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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 NTRS2015

Fracture Toughness, Mechanical Property, And Chemical Characterization Of A Critical Modification To The NASA SLS Solid Booster Internal Material System

A modified propellant-liner-insulation (PLI) bondline in the Space Launch System (SLS) solid rocket booster required characterization for flight certification. The chemical changes to the PLI bondline and the required additional processing have been correlated to mechanical responses of the materials across the bondline. Mechanical properties testing and analyses included fracture toughness, tensile, and shear tests. Chemical properties testing and analyses included Fourier transform infrared (FTIR) spectroscopy, cross-link density, high-performance liquid chromatography (HPLC), gas chromatography (GC), gel permeation chromatography (GPC), and wave dispersion X-ray fluorescence (WDXRF). The testing identified the presence of the expected new materials and found the functional bondline performance of the new PLI system was not significantly changed from the old system.

Pancoast, Justin↗
DOE OSTI2023

Method for simultaneous characterization and expansion of reference libraries for small molecule identification

A variational autoencoder (VAE) has been developed to learn a continuous numerical, or latent, representation of molecular structure to expand reference libraries for small molecule identification. The VAE has been extended to include a chemical property decoder, trained as a multitask network, to shape the latent representation such that it assembles according to desired chemical properties. The approach is unique in its application to metabolomics and small molecule identification, focused on properties that are obtained from experimental measurements (m/z, CCS) paired with its training paradigm, which involves a cascade of transfer learning iterations. First, molecular representation is learned from a large dataset of structures with m/z labels. Next, in silico property values are used to continue training. Finally, the network is further refined by being trained with the experimental data. The trained network is used to predict chemical properties directly from structure and generate candidate structures with desired chemical properties. The network is extensible to other training data and molecular representations, and for use with other analytical platforms, for both chemical property and feature prediction as well as molecular structure generation.

Colby, Sean M.↗
DOE OSTI2023

Correlating the role of nanofillers with active layer properties and performance of thin-film nanocomposite membranes

Thin-film nanocomposite (TFN) membranes are emerging water-purification membranes that could provide enhanced water permeance with similar solute removal over traditional thin-film composite (TFC) membranes. However, the effects of nanofiller incorporation on active layer physico-chemical properties have not been comprehensively studied. Accordingly, we aimed to understand the correlation between nanofillers, active layer physico-chemical properties, and membrane performance by investigating whether observed performance differences between TFN and control TFC membranes correlated with observed differences in physico-chemical properties. Throughout this work, the effects of nanofiller loading, surface area, and size on membrane performance, along with active layer physico-chemical properties, were characterized in TFN membranes incorporated with Linde Type A (LTA) zeolite and zeolitic imidazole framework-8 (ZIF-8). Results show that nanofiller incorporation up to ~0.15 wt% resulted in higher water permeance and unchanged salt rejection, above which salt rejection decreased 0.9–25.6 % and 26.1–48.3 % for LTA-TFN and ZIF-8-TFN membranes, respectively. Observed changes in active layer physico-chemical properties were generally unsubstantial and did not explain observed changes in TFN membrane performance. Therefore, increased water permeance in TFN membranes could be due to preferential water transport through porous structures of nanofillers or along polymer-nanofiller interfaces. These findings offer new insights into the development of high-performance TFN membranes for water/ion separations.

42 ENGINEERING↗
DOE OSTI2022

Atmospheric Brown Carbon on the Tibetan Plateau: Regional Differences in Chemical Composition and Light Absorption Properties

In this work, fine particulate matter (PM 2.5 ) filter samples were collected from two high-altitude remote sites located in the southern (QOMS) and northern (WLG) regions of the Tibetan Plateau (TP) to explore the regional differences in brown carbon (BrC) properties. Chemical differences in BrC composition representative of these two areas were inferred from molecular-level analysis of the PM 2.5 samples using high-performance liquid chromatography coupled with photodiode array and high-resolution mass spectrometry detectors. The results show that more polar BrC chromophores were abundant in QOMS samples, while contributions from polar and less polar chromophores were comparable in WLG samples. A higher mass absorption coefficient of BrC was observed at QOMS than at WLG. Strong BrC chromophores in QOMS samples were identified as oxygenated aromatics and nitrophenol compounds, while organosulfate compounds were found in the WLG sample. The results of this study indicate regional differences in BrC chromophores and provide insights into their sources and chemical processes, which should be considered for predictive understanding and modeling of the radiative forcing of aerosol in the TP area.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1973

Environmental Capability of Liquid Lubricants

The methods available for predicting the properties of liquid lubricants from their structural formulas are discussed. The methods make it possible to design lubricants by forecasting the results of changing the structure and to determine the limits to which liquid lubricants can cope with environmental extremes. The methods are arranged in order of their thermodynamic properties through empirical physical properties to chemical properties.

Beerbower, A.↗
NASA NTRS1975

Classification and properties of iron meteorites

The paper describes the general requirements for a genetically significant scheme for classifying iron meteorites. Some of the properties which may be used to classify iron meteorites are reviewed, and a classification scheme based on Ga-Ni and Ge-Ni plots, taxonomic properties, and chemical properties is proposed. It is found that 95% of the irons can be assigned to a genetic group or an anomalous class.

Scott, E. R. D.↗
NASA NTRSDate not supplied

High-precision predictions of properties of chemically disordered crystals

Multiple scattering theory (MST) combined with density functional theory (DFT) allows to predict properties of chemically disordered materials from the first principles. However, such predictions often suffer from the systematic errors, which depend on crystal geometry. Each computed property of a particular crystal structure typically has a relatively small random error and a larger systematic error. Cancellation of systematic errors allows more accurate predictions. We propose a computational methodology based on the subtraction of the systematic errors in MST. To exemplify it, we apply it to the precipitated alloys. Considering precipitation strengthening in Ni superalloys, we compute the relative enthalpies of the competing Ni3(Al,Ti)1 crystal structures with a chemical disorder on the Al+Ti sublattice. Such predicted composition-structure-property dependencies are useful for the guided design of the next-generation alloys with improved strength. Our predictions are validated by comparison with the results of other DFT methods (having a higher computational cost) and with experiment. We acknowledge funding of the guided design of stronger superalloys for airspace by NASA Aeronautics Research Mission Directorate (ARMD) via Transformational Tools and Technologies (TTT) Project.

Computational↗
NASA NTRS2022

High-precision predictions of properties of chemically disordered crystals

Multiple scattering theory (MST) combined with density functional theory (DFT) allows to predict properties of chemically disordered materials from the first principles. However, such predictions often suffer from the systematic errors, which depend on crystal geometry. Each computed property of a particular crystal structure typically has a relatively small random error and a larger systematic error. Cancellation of systematic errors allows more accurate predictions. We propose a computational methodology based on the subtraction of the systematic errors in MST. To exemplify it, we apply it to the precipitated alloys. Considering precipitation strengthening in Ni superalloys, we compute the relative enthalpies of the competing Ni_3(Al_{1-x}Ti_x)_1 crystal structures with a chemical disorder on the Al+Ti sublattice. Such predicted composition-structure-property dependencies are useful for the guided design of the next-generation alloys with improved strength. Our predictions are validated by comparison with the results of other DFT methods (having a higher computational cost) and with experiment.

density functional theory↗
DOE OSTI2021

Deep Learning Coordinate-Free Quantum Chemistry

Computing quantum chemical properties of small molecules and polymers can provide insights valuable to physicists, chemists and biologists when designing new materials, catalysts, biological probes and drugs. Deep learning can compute quantum chemical properties accurately in a fraction of the time required by commonly used methods such as density functional theory (DFT). However, many of these deep learning architectures require energy minimized molecular geometries as input, which is also computationally expensive, and decreasing the reproducibility and throughput of these methods. In this study, we demonstrate that accurate quantum chemical computations can be performed without optimized geometries by operating in the coordinate-free domain using deep learning on graph encodings. Furthermore, we also find that the choice of graph-encoding architecture substantially affects the performance of these methods. The Wave architecture outperforms graph convolution architectures, particularly on complex molecules. Furthermore, the structures of these graph encoding architectures provide an opportunity to probe an important, outstanding question in quantum mechanics: What types of quantum chemical properties can be represented by local-variable models? We find that Wave, a local-variable model, is more accurately calculates quantum chemical properties. Graph convolutional architectures require global variables, and are not as effective as as Wave. We anticipate that coordinate-free, deep-learning models of quantum chemistry will become valuable tools in chemistry and biology, enabling researchers to rapidly screen chemical databases or identify new molecules using automated, de-novo design algorithms.

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

Chemical structure–property relationships in nanocelluloses

Abstract Nanocelluloses, nanoscale cellulosic particles or fibrils, are an appealing class of nanomaterials with enormous potential for sustainable development. They have great promise to target some of the 17 Sustainable Development Goals outlined by the United Nations. Nanocelluloses are derived from the most abundant biopolymer in the world, cellulose, which have multiple hydroxyl groups on their surface, enabling a vast range of chemical modifications. The interplay between the chemical structure and physicochemical properties of nanocelluloses is crucial to engineering the next generation of green, functional nanomaterials. In this review paper, we provide a comprehensive account of the structure–property relationships in nanocelluloses, which may establish the basis for the design of precision bio‐based materials. Chemical modifications of nanocelluloses, including hydrolysis, oxidation, esterification, amidation, and polymer grafting, provide an array of chemical and physical properties that open opportunities in a diverse spectrum of applications. We review how chemical modifications affect the physicochemical properties of nanocelluloses, such as thermal stability, hydrophobicity, and dispersibility in nonpolar media. Understanding nanocellulose chemical structure–property relationships is integral to the development of sustainable material platforms based on the most renewable biopolymer on earth.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Correlated Electrical and Chemical Nanoscale Properties in Potassium-Passivated, Triple-Cation Perovskite Solar Cells

Perovskite semiconductors are an exciting class of materials due to their promising performance outputs in photovoltaic devices. To boost their efficiency further, researchers introduce additives during sample synthesis, such as KI. However, it is not well understood how KI changes the material and, often, leaves precipitants. To fully resolve the role of KI, multiple microscopy techniques are applied and the electrical and chemical behavior of a Reference (untreated) and a KI-treated perovskite are compared. Upon correlation between electrical and chemical nanoimaging techniques, it is discovered that these local properties are linked to the macroscopic voltage enhancement of the KI-treated perovskite. The heterogeneity revealed in both the local electrical and chemical responses indicates that the additive partially migrates to the surface, yet surprisingly does not deteriorate the performance locally, rather, the voltage response homogeneously increases. The research presented within provides a diagnostic methodology, which connects the nanoscale electrical and chemical properties of materials, relevant to other perovskites, including multication and Pb-free alternatives.

36 MATERIALS SCIENCE↗
NASA NTRS2015

Systematic Relationships Between Lidar Observables and Sizes And Mineral Composition Of Dust Aerosols

The physical and chemical properties of soil dust aerosol particles fundamentally affect their interaction with climate, including shortwave absorption and radiative forcing, nucleation of cloud droplets and ice crystals, heterogeneous formation of sulfates and nitrates on the surface of dust particles, and atmospheric processing of iron into bioavailable forms that increase the productivity of marine phytoplankton. Lidar measurements, such as extinction-to-backscatter, color and depolarization ratios, are frequently used to distinguish between aerosol types with different physical and chemical properties. The chemical composition of aerosol particles determines their complex refractive index, hence affecting their backscattering properties. Here we present a study on how dust aerosol backscattering and depolarization properties at wavelengths of 355, 532 and 1064 nm are related to size and complex refractive index, which varies with the mineral composition of the dust. Dust aerosols are represented by collections of spheroids with a range of prolate and oblate aspect ratios and their optical properties are obtained using T-matrix calculations. We find simple, systematic relationships between lidar observables and the dust size and complex refractive index that may aid the use of space-based or airborne lidars for direct retrieval of dust properties or for the evaluation of chemical transport models using forward simulated lidar variables. In addition, we present first results on the spatial variation of forward-simulated lidar variables based on a dust model that accounts for the atmospheric cycle of eight different mineral types plus internal mixtures of seven mineral types with iron oxides, which was recently implemented in the NASA GISS Earth System ModelE2.

Dust↗
DOE OSTI2026

Handling and Properties of Methanol as a Marine Fuel

Given the increasing concern around greenhouse gas emissions and the decline in the availability of fossil fuels, there is increasing global demand to develop alternate fuels for maritime transportation that are sustainable and which have lower greenhouse gas emissions. Methanol is one such alternative fuel that has garnered considerable attention given its potential to be produced by more sustainable processes and its more favorable greenhouse gas emission profile in comparison with current fossil fuels. Understanding the physical and chemical properties of methanol under a range of conditions is essential for its development as a marine fuel. In this study, we seek to define physical and chemical properties of different methanol samples to simulate real-world storage conditions as these data are lacking in the literature. Several methanol samples were evaluated: nearly pure methanol; International Organization for Standardization (ISO) marine methanol (MM) grades A, B, and C; and methanol plus higher alcohols. We first evaluated all methanol samples for impurities, acetic acid content, density, and distillation range. We then characterized the effects of water absorption and found that methanol can easily absorb unacceptable water content from humid air within hours, necessitating storage conditions that prevent this process. In eight-week aging experiments at 20 °C and 40 °C in ambient air, we did not observe significant oxidation for any of the methanol samples; however, we did observe increases in acid number. We assessed the impact of contamination of methanol with water, marine gas oil (MGO), and an MGO–biodiesel mixture on density, viscosity, distillation range, and lubricity. Finally, we show that MGO contamination of methanol results in a slight increase in sooting tendency. In aggregate, our results provide an in-depth analysis of physical and chemical properties of methanol as well as the impacts of storage conditions and impurities on the properties of fuel methanol.

09 BIOMASS FUELS↗
DOE OSTI2021

Characterizing Reactive Transport Behavior in a Three-Dimensional Discrete Fracture Network

While several studies have linked network and in-fracture scale properties to conservative transport behavior in subsurface fractured media, studies on reactive transport cases remain relatively underdeveloped. In this study, we explore the behavior of an irreversible kinetic reaction during the interaction of two solute plumes, one consisting of species A and the other species B. When the plumes converge, these species react kinetically to form a new species C via A+B→kC. This reactive system is studied using a three-dimensional discrete fracture network (DFN) model coupled with reactive Lagrangian particle tracking. We find that the interplay of network topology and chemical properties of the reactive solutes controls reactive transport processes. The network topology drives species A and B together, and the chemical properties dictate whether and how quickly a reaction occurs. Results demonstrate that reactions are most likely to occur in high-velocity fractures that make up the network backbone. The interplay between species’ chemical properties and transport is characterized by a non-dimensional Damköhler (Da) number. We show that the spatial distribution of reactions is sensitive to Da, which subsequently influences late-time tailing behavior in outlet breakthrough time distributions. The results of this study provide initial insights into how an irreversible reaction occurs during transport in a fracture network, using a methodology that can be applied to study reactive transport in a wide range of fractured media environments and contexts.

58 GEOSCIENCES↗
DOE OSTI2021

Surface Functionalization of Black Phosphorus with Nitrenes: Identification of P=N Bonds Using Isotopic Labeling

Abstract: Surface functionalization of two-dimensional crystals is a key path to tuning their intrinsic physical and chemical properties. However, synthetic protocols and experimental strategies to directly probe chemical bonding in modified surfaces are scarce. Introduced here is a mild, surface-specific protocol for the surface functionalization of few-layer black phosphorus nanosheets using a family of photolytically generated nitrenes (RN) from the corresponding azides. By embedding spectroscopic tags in the organic backbone, a multitude of characterization techniques are employed to investigate in detail the chemical structure of the modified nanosheets, including vibrational (infrared, Raman), Xray (emission, photoelectron), CPMAS NMR, and UV-vis spectroscopy. To directly probe the functional groups introduced on the surface, R fragments were selected such that in conjunction with vibrational spectroscopy, 15N-labeling experiments, and DFT methods, diagnostic P=N vibrational modes indicative of iminophosphorane units on the nanosheet surface could be conclusively identified.

Walz, Kendall↗
DOE OSTI2020

Chespa: Streamlining Expansive Chemical Space Evaluation of Molecular Sets

Thousands of chemical properties can be calculated for small molecules, which can be used to place the molecules within the context of a broader “chemical space.” These definitions vary based on compounds of interest and the goals for the given chemical space definition. Here, we introduce a customizable (i.e., modular) Python module, chespa, built to easily assess different chemical space definitions through cluster-ing of compounds in these spaces and visualize trends of these clusters. To demonstrate this, chespa currently streamlines prediction of vari-ous molecule descriptors (predicted chemical properties, molecular substructures, AI-based chemical space, and chemical class ontology) in order to test 6 different chemical space definitions. Furthermore, we investigated how these varying definitions trend with mass spectrometry (MS)-based observability, i.e., the ability of a molecule to be observed with MS (e.g., as a function of the molecule ionizability), using an example data set from the U.S. EPA's Non-Targeted Analysis Collaborative Trial (ENTACT), where blinded samples had been analyzed previously, providing 1,398 data points. Improved understanding of observability would offer many advantages in small molecule identifica-tion, such as (i) a priori selection of experimental conditions based on suspected sample composition, (ii) the ability to reduce the number of candidate structures during compound identification by removing those less likely to ionize, and, in turn, (iii) a reduced false discovery rate and increased confidence in identifications. Factors controlling observability are not fully understood, making prediction of this property non-trivial and a prime candidate for chemical space analysis. Chespa is available at github.com/pnnl/chespa.

Nunez, Jamie↗