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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↗
DOE OSTI2012

Structural and chemical properties of the nitrogen-rich energetic material triaminoguanidinium 1-methyl-5-nitriminotetrazolate under pressure

The structural and chemical properties of the bi-molecular, hydrogen-bonded, nitrogen-rich energetic material triaminoguanidinium 1-methyl-5-nitriminotetrazolate C{sub 3}H{sub 12}N{sub 12}O{sub 2} (TAG-MNT) have been investigated at room pressure and under high pressure isothermal compression using powder x-ray diffraction and Raman and infrared spectroscopy. A stiffening of the equation of state and concomitant structural relaxation between 6 and 14 GPa are found to correlate with Raman mode disappearances, frequency discontinuities, and changes in the pressure dependence of modes. These observations manifest the occurrence of a reversible martensitic structural transformation to a new crystalline phase. The onset and vanishing of Fermi resonance in the nitrimine group correlate with the stiffening of the equation of state and phase transition, suggesting a possible connection between these phenomena. Beyond 15 GPa, pressure induces irreversible chemical reactions, culminating in the formation of a polymeric phase by 60 GPa.

08 HYDROGEN↗
DOE OSTI2015

Relationships between the surface electronic and chemical properties of doped 4d and 5d late transition metal dioxides

Density functional theory calculations were performed to elucidate the underlying physics describing the adsorption energies on doped late transition metal dioxide rutiles. Adsorption energies of atomic oxygen on doped rutiles M{sup D}-M{sup H}O{sub 2}, where transition metal M{sup D} is doped into M{sup H}O{sub 2}, were expressed in terms of a contribution from adsorption on the pure oxide of the dopant M{sup D} and perturbations to this adsorption energy caused by changing its neighboring metal cations and lattice parameters to that of the host oxide M{sup H}O{sub 2}, which we call the ligand and strain effects, respectively. Our analysis of atom projected density of states revealed that the t{sub 2g}-band center had the strongest correlation with adsorption energies. We show that charge transfer mediated shifts to the t{sub 2g}-band center describe the ligand effect, and the radii of the atomic orbitals of metal cations can predict the magnitude and direction of this charge transfer. Strain produces systematic shifts to all features of the atom projected density of states, but correlations between the strain effect and the electronic structure were dependent on the chemical identity of the metal cation. The slope of these correlations can be related to the idealized d-band filling. This work elucidates the underlying physics describing adsorption on doped late transition metal oxides and establishes a foundation for models that use known chemical properties for the prediction of reactivity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Insights into the physical and chemical properties of a cement-polymer composite developed for geothermal wellbore applications

To isolate injection and production zones from overlying formations and aquifers during geothermal operations, cement is placed in the annulus between well casing and formations. Wellbore cement can fracture due to chemical and physical stress. A polymer-cement (composite) with self-healing properties, stable at high temperatures, was recently developed by our group. Here we present data on the elemental distribution, mineral composition, internal microstructure, and tensile elasticity of this composite and how it compares to cement without polymer (control cement). Entrapped air porosity was similar between control cement (0.44% by volume) and composite (0.22% by volume). Total voids increased with polymer content in the cement (10% and 25% composite), and polymer was found to fill most of these voids. Polymer appears to act as a (cement hydration) retarder increasing the amount of unhydrated silicate minerals in the matrix. Tensile elasticity was improved for composites as compared to control cement, suggesting that the composite materials perform better under mechanical stress. Density-functional calculations reproduce the drop in Young’s modulus by about 60% observed experimentally when polymer is added to the cement. In addition, calculations indicate that the bonding interactions between the cement and polymer remain stable over the range of strain studied.

Rod, Kenton A. (ORCID:0000000318189094)↗
DOE OSTI2018

Mechanical and Chemical Properties of Harvested Hypalon Cable Jacket Subjected to Accelerated Thermal Aging

For nuclear power plants (NPPs) that are considering second license renewal for their operation beyond 60 years, knowledge of long-term operation, condition monitoring, and viability for the reactor components including reactor pressure vessel, concrete structures, and cable systems is essential. Such knowledge will provide NPPs with a game plan to predict performance and to estimate costs that are associated with monitoring or replacement programs for the affected systems. For cable systems that encompass a wide variety of materials, manufacturers, and in-plant location, accelerated aging of harvested cable jacket and insulation can provide insight as to remaining useful life and methods for monitoring. Accelerated thermal aging in air at temperatures between 80°C and 120°C was carried out on a multi-conductor control rod cable that had been in-service for over 30 years and was made by Boston Insulated Wire with Hypalon cable jacket and ethylene-propylene rubber insulation. From elongation at break (EAB) measurements and supporting Arrhenius analysis of the jacket material, an activation energy of 97.84 kJ/mol was estimated and the time to degradation, as represented by 50% EAB at the expected maximum operating temperature of 45°C, was estimated as consistent with 80 years. These values were slightly below previous measurements and could either be attributed to in-service degradation or variations in material properties from a given manufacturer. In addition, results from indenter modulus measurements and Fourier transform infrared spectroscopy suggest possible markers that could be beneficial to monitor cable conditions.

cable aging, cable insulation, Hypalon, second lic↗
DOE OSTI2014

Control of Thickness and Chemical Properties of Atomic Layer Deposition Overcoats for Stabilizing Cu/γ-Al 2 O 3 Catalysts

Whereas sintering and leaching of copper nanoparticles during liquid-phase catalytic processing can be prevented by using atomic layer deposition (ALD) to overcoat the nanoparticles with AlOx, this acidic overcoat leads to reversible deactivation of the catalyst by resinification and blocking of the pores within the overcoat during hydrogenation of furfural. We demonstrate that decreasing the overcoat thickness from 45 to 5 ALD cycles is an effective method to increase the rate per gram of catalyst and to decrease the rate of deactivation for catalysts pretreated at 673 K, and a fully regenerable copper catalyst can be produced with only five ALD cycles of AlOx. Moreover, although an overcoat of MgOx does not lead to stabilization of copper nanoparticles against sintering and leaching during liquid-phase hydrogenation reactions, the AlOx overcoat can be chemically modified to decrease acidity and deactivation through the addition of MgOx, while maintaining stability of the copper nanoparticles.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
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

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.↗
DOE OSTI2019

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↗
DOE OSTI2019

Effect of the composition and structure of carbofunctional oligodimethylsiloxanes on their colloidal-chemical properties

The effect of the composition and structure of oligodimethylsiloxanes containing carboxy- and amino-functional substituents at the silicon atom on their colloidal-chemical properties was studied. It was established that their colloidal-chemical properties are affected not only by the nature and concentration of functional groups, but also by the molecular weight of the organosilicon compound.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Using Rule-Based Models for Weak Supervised Learning: A ChemNet for Transferable Chemical Property Prediction

With access to large datasets, deep neural networks (DNN) have achieved human-level accuracy in image and speech recognition tasks. However, in chemistry, data is inherently small and fragmented. In this work, we develop an approach of integrating rule-based knowledge into the Chemception CNN model via transfer learning techniques. The resulting model, ChemNet, is a transferable and generalizable deep neural network for chemical property prediction that learns in a semi-supervised manner from large unlabeled chemical databases. When ChemNet is further fine-tuned on 3 smaller datasets to predict chemical properties that it was not originally trained on, we show that ChemNet exceeds the accuracy of existing Chemception CNN models and other contemporary DNN models that were trained using conventional supervised learning approaches. These results indicate that pre-training ChemNet on a large diverse chemical database while incorporating chemistry domain knowledge, enables the development of more generalizable deep neural networks for the prediction of novel chemical properties.

Artificial Intelligence, Cheminformatics, Computat↗
DOE OSTI2013

THE CHEMICAL PROPERTIES OF LOW-REDSHIFT QSOs

We investigate the chemical properties of low-z QSOs, using archival UV spectra obtained with the Hubble Space Telescope and International Ultraviolet Explorer for a sample of 70 Palomar-Green QSOs at z < 0.5. By utilizing the flux ratios of UV emission lines (i.e., N V/C IV, (Si IV+O IV])/C IV, and N V/He II) as metallicity indicators, we compare broad-line region (BLR) gas metallicity with active galactic nucleus (AGN) properties, i.e., black hole (BH) mass, luminosity, and Eddington ratio. We find that BLR metallicity correlates with Eddington ratio while the dependency on BH mass is much weaker. Although these trends of low-z AGNs appear to be different from those of high-z QSOs, the difference between low-z and high-z samples is partly caused by the limited dynamical range of the samples. We find that metal enrichment at the center of galaxies is closely connected to the accretion activity of BHs and that the scatter of metallicity correlations with BH mass increases over cosmic time.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

A STUDY ON THE CHEMICAL PROPERTIES OF BLUE COMPACT DWARF GALAXIES

In this paper, we report our studies on the gaseous and chemical properties of a relatively large sample (53 members) of blue compact dwarf galaxies (BCDs). The results of correlations among the oxygen abundance, stellar mass, gas mass, baryonic mass, and gas fraction are present both for E- and I-type BCDs, which are classified according to Loose and Thuan and show elliptical and irregular outer haloes, respectively. These correlations of I-type BCDs show slopes similar to those of E-type ones. However, in general, E-type BCDs are more gas-poor and metal-rich than I-type ones at a given baryonic mass. Based on these results, we suggest that E-type BCDs, at least a part of them, and I-type ones might be likely at different evolutionary phases and/or having different progenitors. Our investigation of the correlation between oxygen abundance and gas fraction shows that BCDs appear to have not evolved as isolated systems, but to have experienced some gas flows and/or mergers.

79 ASTRONOMY AND ASTROPHYSICS↗
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↗
DOE OSTI2015

Accurate electronic and chemical properties of 3d transition metal oxides using a calculated linear response U and a DFT + U(V) method

We validate the usage of the calculated, linear response Hubbard U for evaluating accurate electronic and chemical properties of bulk 3d transition metal oxides. We find calculated values of U lead to improved band gaps. For the evaluation of accurate reaction energies, we first identify and eliminate contributions to the reaction energies of bulk systems due only to changes in U and construct a thermodynamic cycle that references the total energies of unique U systems to a common point using a DFT + U(V ) method, which we recast from a recently introduced DFT + U(R) method for molecular systems. We then introduce a semi-empirical method based on weighted DFT/DFT + U cohesive energies to calculate bulk oxidation energies of transition metal oxides using density functional theory and linear response calculated U values. We validate this method by calculating 14 reactions energies involving V, Cr, Mn, Fe, and Co oxides. We find up to an 85% reduction of the mean average error (MAE) compared to energies calculated with the Perdew-Burke-Ernzerhof functional. When our method is compared with DFT + U with empirically derived U values and the HSE06 hybrid functional, we find up to 65% and 39% reductions in the MAE, respectively.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2012

Special Chemical Properties of RuOx Nanowires in RuOx/TiO2(110): Dissociation of Water and Hydrogen Production

Recently, there has been a strong interest in understanding the role of mixed-metal oxides in catalysts used for the production of hydrogen through the splitting of water. Here, we investigate the structural and chemical properties of RuO{sub x}/TiO{sub 2}(110) surfaces employing scanning tunneling microscopy, photoemission, and density functional calculations. Ruthenium oxide forms unique wirelike structures on top of TiO{sub 2}(110) which are very reactive toward water dissociation, being able to cleave O-H bonds at a temperature as low as 200 K. The calculated barrier for the dissociation of water on RuO{sub 2} nanowires is <0.05 eV. The presence of easily formable O vacancies in the ruthenium oxide nanowires facilitates the dissociation of water. Furthermore, RuO{sub x}/TiO{sub 2} (110) surfaces are able to catalyze the production of hydrogen through the water-gas shift reaction (H{sub 2}O + CO {yields} H{sub 2} + CO{sub 2}), exhibiting an activity that compares well with the activity found for extended surfaces of copper typically used as benchmarks for studying this reaction.

08 HYDROGEN↗
DOE OSTI2014

A comparative study of physico-chemical properties of CBD and SILAR grown ZnO thin films

Graphical abstract: Schematic model indicating ZnO nanorods by CBD (Z{sub 1}) and nanograins by SILAR (Z{sub 2}). - Highlights: • Simple methods for the synthesis of ZnO thin films. • Comparative study of physico-chemical properties of ZnO thin films prepared by CBD and SILAR methods. • CBD outperforms SILAR method. - Abstract: In the present work, nanocrystalline zinc oxide (ZnO) thin films have been successfully deposited onto glass substrates by simple and economical chemical bath deposition (CBD) and successive ionic layer adsorption reaction (SILAR) methods. These films were further characterized for their structural, optical, surface morphological and wettability properties. The X-ray diffraction (XRD) patterns for both CBD and SILAR deposited ZnO thin films reveal the highly crystalline hexagonal wurtzite structure. From optical studies, band gaps obtained are 2.9 and 3.0 eV for CBD and SILAR deposited thin films, respectively. The scanning electron microscope (SEM) patterns show growth of well defined randomly oriented nanorods and nanograins on the CBD and SILAR deposited samples, respectively. The resistivity of CBD deposited films (10{sup 2} Ω cm) is lower than that of SILAR deposited films (10{sup 5} Ω cm). Surface wettability studies show hydrophobic nature for both films. From the above results it can be concluded that CBD grown ZnO thin films show better properties as compared to SILAR method.

36 MATERIALS SCIENCE↗