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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Chemical mixture exposure patterns and obesity among U.S. adults in NHANES 2005–2012

The effect of chemical exposure on obesity has raised great concerns. Real-world chemical exposure always imposes mixture impacts, however their exposure patterns and the corresponding associations with obesity have not been fully evaluated. To discover obesity-related mixed chemical exposure patterns in the general U.S. population. Sparse Decompositional Regression (SDR), a model adapted from sparse representation learning technique, was developed to identify exposure patterns of chemical mixtures with exclusion (non-targeted model) and inclusion (targeted model) of health outcomes. We assessed the relationships between the identified chemical mixture patterns and obesity-related indexes. We also conducted a comprehensive evaluation of this SDR model by comparing to the existing models, including generalized linear regression model (GLM), principal component analysis (PCA), and Bayesian kernel machine regression (BKMR). Eight core exposure patterns were identified using the non-targeted SDR model. Patterns of high levels of MEP, high levels of naphthalene metabolites (ΣOH-Nap), and a pattern of high exposure levels of MCOP, MCNP, and MCPP were positively associated with obesity. Patterns of high levels of BP3, and a pattern of higher mixed levels of MPB, PPB, and MEP were found to have negative associations. Associations were strengthened using the targeted SDR model. In the single chemical analysis by GLM, BP3, MBP, PPB, MCOP, and MCNP showed significant associations with obesity or body indexes. The SDR model exceeded the performance of PCA in pattern identification. Both SDR and BKMR identified a positive contribution of ΣOH-Nap and MCOP, as well as a negative contribution of BP3 and PPB to obesity. Our study identified five core exposure patterns of chemical mixtures significantly associated with obesity using the newly developed SDR model. The SDR model could open a new avenue for assessing health effects of environmental mixture contaminants.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Behavior of astatine and bismuth in non-conventional solvents: Extraction into imidazolium-based ionic liquid and methyl anthranilate with active pharmaceuticals binary mixtures from nitric acid media

Astatine is one of the least chemically studied elements and its behavior in the presence of non- conventional solvents has not been investigated before. This work considers both hydrophobic ionic liquids and binary mixtures as alternatives to conventional solvents. The study is based on the extraction of astatine and bismuth (target material required to produce astatine) into imidazolium-based ionic liquid, and binary mixtures formed by active pharmaceuticals (ibuprofen, lidocaine) and a food grade ingredient (methyl anthranilate). It is shown that both the ionic liquid and binary mixtures can successfully extract At from nitric acid media, but extraction of Bi into the ionic liquid is very inefficient, resulting in a good separation factor for these two elements in the entire studied acidity range. Extraction of At into binary mixtures is very efficient, having distribution ratio values as high as 1000, while the behavior of Bi under these conditions depends on the composition of the mixture. Furthermore, a mathematical model has been developed to fit both At and Bi experimental data and applied to determine corresponding thermodynamic extraction constants.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

A Collection of More than 900 Gas Mixture Adsorption Experiments in Porous Materials from Literature Meta-Analysis

Information on mixture adsorption equilibrium is vital in developing adsorption-based separation processes. Because measuring mixture adsorption is more difficult than measuring single-component adsorption, far more data of the latter kind are available. Previous efforts to compile experimental mixture adsorption data for gases have given data sets with at most a few dozen examples. Here, we report the results of systematic literature meta-analysis that produced a data set of more than 900 gas mixture adsorption experiments. This collection includes data from 125 different binary mixtures including 60 different molecular species and information from 333 different adsorbents. We refer to this data set as the Binary adsorption ISOtherm ExperimeNtal 2020 (BISON-20) Database. Because the BISON-20 data set enormously expands the number and variety of experimental results for binary gas adsorption that are readily available, it will be a useful resource for future efforts in developing new materials or processes for gas separations. As initial applications of the BISON-20 data set, we show how identifying replicate measurements can be used to assess the reliability of binary adsorption data, how the accuracy of Ideal Adsorbed Solution Theory (IAST) can be systematically tested using experimental data, and how trends in selectivity for gas separations across many materials can be examined.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Refined Classical Force Field for Choline Chloride and Ethylene Glycol Mixtures over Wide Composition Range

In this work, a classical force field based on the General Amber Force Field (GAFF) was refined for the simulation of choline chloride (ChCl) and ethylene glycol (EG) mixtures over a wide composition range by scaling the partial charges and van der Waals parameters. The scaling factors were derived by fitting the simulation results to only eight experimental density and viscosity data points of pure EG, and ChCl/EG mixtures at 1:2, 1:4, and 1:6 molar ratios. Using the refined force field, properties essential for electrochemical applications such as density, viscosity, self-diffusion coefficient, and ionic conductivity were calculated, and excellent agreement to experimental results was found even for compositions and temperatures not used in the fitting procedure. In addition, new experimental data for density, viscosity, and ionic conductivity are reported as a function of temperature and composition for this mixture. To the best of our knowledge, this is the first classical force field developed for the study of ChCl/EG mixtures over a composition range that includes the eutectic point. Using the new model, the liquid dynamics was studied in terms of ionic conductivity. It was found that the dynamics in ChCl/EG mixtures with ChCl mole fraction higher than 20% is similar to that of ionic liquids, high temperature molten salts, and highly concentrated water-in-salt electrolytes.

25 ENERGY STORAGE↗
DOE OSTI2026

Direct Simulations of H–He Mixtures at Planetary Interior Conditions: Demixing, Insulator–Metal Transition and Miscibility Boundaries

Accurate knowledge of the electrical and thermal conductivities and structural properties of hydrogen–helium mixtures under thermodynamic conditions within and beyond the immiscibility range is very important to predict the thermal evolution and internal structure of gas giant planets like Jupiter and Saturn. Here, we propose a novel method to determine the immiscibility boundary accurately without the need for free energy calculations, while providing consistent insights into structural and transport properties of mixtures. We show with direct large-scale ab initio simulations that the insulator–metal transition (IMT) of the hydrogen subsystem is strongly affected by an admixture with a small fraction of helium and occurs at temperatures significantly higher than those of pure hydrogen. At pressures below 150 GPa, the IMT boundary is not related anymore to the H 2 subsystem dissociation, the system remains insulating even after the full dissociation of H 2 molecules and its transition to an H–He mixture. The offset of the IMT in the H–He mixture relative to the dissociation region in the hydrogen subsystem and the significant reduction of static electrical and thermal conductivity by a factor between two and a few thousand relative to pure hydrogen found in mixtures have consequences for Jupiter and Saturn’s thermal evolution, internal structure, and dynamo action, affecting a large fraction of the interior of both planets.

Helium↗
DOE OSTI2023

Validating Mixtures of 233 U, 235 U, and 239 Pu for the Sum-of-Fractions Method

The Sum-of-Fractions method is a technique used to assure that homogeneous mixtures of fissile and fissionable isotopes are below a minimum margin of k eff or reactivity. Current work by Pacific Northwest National Laboratory examines different mixtures of 233 U, 235 U, and 239 Pu to determine critical mass limits for mixtures of transuranic actinides lacking a validation basis. To provide a validation basis for these limits, the work presented here describes the results of a sensitivity and uncertainty analysis of various mixtures of these isotopes in various concentrations moderated and reflected by light water and polyethylene. The TSUNAMI-1D sequence in the SCALE code system was used to generate sensitivity coefficients for three different concentrations of mixtures of 233 U, 235 U, and 239 Pu. The TSUNAMI-IP sequence was then used for similarity assessment (c k ) with critical benchmark experiment sensitivity data files (SDFs) from the Oak Ridge National Laboratory Verified, Archived Library of Inputs and Data and the Nuclear Energy Agency SDF database. The VADER sequence in SCALE was used for statistical testing and to generate upper subcritical limits from the data to develop a basis for validating critical mass limits.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2022

Multicomponent gas mixture parametric CFD study of condensation heat transfer in small modular reactor system safety

Safety is always the primary concern for designing and analyzing nuclear reactor systems. The requirements for the safety margin for advanced small modular reactor (SMR) systems are targeted even higher than the conventional commercial large-scale nuclear reactors incorporating the passive and inherent safety systems. The SMR systems are designed with the condensation passive containment cooling system (PCCS), which plays a critical role in removing reactor heat during a steam release accident case. However, the presence of non-condensable gas (NCG), like air, reduces the heat transfer performance. This physics phenomenon becomes multifactorial for nuclear reactor containment during a fuel failure accident case that releases hydrogen gas. Besides, the mixture component of steam-air-hydrogen varies in reactor accident cases, which need simulation and validation keeping parameters of importance. Reviews showed that previous studies for SMR’s PCCCS did not cover the condensation heat transfer (CHT) in the presence of multicomponent NCG mixture parametric computational fluid dynamics (CFD) simulation and validation, making a research gap in the SMR design safety. A comprehensive CHT parametric CFD study was performed for SMR PCCS to fill this research gap. This study used experimental data as simulation 3D physics domain inlet and outlet boundary conditions. However, the wall boundary conditions were constant temperature, curve-fit, and annular coolant for verifying the turbulence models. Parametric simulations were performed, verified, and optimized for steam-NCGs mixtures. The multicomponent gases, multiphase mixtures, and fluid film condensation models were applied with associated turbulence models. The results of the parametric study were evaluated for realistic reactor conditions. Results showed that parametric study provided critical insight about the dependency of multicomponent gas mixture parameters that supports reactor safety design, analysis, and licensing.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Detonation structure in the presence of mixture stratification using reaction-resolved simulations

Many investigations of detonation-based combustors have identified reactant mixture inhomogeneity as having a leading-order impact on wave dynamics and combustion efficiency. To examine this phenomenon in a simplified context, an array of two- and three-dimensional channel detonation simulations are conducted in the present work. The reactant mixture consists of stratified fuel and air, wherein the randomly distributed equivalence ratio field features a characteristic stratification length scale. Detailed chemical kinetics are implemented in an adaptive mesh refinement solution framework where the region near the shock front is resolved with Ο (100) cells per representative ZND induction length. The results show that in comparison to baseline cases with uniform reactant mixtures, reactant stratification has a marked impact on the detonation structure. Increasing the stratification length scale increases the size and irregularity of the detonation cells, yielding larger variations in wave speed. Triple point collisions in fuel-rich regions lead to local wave speeds above the notional mean CJ speed, but wave passage through inert regions causes the local wave speed and strength to diminish. Further, conditional statistics show that increasing the stratification length scale increases the variance in pressure and temperature in the primary reaction zone, as well as the variance in heat release over a range of mixture conditions. In addition to the reactant mixture, the impact of the boundary condition behind the detonation is also investigated. The results show that an inflow boundary condition acts to over-drive the wave, leading to higher peak pressures, smaller detonation cells, and increased reactant consumption. On the other hand, cases with a wall behind the wave exhibit weaker waves with lower peak pressures and heat release rates, as well as greater variance in conditional quantities. Comparisons between complementary two- and three-dimensional simulations show reasonable qualitative agreement in wave structure, speed, and conditional statistics.

42 ENGINEERING↗
DOE OSTI2024

Integration and validation of some modules for modelling of high-speed chemically reactive flows in two-phase gas-droplet mixtures

Three modules are integrated into the built-in OpenFOAM rhoCentralFoam solver towards accurate and efficient modelling of high-speed chemically reactive flows in two-phase gas-droplet mixtures within the OpenFOAM 10.0 framework. The first module is the mixture-averaged diffusion model. The second module is the built-in OpenFOAM Lagrangian solver coupled with optimised droplet drag coefficient and convective heat transfer coefficient sub-models. The last module is a sparse stiff chemistry solver based on dynamic adaptive hybrid integration (AHI-S). The optimised droplet sub-models are first verified in correct implementation for subsequent simulations in this work. Further, they show good accuracy against experimental and analytical data in the modelling of ammonia droplet acceleration and cooling in the flowing and/or low-temperature air. The accuracy and efficiency gains related to the mixture-averaged diffusion model and the AHI-S chemistry solver are examined by simulating 1-D detonation propagation in ammonia droplet-free/laden ammoniaoxygen mixtures. Numerical results of detonation propagation speed, gaseous temperature, density, and species distributions around the induction zone show good agreement with experimental data and analytical solutions. Compared to the built-in OpenFOAM diffusion model, the mixture-averaged diffusion model provides different numerical predictions of pulsating instabilities in detonation propagation. It shows better accuracy in depicting the detonation structure within the droplet-free section attributed to improved multi-component diffusion modelling. Compared to the built-in OpenFOAM solver EulerImplicit (backward Euler), the AHI-S chemistry solver reduces the computational cost by around 50%. It achieves satisfactory accuracy in calculating detonation propagation speed within the droplet-free section with the optimal efficiency when the safety factor, β, equals 0.5.

42 ENGINEERING↗
DOE OSTI2025

Energy resolution and gain measurements in Argon-based gas mixtures: Exploring Ar:CF 4 for low energy measurements with TPCs

Time Projection Chambers (TPCs) are among the most advanced charged-particle detectors. Gas-filled TPCs have tracking capabilities that provide 3D-imaging of charged particles with a good energy resolution for spectroscopy. Different gas mixtures have different properties that determine the energy resolution as well as the spatial resolution. Therefore, optimization of operating conditions is required to simultaneously obtain adequate gain, energy resolution, spatial/track resolution, as well as higher drift velocities for high counting rates applications. Ar:CF 4 gas mixture has higher electron drift velocities and lower electron diffusion, which makes it an attractive candidate for TPC filling gas for low energy nuclear physics applications as compared to commonly used Ar:CH 4 and Ar:CO 2 gas mixtures, namely when tracking information is needed. However, other properties, including energy resolution and gain, remain largely unexplored in Ar:CF 4 especially at pressures and other operating conditions relevant for low-energy nuclear physics applications. Here, in this paper we report on gain and energy resolution measurements, using Gas Electron Multipliers (GEMs), in the less explored Ar:CF 4 mixture (Alfonsi et al., 2006), as well as in the more commonly used gas mixtures Ar:CH 4 and Ar:CO 2 . In addition to obtaining energy resolution and gain, we provide results from Garfield++ simulations for gain fluctuations, and their impact on energy resolution is discussed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Generic Behavior of Ultrastability and Anisotropic Molecular Packing in Codeposited Organic Semiconductor Glass Mixtures

Vapor-deposited glass mixtures of organic semiconductors commonly serve as active layers in organic electronic devices, whose lifetime and performance are strongly influenced by the stability and structure of these mixed glasses. Here, we study the stability and anisotropic molecular packing of six co-deposited organic semiconductor glass mixtures with 50:50 weight ratio, by differential scanning calorimetry and spectroscopic ellipsometry. We also find that all six binary systems exhibit high kinetic stability and significantly reduced enthalpy relative to the corresponding liquid-cooled glassy mixtures (ultrastable behavior), even for systems where the glass transition temperatures of the components differ by more than 90 K. Furthermore, we demonstrate that the birefringence of a co-deposited glass mixture, a measure of its anisotropic packing, can be predicted from the birefringence of glasses of the two pure components. These results for stability and structure are expected to be applicable to other co-deposited organic semiconductor glass mixtures, so long as the two components mix well in the glass and individually can form ultrastable glasses. Therefore, our findings are significant for designing novel electronic devices with enhanced device lifetime and increased operational efficiency.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Data-driven predictions of complex organic mixture permeation in polymer membranes

Membrane-based organic solvent separations are rapidly emerging as a promising class of technologies for enhancing the energy efficiency of existing separation and purification systems. Polymeric membranes have shown promise in the fractionation or splitting of complex mixtures of organic molecules such as crude oil. Determining the separation performance of a polymer membrane when challenged with a complex mixture has thus far occurred in an ad hoc manner, and methods to predict the performance based on mixture composition and polymer chemistry are unavailable. Here, we combine physics-informed machine learning algorithms (ML) and mass transport simulations to create an integrated predictive model for the separation of complex mixtures containing up to 400 components via any arbitrary linear polymer membrane. We experimentally demonstrate the effectiveness of the model by predicting the separation of two crude oils within 6-7% of the measurements. Integration of ML predictors of diffusion and sorption properties of molecules with transport simulators enables for the rapid screening of polymer membranes prior to physical experimentation for the separation of complex liquid mixtures.

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

AL4GAP: Active learning workflow for generating DFT-SCAN accurate machine-learning potentials for combinatorial molten salt mixtures

Machine learning interatomic potentials have emerged as a powerful tool for bypassing the spatiotemporal limitations of ab initio simulations, but major challenges remain in their efficient parameterization. We present AL4GAP, an ensemble active learning software workflow for generating multicomposition Gaussian approximation potentials (GAP) for arbitrary molten salt mixtures. The workflow capabilities include: (1) setting up user-defined combinatorial chemical spaces of charge neutral mixtures of arbitrary molten mixtures spanning 11 cations (Li, Na, K, Rb, Cs, Mg, Ca, Sr, Ba and two heavy species, Nd, and Th) and 4 anions (F, Cl, Br, and I), (2) configurational sampling using low-cost empirical parameterizations, (3) active learning for down-selecting configurational samples for single point density functional theory calculations at the level of Strongly Constrained and Appropriately Normed (SCAN) exchange-correlation functional, and (4) Bayesian optimization for hyperparameter tuning of two-body and many-body GAP models. Here, we apply the AL4GAP workflow to showcase high throughput generation of five independent GAP models for multicomposition binary-mixture melts, each of increasing complexity with respect to charge valency and electronic structure, namely: LiCl–KCl, NaCl–CaCl 2 , KCl–NdCl 3 , CaCl 2 –NdCl 3 , and KCl–ThCl 4 . Our results indicate that GAP models can accurately predict structure for diverse molten salt mixture with density functional theory (DFT)-SCAN accuracy, capturing the intermediate range ordering characteristic of the multivalent cationic melts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Effect of particle shape on stratification in drying films of binary colloidal mixtures

The role of particle shape in evaporation-induced auto-stratification in polydisperse colloidal suspensions is explored with molecular dynamics simulations of mixtures of spheres and aspherical particles. A unified framework based on the competition between diffusion and diffusiophoresis is proposed to understand the effects of shape and size dispersity. In general, particles diffusing more slowly (e.g., larger particles) tend to accumulate more strongly at the evaporation front. However, larger particles have larger surface areas and therefore greater diffusiophoretic mobility. Hence, they are more likely to be driven away from the evaporation front via diffusiophoresis. For a rapidly dried bidisperse suspension containing small and large spheres, the competition leads to “small-on-top” stratification. Here, we employ a computational model in which the diffusion coefficient is inversely proportional to particle mass. For a mixture of spheres and aspherical particles with similar mass, the diffusion contrast is reduced, and the spheres are always enriched at the evaporation front as they have the smallest surface area for a given mass and, therefore, the lowest diffusiophoretic mobility. Furthermore, for a mixture of solid and hollow spheres that have the same outer radius and thus the same surface area, the diffusiophoretic contrast is suppressed, and the system is dominated by diffusion. Consequently, the solid spheres, which have a larger mass and diffuse more slowly, accumulate on top of the hollow spheres. Finally, for a mixture of thin disks and long rods that differ significantly in shape but have similar mass and surface area, both diffusion and diffusiophoresis contrasts are suppressed, and the mixture does not stratify.

Classical molecular dynamic simulations↗
DOE OSTI2024

A mixture of grass–legume cover crop species may ameliorate water stress in a changing climate

Climate change models predict increasing precipitation variability in the mid-latitude regions of Earth, generating a need to reduce the negative impacts of these changes on crop production. Despite considerable research on how cover crops support agriculture in a changing climate, understanding is limited of how climate change influences the growth of cover crops. We investigated the early development of two common cover crop species—crimson clover (Trifolium incarnatum) and rye (Secale cereale)—and hypothesized that growing them in the mixture would ameliorate stress from drought or waterlogging. This hypothesis was tested in a 25-day greenhouse experiment, where the two factors (species number and water stress) were fully crossed in randomized blocks, and plant responses were quantified through survival, growth rate, biomass production and root morphology. Water stress negatively influenced the early growth of these two species in contrasting ways: crimson clover was susceptible to drought while rye performed poorly under waterlogging. Per-plant biomass in rye was always greater in mixture than in monoculture, while per-plant biomass of crimson clover was greater in mixture under drought. Both species grew longer roots in mixture than in monoculture under drought, and total biomass of mixtures did not differ significantly from the more-productive monoculture (rye) in any water condition. In the face of increasingly variable precipitation, growing crimson clover and rye together has potential to ameliorate water stress, a possibility that should be further investigated in field experiments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Extreme compression of planetary gases: High-accuracy pressure-density measurements of hydrogen-helium mixtures above fourfold compression

Hydrogen (H 2 ) and helium (He), the most abundant elements in the universe, pose a unique challenge in measuring the equation of state of the mixture, owing to their differing physical properties. There remains a need for data with high enough precision to discriminate between existing equation of state (EOS) mix models in order to understand the internal structure of gas-giant planets. Here, we have measured the EOS of precompressed H 2 - He mixtures at conditions directly relevant to the planetary interiors using hypervelocity gas guns and Sandia’s Z machine with less than 10% uncertainty in density, enabling validation of mixture models. We precompressed 50:50 molar mixtures of H 2 -He to 0.1–0.2 GPa and directly measured particle velocity (in gas-gun experiments) and shock velocities (in Z-machine experiments). To complement the experimental efforts, we also computed the Hugoniots of precompressed H 2 -He mixtures using density-functional-theory-based molecular dynamics. Furthermore, we observe approximately 3- to 4.3-fold density compression at pressures up to 44 GPa.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Learning Molecular Mixture Property Using Chemistry-Aware Graph Neural Network

Recent advances in machine learning (ML) are expediting materials discovery and design. One significant challenge facing ML for materials is the expansive combinatorial space of potential materials formed by diverse constituents and their flexible configurations. This complexity is particularly evident in molecular mixtures, a frequently explored space for materials, such as battery electrolytes. Owing to the complex structures of molecules and the sequence-independent nature of mixtures, conventional ML methods have difficulties in modeling such systems. Here, we present MolSets, a specialized ML model for molecular mixtures, to overcome the difficulties. Representing individual molecules as graphs and their mixture as a set, MolSets leverages a graph neural network and the deep sets architecture to extract information at the molecular level and aggregate it at the mixture level, thus addressing local complexity while retaining global flexibility. We demonstrate the efficacy of MolSets in predicting the conductivity of lithium battery electrolytes and highlight its benefits in the virtual screening of the combinatorial chemical space. Published by the American Physical Society 2024

Zhang, Hengrui (ORCID:0000000231831654)↗
DOE OSTI2023

Development of Binder Jet Compatible Silicon Carbide Mixtures for Increased Sintered Density

Binder jet additive manufacturing can process a variety of ceramic powder feedstocks to manufacture near net shape green parts that are subsequently sintered to achieve densification. In particular, silicon carbide (SiC) is a promising ceramic material for heat exchangers in energy storage applications and binder jet is a well-suited process for manufacturing these heat exchangers due to the often geometrically complex designs. However, the need for good powder flowability in binder jet printing often limits the feedstock powder to be larger particles rather than that of ultra fine submicron powders used in conventional ceramic processing, which are more readily densified during sintering. The present work explores the use of various powder mixtures and sintering settings to identify strategies for achieving high density sintered parts in binder jet compatible systems without the use of infiltration. Liquid-phase sintering additives and bimodal powder mixtures are explored alongside different sintering temperatures, rates, atmospheres, and hold times. Two mixtures were found to be good candidates for further exploration: 1) 90 wt% 0.6 µm SiC + 6.25 wt% Al 2 O 3 + 3.75 wt% Y 2 O 3 mixture achieved the highest sintered relative density of 91.2%; and 2) a bimodal mixture comprised of 45 wt% 10 µm SiC + 45 wt% 0.6 µm SiC + 6.25 wt% Al 2 O 3 + 3.75 wt% Y 2 O 3 had the best powder flowability of the powders measured and a 75.1% sintered relative density.

25 ENERGY STORAGE↗