ELECTROPHYSICAL PROPERTIES OF HG2MNINTE6 SINGLE CRYSTALS ELECTROPHYSICAL PROPERTIES OF HG2MNINTE6 SINGLE CRYSTALS FOR X- AND G-RADIATION DETECTORS
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JAMES, RALPH B↗
SEARCH · Engineering Papers
Results for “Properties”
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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Effects of Film Properties, Guests and Defects on MOF Electronic Properties .
Abstract not provided.
Allendorf, Mark↗
Characterizing the Relationship Between Coating Structure and Properties: Acoustic and Elastic Properties in Controlled Atmosphere Plasma Sprayed Metals
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Peleg, Emma Esther↗
Assessing Cloud and Precipitation Properties on Temporal and Spatial Scales Using LASSO Simulations over ENA
Low clouds and precipitation representation remain a major source of uncertainty in Earth System Models (ESMs), particularly due to challenges in representing their sub-grid variability and scale-dependent sampling. This study evaluates the performance of preliminary simulations from the Large-Eddy Simulation (LES) ARM Symbiotic Simulation and Observation (LASSO) project over the Eastern North Atlantic (ENA), with a focus on liquid water path (LWP), ice water path (IWP), cloud fraction (CF), and surface precipitation simulated across closed-cell, open-cell, and transitional cloud regimes. Using LES (100 m horizontal grid spacing) driven by ERA5 and MERRA-2 reanalyses, we assess the representativeness of ground-based point observations by analyzing their correspondence to model-resolved spatial and temporal means. Results suggest that observational sampling of at least 6 hours is required to achieve consistency with domain-scale averages, in particular for observations that exhibit pronounced sub-grid heterogeneity, such as precipitation. ERA5-forced simulations exhibit improved spatial coherence and agreement with domain-averaged quantities when compared to MERRA-2 runs, with performance discrepancies largest for convective cloud conditions due to differences in forcing fidelity and temporal resolution. These findings highlight the importance of regime-aware model evaluation strategies and potentially demonstrate how LES can inform observation-model comparison practices and the development of cloud and precipitation parameterizations in ESMs.
Liang, Jiakun [University of Hawai'i at Manoa] (OR↗
Nonadiabatic Dynamics Shines Light on How to Best Harness Perovskite Properties for Use in Optoelectronic Properties
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Material Science↗
Understanding Isomeric Effects on Properties of Aviation Fuels via a Group Contribution Method: Preprint
The molecular composition of aviation fuels, including conventional and sustainable aviation fuels (SAFs), significantly influences their performance, safety, and environmental impact. This study examines the effect of isomeric variation for compounds with the same carbon number and chemical family on key fuel properties, focusing on compounds commonly found in conventional jet fuels and SAFs. A group contribution method (GCM) is employed to predict thermophysical and combustion properties, providing an efficient analytical approach to evaluate the contributions of individual compounds to overall fuel mixture behavior. As part of this work, we introduce FuelLib, an open-source Python tool built around the GCM, to calculate individual compound and fuel mixture properties using various mixing rules. Our work evaluates whether the GCM can capture isomeric effects, that are often overlooked in traditional fuel property estimation. This is particularly important for SAFs, which often are composed of a more limited set of compound classes than conventional fuels, making isomeric differences more critical. Two-dimensional gas chromatography (GCxGC) data, which can be obtained from small fuel samples, provides weight percentages of compounds grouped by chemical family and carbon number rather than detailed information about individual compounds. As a result, assumptions must be made when decomposing GCxGC data into functional groups for GCM applications. Using GCxGC data, we show that the FuelLib tool can be used to predict the fuel properties of conventional jet fuels, with validation against experimental data. The provided tool enables researchers to predict fuel properties of candidate fuels and supports the design of new SAFs at both the component and mixture levels. This capability provides a foundation for studying fuel and combustion properties during SAF development, reducing reliance on costly experimental methods and advancing progress toward certification of new SAFs.The molecular composition of aviation fuels, including conventional and sustainable aviation fuels (SAFs), significantly influences their performance, safety, and environmental impact. This study examines the effect of isomeric variation for compounds with the same carbon number and chemical family on key fuel properties, focusing on compounds commonly found in conventional jet fuels and SAFs. A group contribution method (GCM) is employed to predict thermophysical and combustion properties, providing an efficient analytical approach to evaluate the contributions of individual compounds to overall fuel mixture behavior. As part of this work, we introduce FuelLib, an open-source Python tool built around the GCM, to calculate individual compound and fuel mixture properties using various mixing rules. Our work evaluates whether the GCM can capture isomeric effects, that are often overlooked in traditional fuel property estimation. This is particularly important for SAFs, which often are composed of a more limited set of compound classes than conventional fuels, making isomeric differences more critical. Two-dimensional gas chromatography (GCxGC) data, which can be obtained from small fuel samples, provides weight percentages of compounds grouped by chemical family and carbon number rather than detailed information about individual compounds. As a result, assumptions must be made when decomposing GCxGC data into functional groups for GCM applications. Using GCxGC data, we show that the FuelLib tool can be used to predict the fuel properties of conventional jet fuels, with validation against experimental data. The provided tool enables researchers to predict fuel properties of candidate fuels and supports the design of new SAFs at both the component and mixture levels. This capability provides a foundation for studying fuel and combustion properties during SAF development, reducing reliance on costly experimental methods and advancing progress toward certification of new SAFs.
33 ADVANCED PROPULSION SYSTEMS↗
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.↗
Property Measurements of NaCl-UCl 3 and LiF-NaF-KF Molten Salts Doped with Surrogate Fission Products
Knowing thermophysical and thermochemical property values of salts over the expected range of operating temperatures is essential to modeling and simulation efforts that support the commercialization of molten salt reactor (MSR) technologies. Salt properties being measured at Argonne include thermal transitions, phase behavior, heat capacity, density, surface tension, volumetric thermal expansion, thermal diffusivity, thermal conductivity, and viscosity. The properties of several salts of interest including NaCl-UCl 3 and LiF-NaF-KF (FLiNaK) have been measured at Argonne and reported previously. These property measurements are suitable for use in evaluating reactor performance during startup and the early operating life of the reactor. The physical and chemical behavior of the fuel salt is altered by the buildup of fission products and the resulting changes in heat transfer and other properties must be understood by reactor developers. This report summarizes melting behavior, heat capacity, and thermal diffusivity values that were measured for mixtures of FLiNaK and NaCl-UCl 3 salts that have been doped with surrogate fission products. Additional heat capacity measurements were also performed on a separate mixture of FLiNaK without dopants. Measured properties are suitable for incorporation into the Molten Salt Thermal Properties Database–Thermochemical (MSTDB–TC) and the Molten Salt Thermal Properties Database–Thermophysical (MSTDB–TP) to support the development of MSRs. Salts were prepared by doping eutectic mixtures of NaCl-UCl 3 and FLiNaK with surrogate fission products. These salts were prepared for use in salt spill experiments being performed at Argonne and were subsequently used for thermal property measurements. Thermal transitions and heat capacities were measured by using differential scanning calorimetry (DSC) and thermal diffusivity was measured by using laser flash analysis (LFA) at temperatures spanning the typical MSR operating range. The measured property values of the doped salts were compared to measured property values of salts without fission products to quantify the effects of the doped fission products.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Using Computationally-Determined Properties for Machine Learning Prediction of Self-Diffusion Coefficients in Pure Liquids
The ability to predict transport properties of liquids quickly and accurately will greatly improve our understanding of fluid properties both in bulk and complex mixtures, as well as in confined environments. Such information could then be used in the design of materials and processes for applications ranging from energy production and storage to manufacturing processes. As a first step, we consider the use of machine learning (ML) methods to predict the diffusion properties of pure liquids. Recent results have shown that Artificial Neural Networks (ANNs) can effectively predict the diffusion of pure compounds based on the use of experimental properties as the model inputs. In the current study, a similar ANN approach is applied to modeling diffusion of pure liquids using fluid properties obtained exclusively from molecular simulations. A diverse set of 102 pure liquids is considered, ranging from small polar molecules (e.g., water) to large nonpolar molecules (e.g., octane). Self-diffusion coefficients were obtained from classical molecular dynamics (MD) simulations. Since nearly all the molecules are organic compounds, a general set of force field parameters for organic molecules was used. The MD methods are validated by comparing physical and thermodynamic properties with experiment. Computational input features for the ANN include physical properties obtained from the MD simulations as well as molecular properties from quantum calculations of individual molecules. Furthermore, fluid properties describing the local liquid structure were obtained from center of mass radial distribution functions (COM-RDFs). Feature sensitivity analysis revealed that isothermal compressibility, heat of vaporization, and the thermal expansion coefficient were the most impactful properties used as input for the ANN model to predict the MD simulated self-diffusion coefficients. The MD-based ANN successfully predicts the MD self-diffusion coefficients with only a subset (2 to 3) of the available computationally determined input features required. A separate ANN model was developed using literature experimental self-diffusion coefficients as model targets. Although this second ML model was not as successful due to a limited number of data points, a good correlation is still observed between experimental and ML predicted self-diffusion coefficients.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Heat transfer and transport property contrast effects on the compressible Rayleigh-Taylor instability
In extreme environments, the Rayleigh-Taylor instability (RTI) may occur under large variations in density and temperature and with fluid transport properties strongly dependent on temperature. Direct numerical simulations of the 3D fully compressible RTI are conducted, examining the idealized configuration of a hotter, less dense fluid pushing against a colder, denser fluid. Various temperature ratios and transport property configurations are explored to examine how heat conduction, large variations in transport properties, and sudden changes in transport properties can affect the evolution of the mixing layer. Nonuniform fluid expansion and contraction induced by heat transfer can significantly affect local density differences and overall instability growth, causing profile asymmetries about the initial interface for flow and mixing statistics. The departures from classical self-similar development of the instability along with misalignment between regions of mixing and regions of most intense turbulent activity caused by both heat transfer and transport property contrasts are examined. After sudden changes in fluid transport properties, which may occur as a result of rapid heating (e.g., in inertial confinement fusion), the flow quickly responds and begins to relax towards quasi-self-similar late-time evolution. For many dynamical quantities such as vorticity and dissipation, this late-time evolution resembles that of the configuration that already started with the final transport property magnitudes, suggesting that these quantities depend only on the transport properties and not on past flow history, provided that the density field distributions for the flows remain similar. On the other hand, the mixing evolution after the transport property change is unique, implying that both property magnitudes and previous history are impactful on the mixing. In conclusion, these simulations demonstrate how various temperature-related effects are extremely important to consider in compressible RTI flows with large temperature variations.
42 ENGINEERING↗
Property Measurements of LiF-NaF-KF Molten Salts Doped with Corrosion Products and Oxygen
Measurements of thermophysical properties of molten salts are needed for modeling and simulation activities that support the development of molten salt reactor (MSR) technologies. Properties of interest including transition temperatures, phase behavior, heat capacity, density, volumetric thermal expansion, surface tension, viscosity, thermal diffusivity, thermal conductivity, and vapor pressure are being performed at Argonne. Results of these property measurements are suitable for use in evaluating reactor performance during startup and the early operating life of the reactor. Ingressions of oxygen and moisture into the fuel salt are expected to occur at different times during the operating life of the reactor due to system leaks, maintenance, and refueling activities. The presence of these environmental contaminants induces corrosion of structural materials. The introduction of corrosion-derived species, oxygen and moisture is expected to affect the physical and chemical properties of the salt and operation of the reactor. Previous work performed at Argonne evaluated the effects of fission product dopants on the thermal properties of eutectic LiF-NaF-KF (FLiNaK). Properties of FLiNaK are commonly used to represent those of fluoride-bearing fuel salts. Metallic corrosion products such as chromium and nickel ions together with dissolved oxygen are expected to affect system redox differently than the accumulation of fission products. Work summarized in this report was performed to measure the effects of corrosion products and dissolved oxygen on the phase transitions, and specific heat capacity of FLiNaK. Thermophysical property measurements were made using four salts that were prepared by doping aliquots of a eutectic mixture of FLiNaK with surrogate corrosion products. Controlled additions of CrF 3 and NiF 2 were used as surrogates for corrosion product contamination anticipated during extended reactor operations in which fuel salt is in contact with steel reactor components. Controlled additions of UF 4 from two sources containing known amounts of UO 2 at different concentrations were used to represent oxygen contamination. The phase transitions and specific heat capacities of the four salts were measured by using differential scanning calorimetry. Measurements were made at temperatures spanning the range of 500−900 °C, which is the expected operating range of MSRs. Measured property values were compared to values measured previously with eutectic FLiNaK without dopants. Differences between property values measured for the doped and non-doped salts were compared with the uncertainties of the measurements to determine the significance of the effect of corrosion products and oxygen on salt properties.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Statistical Homogenization of Elastic and Fracture Properties of a Sample Selective Laser Melting Material
Selective laser melting (SLM) is an additive manufacturing technique commonly used in the rapid prototyping of components. The complexity of the SLM microstructure poses a unique challenge to deriving effective mechanical properties at different length scales. Representative volume elements (RVEs) are often used to homogenize the material properties of composites. Instead of RVEs, we use statistical volume elements (SVEs) to homogenize the elastic and fracture properties of the material. This relates the inherent variation of a material’s microstructure to the variation in its mechanical properties at different observation scales. The convergence to the RVE limit is examined from two perspectives: the stability of the mean value as the SVE size increases for the mean-based approach, and the tendency of the normalized variation in homogenized properties to zero as the SVE size increases for the variation-based approach. Fracture properties tend to make the RVE limit slower than do elastic properties from both perspectives. There are also differences between vertical (normal to printing plane) and horizontal (in-plane) properties. While the elastic properties tend to make the RVE limit faster for the horizontal direction, i.e., having a smaller variation and more stable mean value, the fracture properties exhibit the opposite effect. We attributed these differences to the geometry of the melt pools.
36 MATERIALS SCIENCE↗
Mechanical and Dielectric Properties of Aligned Electrospun Fibers
This review paper examines the current state-of-the-art in fabrication of aligned fibers via electrospinning techniques and the effects of these techniques on the mechanical and dielectric properties of electrospun fibers. Molecular orientation, system configuration to align fibers, and post-drawing treatment, like hot/cold drawing process, contribute to better specific strength and specific stiffness properties of nanofibers. The authors suggest that these improved, aligned nanofibers, when applied in composites, have better mechanical and dielectric properties for many structural and multifunctional applications, including advanced aerospace applications and energy storage devices. For these applications, most fiber alignment electrospinning research has focused on either mechanical property improvement or dielectric property improvement alone, but not both simultaneously. Relative to many other nanofiber formation techniques, the electrospinning technique exhibits superior nanofiber formation when considering cost and manufacturing complexity for many situations. Even though the dielectric property of pure nanofiber mat may not be of general interest, the analysis of the combined effect of mechanical and dielectric properties is relevant to the present analysis of improved and aligned nanofibers. A plethora of nanofibers, in particular, polyacrylonitrile (PAN) electrospun nanofibers, are discussed for their mechanical and dielectric properties. In addition, other types of electrospun nanofibers are explored for their mechanical and dielectric properties. An exploratory study by the author demonstrates the relationship between mechanical and dielectric properties for specimens obtained from a rotating mandrel horizontal setup.
77 NANOSCIENCE AND NANOTECHNOLOGY↗
EvoDiffMol: evolutionary diffusion framework for 3D molecular design with optimized properties
Designing molecules with specific target properties remains a fundamental challenge in computational chemistry. While existing approaches show promise, most rely on simplified representations like SMILES strings or 2D graphs that lack essential three-dimensional geometric information. We present EvoDiffMol, a computational framework that integrates evolutionary algorithms with three-dimensional diffusion models for property-driven molecular generation. The method operates through adaptive evolutionary optimization, where population-based selection guides the generation process toward desired property landscapes. EvoDiffMol supports both unconstrained molecular design and scaffold-constrained generation that preserves fixed substructures while optimizing complementary regions. Comprehensive evaluation demonstrates exceptional performance, achieving the highest drug-likeness score (0.94) among all compared state-of-the-art methods while maintaining excellent validity, uniqueness, and novelty. Beyond single property optimization, the framework demonstrates flexible multi-property optimization capabilities, simultaneously controlling multiple molecular descriptors including synthetic accessibility, lipophilicity, topological polar surface area, and clinically relevant ADMET properties such as cardiotoxicity (hERG) and intestinal permeability (Caco-2). This adaptability spans from simple descriptors to practical pharmaceutical endpoints without requiring complete model retraining. The framework achieves precise control over target property values, generating molecules with properties closely matching specified targets for both single and multiple descriptors. Scaffold-constrained experiments preserve fixed molecular cores while maintaining effective property optimization. The three-dimensional representation offers advantages in maintaining structural validity during iterative optimization, with potential for geometry-aware applications in materials science and drug discovery.
3D molecular generation↗
Accelerated thermal property mapping of TRISO advanced nuclear fuel
TRistructural ISOtropic (TRISO) fuel is a leading-edge nuclear fuel form representing a departure from the more traditional nuclear fuel forms utilized in the reactor fleet of today. Rather than a monolithic fuel pellet of uranium dioxide, integral fuel forms containing TRISO fuel are composed of thousands of microencapsulated uranium-bearing fuel kernels and individually coated with multiple layers of pyrolytic carbon and silicon carbide. These multilayered ceramic coatings serve as an environmental barrier to ensure radioactive and chemically reactive fission products are contained within the reactor fuel elements, but also participate in the transfer of heat generated in the nuclear fuel to the coolant – the primary purpose of a nuclear reactor. Since traditional thermal property measurement techniques, such as laser flash analysis, would be unable to resolve the thermal properties of the individual TRISO coating layers, a simplified frequency-domain thermoreflectance technique has been developed to rapidly map the thermal properties of TRISO particles. Using this technique, the thermal properties of TRISO particles have been mapped from room temperature up to 1000 °C to examine the spatial variation and temperature-dependency of the thermal properties within each layer. Additionally, spatial-domain thermoreflectance was used to examine the anisotropy of the thermal properties for each layer at different locations within a single TRISO particle, and across multiple TRISO particles to assess the intra- and inter-particle uniformity of thermal properties, respectively. To elucidate the underlying causes for the measured variations in thermal properties, scanning electron microscopy and Raman spectroscopy were used to examine variations in microstructure and chemical bonding within the different coating layers. Results from this work are then compared with previous examinations of TRISO fuel particles and microstructurally driven mechanisms for the variations in the measured thermal properties of the different carbonaceous layers are discussed.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Simulations of activities, solubilities, transport properties, and nucleation rates for aqueous electrolyte solutions
This article reviews recent molecular simulation studies of "collective" properties of aqueous electrolyte solutions, specifically free energies and activity coefficients, solubilities, nucleation rates of crystals, and transport coefficients. These are important fundamental properties for biology and geoscience, but also relevant for many technological applications. Their determination from molecular-scale calculations requires large systems and long sampling times, as well as specialized sampling algorithms. As a result, such properties have not typically been taken into account during optimization of force field parameters; thus, they provide stringent tests for the transferability and range of applicability of proposed molecular models. There has been significant progress on simulation algorithms to enable the determination of these properties with good statistical uncertainties. Comparisons of simulation results to experimental data reveal deficiencies shared by many commonly used models. Moreover, there appear to exist specific tradeoffs within existing modeling frameworks, so that good prediction of some properties is linked to poor prediction for specific other properties. For example, non-polarizable models that utilize full charges on the ions generally fail to predict accurately both activity coefficients and solubilities; the concentration dependence of viscosity and diffusivity for these models is also incorrect. Scaled-charge models improve the dynamic properties and could also perform well for solubilities, but fail in the prediction of nucleation rates. Even models that do well at room temperature for some properties generally fail to capture their experimentally observed temperature dependence. Finally, the main conclusion from the present review is that qualitatively new physics will need to be incorporated in future models of electrolyte solutions to allow description of collective properties for broad ranges of concentrations, temperatures, and solvent conditions.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Quality Assessment of Molten Salt Thermochemical Property Data
Recommendations are made to enhance user confidence in property values included in the Molten Salt Thermal Database-Thermochemical Properties (MSTDB-TC). Specifically, it is recommended that all available measured values be included with a transparent assessment of each to arrive at a preferred value for each salt composition in the database. The use of a quality assessment and ranking system is recommended for data and property values compiled in the MSTDB-TC that is analogous to the approach developed for data and property values included in the MSTDB-Thermophysical Properties (TP) database. The approach includes assessments of five features of data collection: the method used to measure the property value, calibration of devices used to make the measurements, the quantified uncertainty of the results, control of the environmental conditions, and determination of the salt composition. The quality of each facet is ranked as High, Moderate, or Incomplete based on objective metrics. The overall quality of the dataset is ranked based on rankings of the five data features and traceability of the reported property value, with rankings of A and B indicating the data and property value are deemed suitable for quantitative use, C indicating the value is computed and not validated and U indicating uncertainties restrict the recommended use to qualitative purposes. Sufficient information should be provided or cited within each data set included in the database to verify the reported property value. The recommended approach is suitable for assessing data already in the MSTDB-TC and screening new entries.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Quality Ranking of Unary Chloride Salt Property Data Included in MSTDB-TP
Molten salt reactor developers rely on thermal property data to design, license and operate the reactors. The Molten Salt Thermal Database-Thermophysical Properties (MSTDB-TP) was established under the DOE Nuclear Energy Advanced Modeling and Simulation (NEAMS) program and is managed by Oak Ridge National Laboratory to serve as a single source of thermophysical property values measured for a wide variety of molten salt systems for use by researchers, molten salt reactor developers, and regulators. These properties include density, viscosity and thermal diffusivity and conductivity. Published measurements of molten salt properties are lacking for many salts of interest and the data that are available are often inconsistent. This creates a challenge for MSR developers when determining which property values to use when designing their reactors. It is the purpose of this work to apply a consistent ranking system to all data entries that indicates the quality of property values listed in the database. These rankings will be the technical basis for down-selections by the database developers and alert users about the quality of the available property values. MSTDB-TP collects all available property data and indicates preferred data sets or correlations. However, all available data sets are included in the database. Quality assessments and rankings are being applied to data in MSTDB-TP to provide an indication of the quality of each data set independent of consistency with other data. Previous reports detailed the ranking system that was followed and assessments of unary fluoride data sets. Documentation of the quality of data in MSTDB-TP was continued by reviewing and assessing all available sources of density, viscosity and thermal diffusivity or conductivity values for unary chloride salts in MSTDB-TP V3.0 using the same criteria.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗