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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 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↗
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.↗
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↗
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.↗
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↗