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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Pilot-Scale Pelleting Tests on High-Moisture Pine, Switchgrass, and Their Blends: Impact on Pellet Physical Properties, Chemical Composition, and Heating Values

In this study, we evaluated the pelleting characteristics of southern yellow pine (SYP), switchgrass (SG), and their blends for thermochemical conversion processes, such as pyrolysis and gasification. Using a pilot-scale ring-die pellet mill, we specifically assessed the impact of blend moisture, length-to-diameter (L/D) ratio in the pellet die, and ratio of pine to SG on the physico-chemical properties of the resulting pellets. We found that an increase in pine content by 25–50% marginally affected the bulk density; however, it also led to an increase in calorific value by 7% and a decrease in ash content by 72%. A moisture content of 25% (wet basis) and an L/D ratio of 5 resulted in poor pellet durability at <90% and bulk density values of <500 kg/m 3 , but increasing the L/D ratio to 9 and lowering the moisture content to 20% (w.b.) improved the pellet durability to >90% and the bulk density to >500 kg/m 3 . Blends with ≥50% pine content resulted in lower energy consumption, while a lower L/D ratio resulted in higher pelleting energy. Based on these findings, we successfully demonstrated the high-moisture pelleting of 2.5 ton of pine top residues blended with SG at 60:40 and 50:50 ratios. The quality of the pellets was monitored off-line and at-line by near infrared (NIR) spectroscopy. Multivariate models constructed by combining the NIR data and the pelleting process variables could successfully predict the pine content (R 2 = 0.99), higher heating value (R 2 = 0.98), ash (R 2 = 0.95), durability (R 2 = 0.94), and bulk density (R 2 = 0.86) of the pellets. Thus, we established how blending and densification of SYP and SG biomass could improve feedstock specifications and that NIR spectroscopy can effectively monitor the pellet properties during the high-moisture pelleting process.

09 BIOMASS FUELS↗
NASA NTRS1985

Crystallization, Optical and Chemical Properties of Fluoride Glasses

Fluoride glasses have great promise as infrared optical components, especially fibers, because they are transparent to 8 micrometers and higher. In order to optimize properties, different glass compositions are needed. Some are hard to form in a container, and may possibly be formable in a containerless furnace. Understanding of crystallization with and without a container could lead to glasses with optimum properties. Chemical durability (attack by water) can limit or extend the applicability of fluoride glasses. Progress to date is given.

Doremus, R. H.↗
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↗
NASA NTRS1996

International Research Project on the Effects of Chemical Ageing of Polymers on Performance Properties: Chemical and Thermal Analysis

Work during the past six months has included significant research in several areas aimed at further clarification of the aging and chemical failure mechanism of thermoplastics (PVDF or Tefzel) pipes. Among the areas investigated were the crystallinity changes associated with both the Coflon and Tefzel after various simulated environmental exposures using X-ray diffraction analysis. We have found that significant changes in polymer crystallinity levels occur as a function of the exposures. These crystallinity changes may have important consequences on the fracture, fatigue, tensile, and chemical resistance of the materials. We have also noted small changes in the molecular weight distribution. Again these changes may result in variations in the mechanical and chemical properties in the material. We conducted numerous analytical studies with methods including X-ray Diffraction, Gel Permeation Chromatography, Fourier Transform Infrared Spectroscopy, Ultra- Violet Scanning Analysis, GC/Mass Spectrometry, Differential Scanning Calorimetry and Thermomechanical Analysis. In the ultra-violet analysis we noted the presence of an absorption band indicative of triene formation. We investigated a number of aged samples of both Tefzel and Coflon that were forwarded from MERL. We also cast films at SWT and subjected these films to a refluxing methanol 1% ethylene diamine solution. An updated literature search was conducted using Dialog and DROLLS to identify any new papers that may have been published in the open literature since the start of this project. The updated literature search and abstracts are contained in the Appendix section of this report.

Bulluck, J. W.↗
NASA NTRS1984

Role of fuel chemical properties on combustor radiative heat load

In an attempt to rigorously study the fuel chemical property influence on combustor radiative heat load, UTRC has conducted an experimental program using 25 test fuels. The burner was a 12.7-cm dia cylindrical device fueled by a single pressure-atomizing injector. Fuel physical properties were de-emphasized by selecting injectors which produced highly-atomized, and hence rapidly-vaporizing sprays. The fuels were specified to cover the following wide ranges of chemical properties: hydrogen, 9.1 to 15- (wt) pct; total aromatics, 0 to 100 (vol) pct; and naphthalene, 0 to 30 (vol) pct. They included standard fuels, specialty products and fuel blends. Fuel naphthalene content exhibited the strongest influence on radiation of the chemical properties investigated. Smoke point was a good global indicator of radiation severity.

Rosfjord, T. J.↗
NASA NTRS1984

Role of fuel chemical properties on combustor radiative heat load

In an attempt to rigorously study the fuel chemical property influence on combustor radiative heat load, United Technologies Research Center (UTRC) has conducted an experimental program using 25 test fuels. The burner was a 12.7-cm dia cylindrical device fueled by a single pressure-atomizing injector. Fuel physical properties were de-emphasized by selecting injectors which produced high-atomized, and hence rapidly-vaporizing sprays. The fuels were specified to cover the following wide ranges of chemical properties; hydrogen, 9.1 to 15- (wt) pct; total aromatics, 0 to 100 (vol) pct; and naphthalene, 0 to 30 (vol) pct. They included standard fuels, specialty products and fuel blends. Fuel naphthalene content exhibited the strongest influence on radiation of the chemical properties investigated. Smoke point was a good global indicator of radiation severity.

Rosfjord, T. J.↗
NASA NTRS2016

Method for Non-Invasive Determination of Chemical Properties of Aqueous Solutions

A method for non-invasively determining a chemical property of an aqueous solution is provided. The method provides the steps of providing a colored solute having a light absorbance spectrum and transmitting light through the colored solute at two different wavelengths. The method further provides the steps of measuring light absorbance of the colored solute at the two different transmitted light wavelengths, and comparing the light absorbance of the colored solute at the two different wavelengths to determine a chemical property of an aqueous solution.

Todd, Paul W.↗
DOE OSTI2020

Deep learning to generate in silico chemical property libraries and candidate molecules for small molecule identification in complex samples

Comprehensive and unambiguous identification of small molecules in complex samples will revolutionize our understanding of the role of metabolites in biological systems. Existing and emerging technologies have enabled measurement of chemical properties of molecules in complex mixtures and, in concert, are sensitive enough to resolve even stereoisomers. Despite these experimental advances, small molecule identification is inhibited by (i) chemical reference libraries (e.g. mass spectra, collision cross section, and other measurable property libraries) representing <1% of known molecules, limiting the number of possible identifications, and (ii) the lack of a method to generate candidate matches directly from experimental features (i.e. without a library). To this end, we developed a variational autoencoder (VAE) to learn a continuous numerical, or latent, representation of molecular structure to expand reference libraries for small molecule identification. We extended the VAE to include a chemical property decoder, trained as a multitask network, in order 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, with its focus on properties that can be obtained from experimental measurements (m/z, CCS) paired with its training paradigm, which involved 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, as experimental property data is limited. Finally, the network is further refined by being trained with the experimental data. This allows the network to learn as much as possible at each stage, enabling success with progressively smaller datasets without overfitting. Once trained, the network can be used to predict chemical properties directly from structure, as well as generate candidate structures with desired chemical properties. Our approach is orders of magnitude faster than first-principles simulation for CCS property prediction. Additionally, the ability to generate novel molecules along manifolds, defined by chemical property analogues, positions DarkChem as highly useful in a number of application areas, including metabolomics and small molecule identification, drug discovery and design, chemical forensics, and beyond.

Colby, Sean M.↗
NASA NTRS1984

Influence of fuel chemical properties on gas turbine combustors

In an attempt to rigorously study the fuel chemical property influence, UTRC (United Technologies Research Center) (under contract to NASA Lewis Research Center) has conducted an experimental program using 25 test fuels. The burner was a 12.7 cm dia cylindrical device consisting of six sheet metal louvers. A single pressure atomizing injector and air swirler were centrally mounted with the conical dome. Fuel physical properties were de-emphasized by using fuel injectors which produced highly atomized, and hence rapidly vaporizing sprays. A substantial fuel spray characterization effort was conducted to allow selection of nozzles which assured that such sprays were achieved for all fuels. The fuels were specified to cover the following wide ranges of chemical properties: hydrogen, 9.1 to 15 (wt) pct; total aromatics, 0 to 100 (vol) pct; and naphthalene, 0 to 30 (vol) pct. They included standard fuel (e.g., Jet A, JP4), specialty products (e.g., decalin, xylene tower bottoms) and special fuel blends. Included in this latter group were six, 4-component blends prepared to achieve parametric variations in fuel hydrogen, total aromatics and naphthalene contents.

Rosfjord, T. J.↗
NASA NTRS1978

Definition and effect of chemical properties of surfaces in friction, wear, and lubrication

Chemical properties relative to their role in adhesion, friction, wear and lubrication discussed in this paper will include: (1) adsorption, both physical and chemical; (2) orientation of the solid as well as the lubricant; (3) surface energy; (4) surface segregation; (5) surface versus bulk metallurgical effects; (6) electronic nature of the surface; and (7) bonding mechanisms.

Buckley, D. H.↗
DOE OSTI2022

S $^{5}$: The Orbital and Chemical Properties of One Dozen Stellar Streams

We report the kinematic, orbital, and chemical properties of 12 stellar streams with no evident progenitors using line-of-sight velocities and metallicities from the Southern Stellar Stream Spectroscopic Survey (S $^{5}$), proper motions from Gaia EDR3, and distances derived from distance tracers or the literature. This data set provides the largest homogeneously analyzed set of streams with full 6D kinematics and metallicities. All streams have heliocentric distances between ∼10 and 50 kpc. The velocity and metallicity dispersions show that half of the stream progenitors were disrupted dwarf galaxies (DGs), while the other half originated from disrupted globular clusters (GCs), hereafter referred to as DG and GC streams. Based on the mean metallicities of the streams and the mass–metallicity relation, the luminosities of the progenitors of the DG streams range between those of Carina and Ursa Major I (−9.5 ≲ M $_{V}$ ≲ −5.5). Four of the six GC streams have mean metallicities of Fe/H < −2, more metal poor than typical Milky Way (MW) GCs at similar distances. Interestingly, the 300S and Jet GC streams are the only streams on retrograde orbits in our dozen-stream sample. Finally, we compare the orbital properties of the streams with known DGs and GCs in the MW, finding several possible associations. Some streams appear to have been accreted with the recently discovered Gaia–Enceladus–Sausage system, and others suggest that GCs were formed in and accreted together with the progenitors of DG streams whose stellar masses are similar to those of Draco to Carina (∼10$^{5}$–10$^{6}$ M $_{⊙}$).

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1997

A survey of the chemical properties of the M17 and Cepheus A cloud cores

We present the results of a systematic survey of the chemical properties of two giant molecular cloud (GMC) cores in M17 and Cepheus A. In all, we have mapped the emission from 32 molecular transitions of 13 molecules and seven isotopic variants over a 4' x 5' region in each core. Each map includes known sites of massive star formation, as well as the more extended quiescent material. In M17 most molecules have emission peaks away from the H II region/molecular cloud interface, while two species, HC3N and CH3C2H, deviate from this structure with sharp maxima closer to this interface. In Cepheus A the core is influenced by a compact high-velocity molecular outflow and a more extended low-velocity flow. The molecular emission distributions in this source are generally quite similar, with most molecules peaking near the center of the core to the east of the compact H II region HW 2. A few molecules, SO, CH3OH, H13CN, and C18O, have more extended emission. Only two molecules, CO and HCO+, appear to trace the high- and low-velocity outflows; all other species are tracing the quiescent core. We have used the results of previous studies of the density and temperature of the dense gas in the same cloud cores to derive accurate abundances relative to CO for several positions in each core. The principal result is that the chemical composition of all the cores we have surveyed (which include OMC-1 as well as M17 and Cepheus A) show remarkable similarity, both within a given core and among the cores. This suggests that the chemical processes are similar in quiescent GMC core material. In M17 the lack of variation of molecular abundances is remarkable because the radiation field and the gas temperature are known to vary appreciably throughout the surveyed region, suggesting that the bulk of the emission arises from gas that is well shielded from radiation.

Non-NASA Center↗
NASA NTRS1978

Chemical properties and methods of analysis of refractory compounds

Reactions involving refractory metals and the alloys based on them are discussed. Chemical, electrochemical, photometric, spectrophotometric, and X-ray analysis are among the methods described for analyzing the results of the reactions and for determining the chemical properties of these materials.

Samsonov, G. V.↗
DOE OSTI2023

Spectroscopic and Chemical Properties of Ionic Liquids: Computational Study

A brief account is given of highlights of our computational efforts – often in collaboration with experimental groups – to understand spectroscopic and chemical properties of ionic liquids (ILs). Molecular dynamics, including their inhomogeneous character, responsible for key spectral features observed in dielectric absorption, infra-red (IR) and fluorescence correlation spectroscopy (FCS) measurements are elucidated. Mechanisms of chemical processes involving imidazolium-based ILs are illustrated for CO 2 capture and related reactions, transesterification of cellulose, and Au nanocluster-catalyzed Suzuki cross-coupling reaction with attention paid to differing roles of IL ions. A comparison with experiments is also made.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗