atom metal species. When employed in electrocatalytic CO 2 reduction, Ni 1 -N-C exhibited a very high CO Faradaic efficiency (FE) up to 96.8 % that far surpassed Fe 1 -, Co 1 - and Cu 1 -N-C. Remarkably, the best-performer, Ni1-N-C, even demonstrated excellent CO FE at low CO 2 pressures, thereby representing a promising opportunity for the direct use of dilute CO 2 feedstock.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Structural characterization of sugarcane lignins extracted from different protic ionic liquid pretreatments

Previous studies based on protic ionic liquids have shown them to be effective for extracting lignins from biomass. Moreover, ILs synthesized with amine cations can promote the insertion of nitrogen into lignin, favoring its valorization for industrial applications. Here, lignins extracted from sugarcane bagasse by eight different PILs were analyzed. Several techniques of characterization were employed (2D HSQC NMR, 31P NMR, GPC, Py-GC/MS, TGA, FTIR, and elemental analysis), seeking a deeper understanding of lignin structure post-PILS treatment. In particular, lignin obtained with acetate and lactate anions with monoethanolamonium cations showed similar structural compositions. Lignins obtained from ethylenediamonium cation combined anions acetate and lactate exhibited relatively higher molecular weights compared to the other lignins studied. The elemental analysis of these lignins further revealed the presence of nitrogen in their structures, suggesting that amination occurred. In this work we used PILs not yet reported in the literature for this purpose, and consequently obtained lignins with specific structures. Overall, our results demonstrated that depending on the PILs used, lignins with distinct properties could be obtained which might be used for value-added applications. This work covered new advances in elucidation of lignin chemical structure and opened a new path for lignin valorization.

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

Quasi-Two-Dimensional Heterostructures (K M 1 – x Te)(LaTe 3 ) ( M = Mn and Zn) with Charge Density Waves

Layered heterostructure materials with two different functional building blocks can teach us about emergent physical properties and phenomena arising from interactions between the layers. Here, we report intergrowth compounds KLaM 1 - x Te 4 (M = Mn and Zn; $x \approx$ 0.35) featuring two chemically distinct alternating layers [LaTe 3 ] and [KM 1 - x Te]. Their crystal structures are incommensurate, determined by single X-ray diffraction for the Mn compound and a transmission electron microscope study for the Zn compound. KLaMn 1 – x Te 4 crystallizes in the orthorhombic superspace group Pmnm(01/2 gamma)s00 with lattice parameters a = 4.4815(3) Å, b = 21.6649(16) Å, and c = 4.5220(3) Å. It exhibits charge density wave order at room temperature with a modulation wave vector q = 1/2b* + 0.3478c* originating from electronic instability of Te-square nets in [LaTe 3 ] layers. The Mn analog exhibits a cluster spin glass behavior with spin freezing temperature $T_f \approx$ 5 K attributed to disordered Mn vacancies and competing magnetic interactions in the [Mn 1 - x Te] layers. The Zn analog also has charge density wave order at room temperature with a similar q-vector having the c* component similar to 0.346 confirmed by selected-area electron diffraction. Electron transfer from [KM 1 - x Te] to [LaTe 3 ] layers exists in KLaM 1 – x Te 4 , leading to an enhanced electronic specific heat coefficient. The resistivities of KLaM 1 - x Te 4 (M = Mn and Zn) exhibit metallic behavior at high temperatures and an upturn at low temperatures, suggesting partial localization of carriers in the [LaTe 3 ] layers with some degree of disorder associated with the M atom vacancies in the [M 1 - x Te] layers.

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

Mesoscale Modeling Approach for Quantifying Microstructure-Aware Micromechanical Responses in Metal Hydrides

Metal hydrides can undergo significant volume changes upon hydrogen uptake and release, which induce a mechanical response that depends not only on the evolving hydrogen composition but also on the microstructure. We present a comprehensive mesoscale modeling framework based on microelasticity theory to quantify the micromechanical responses of metal hydrides, specifically focusing on a hydrogenating polycrystalline MgH 2x particle within a host material as a model micromechanical system. Utilizing digitally generated realistic microstructures and density-functional-theory-derived parameters, we analyzed highly nonuniform local stress profiles in the polycrystalline hydrides under the clamping force exerted by the host during hydrogenation. Our framework also allows us to predict the corresponding strain energy accumulation and mechanical hot spots formation in the hydrides, highlighting their roles in thermodynamic destabilization and mechanical failure, respectively. Through extensive parametric simulations, we further quantified the influence of interface type, crystallinity, grain size, loading ratio, and host stiffness, providing practical guidance for optimizing microstructural design and host material selection. This proposed approach is broadly applicable to micromechanical systems with complex microstructural features involving chemical reaction- and/or phase-transformation-induced deformation.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Mechanistic Duality of Bacterial Efflux Substrates and Inhibitors: Example of Simple Substituted Cinnamoyl and Naphthyl Amides

Antibiotic resistance poses an immediate and growing threat to human health. Multidrug efflux pumps are promising targets for overcoming antibiotic resistance with small-molecule therapeutics. Previously, we identified a diaminoquinoline acrylamide, NSC-33353, as a potent inhibitor of the AcrAB–TolC efflux pump in Escherichia coli. This inhibitor potentiates the antibacterial activities of novobiocin and erythromycin upon binding to the membrane fusion protein AcrA. It is also a substrate for efflux and lacks appreciable intrinsic antibacterial activity of its own in wild-type cells. Here, we have modified the substituents of the cinnamoyl group of NSC-33353, giving rise to analogs that retain the ability to inhibit efflux, lost the features of the efflux substrates, and gained antibacterial activity in wild-type cells. The replacement of the cinnamoyl group with naphthyl isosteres generated compounds that lack antibacterial activity but are both excellent efflux pump inhibitors and substrates. Surprisingly, these inhibitors potentiate the antibacterial activity of novobiocin but not erythromycin. Surface plasmon resonance experiments and molecular docking suggest that the replacement of the cinnamoyl group with naphthyl shifts the affinity of the compounds away from AcrA to the AcrB transporter, making them better efflux substrates and changing their mechanism of inhibition. These results provide new insights into the duality of efflux substrate/inhibitor features in chemical scaffolds that will facilitate the development of new efflux pump inhibitors.

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

Chloride electrolyte enabled practical zinc metal battery with a near-unity Coulombic efficiency

Rechargeable aqueous zinc batteries are finding their niche in stationary storage applications where safety, cost, scalability and carbon footprint matter most. However, harnessing this reversible two-electron redox chemistry is plagued by major technical issues, notably hydrogen evolution reaction (HER) at the zinc surface, whose impacts are often not revealed under typical measurement conditions. Here we report a concentrated electrolyte design that eliminates this parasitic reaction and enables a Coulombic efficiency (CE) of 99.95% for Zn plating/stripping measured at a low current density of 0.2 mA cm -2 . With extra chloride salts and dimethyl carbonate in concentrated ZnCl 2 electrolyte, the hybrid electrolyte with a unique chemical environment features low Hammett acidity and facilitates the in situ formation of a dual-layered solid electrolyte interphase, protecting zinc anodes from HER and dendrite growth. Benefiting from the near-unity CE, the pouch cell with a VOPO 4 ·2H 2 O cathode sustains 500 deep cycles without swelling or leaking and delivers an energy density of 100 Wh kg -1 under practical conditions. Finally, our work represents a critical step forward in accelerating the market adoption of zinc batteries as an energy storage system with higher sustainability.

25 ENERGY STORAGE↗
DOE OSTI2025

Spatial profile of argon (1s 5 ) metastables in an electron beam generated plasma

Electron beams with an applied magnetic field generate a secondary cold plasma with a selective chemical composition, featuring low-energy ions and metastable species in the discharge periphery, ideal for low-damage plasma treatment of material substrates. In this work, we studied the plasma generated by an e-beam using a 4 kV voltage in a pure argon gas environment under a magnetic field of 150 G and in the pressure range of 25–90 mTorr. We measured the absolute spatial density profile of argon (1 s 5 ) metastables in an electron beam generated plasma by laser-induced fluorescence and found it to be of the order of 10 16 m −3 . The electron temperature and the electron density measured by a Langmuir probe were of the order of 10 16 m −3 and less than an eV respectively. Electron-impact quenching was identified as a significant loss mechanism for the Ar(1s 5 ) state, leading to the saturation of the metastable density at higher pressures. Outside the primary ionization region, the spatial distribution of argon metastables followed a linear diffusion profile, indicating negligible additional production in those regions.

EEDF↗
DOE OSTI2020

Assessment of software methods for estimating protein-protein relative binding affinities

A growing number of computational tools have been developed to accurately and rapidly predict the impact of amino acid mutations on protein-protein relative binding affinities. Such tools have many applications, for example, designing new drugs and studying evolutionary mechanisms. In the search for accuracy, many of these methods employ expensive yet rigorous molecular dynamics simulations. By contrast, non-rigorous methods use less exhaustive statistical mechanics, allowing for more efficient calculations. However, it is unclear if such methods retain enough accuracy to replace rigorous methods in binding affinity calculations. This trade-off between accuracy and computational expense makes it difficult to determine the best method for a particular system or study. Here, eight non-rigorous computational methods were assessed using eight antibody-antigen and eight non-antibody-antigen complexes for their ability to accurately predict relative binding affinities (ΔΔG) for 654 single mutations. In addition to assessing accuracy, we analyzed the CPU cost and performance for each method using a variety of physico-chemical structural features. This allowed us to posit scenarios in which each method may be best utilized. Most methods performed worse when applied to antibody-antigen complexes compared to non-antibody-antigen complexes. Rosetta-based JayZ and EasyE methods classified mutations as destabilizing (ΔΔG < -0.5 kcal/mol) with high (83–98%) accuracy and a relatively low computational cost for non-antibody-antigen complexes. Some of the most accurate results for antibody-antigen systems came from combining molecular dynamics with FoldX with a correlation coefficient (r) of 0.46, but this was also the most computationally expensive method. Overall, our results suggest these methods can be used to quickly and accurately predict stabilizing versus destabilizing mutations but are less accurate at predicting actual binding affinities. This study highlights the need for continued development of reliable, accessible, and reproducible methods for predicting binding affinities in antibody-antigen proteins and provides a recipe for using current methods.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1968

Nature of quasi-stellar objects

Quasar properties from cosmological nature, discussing red shift, luminosity, radiation intensity, absorption features, distribution, chemical composition, etc

Burbidge, G. R.↗