SEARCH · Search NASA
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Relationship between thermophysical properties and phonon mean free path. Heat capacity, viscosity, and thermal conductivity in ionic liquids decrease as mean free path decreases and dynamics become less “solid-like” and collect motion diminishes.
The prospect of using artificial intelligence (AI) to accurately screen very large databases of compounds for multiple properties has yet to be realized. Here, we explore this possibility using ionic liquids (ILs) which offer unique physicochemical properties and excellent tunability, making them highly versatile solvents for various research applications. Screening millions of potential ILs for the best perfomance for use in specific tasks with experimental methods alone however, is impractical. Further, traditional’ physics-based computational chemistry is hindered by high computational cost. To address this challenge, we leverage a natural language processing (NLP)-based molecular embedding technique with advanced machine learning (ML) models to predict seven key IL properties: viscosity, density, ionic conductivity, surface tension, melting temperature, toxicity, and water solubility. Comprehensive datasets for these properties are obtained, then NLP featurization with Mol2vec is compared with other featurization techniques such as 2D Morgan fingerprints, and 3D quantum chemistry-derived sigma profiles. NLP-based featurization exhibited the best predictive performance, achieving the highest R 2 and lowest RMSE values for all the studied IL properties. Further, we present case studies of how ILs might be screened using combined property criteria for practical cases – lignocellulosic biomass processing, CO 2 capture, and optimal electrolytes for batteries – screening a novel database of ∼10.6 million generated feasible ILs. The results introduce NLP as a powerful tool for engineering many designer solvents with desirable properties for task specific applications.
Highly concentrated electrolytes (HCEs) based on LiPF 6 in propylene carbonate (PC) have been examined as lithium-ion battery electrolytes. These HCEs have lower ionic conductivities and higher viscosities than ethylene carbonate (EC) electrolytes with 1.2 M LiPF 6 , but they have higher Li + ion transference numbers. Electrochemical cycling behaviour of LiNi 0.8 Co 0.015 Al 0.05 O 2 //graphite cells with 3.2 M LiPF 6 in PC resembles that of cells with EC-based electrolytes; the HCE cells have higher impedance, which can be lowered by increasing test temperature. By employing Raman and infrared spectroscopy, combined with density functional theory and ab initio molecular dynamics simulations, we reveal that the Li + solvation structure and speciation are key factors that determine cell performance. Two distinct regimes are observed as a function of salt concentration—in the conventional regime, the solvation number (SN) is mostly constant, while in the HCE regime it decreases linearly. Graphite exfoliation is suppressed only at very high salt concentrations ( > 2.4 M), where [ PC ] free /[Li + ] < 1 and P F 6 − f r e e > P C f r e e . Results from the Advanced Electrolyte Model indicate that Li + desolvation improves at higher LiPF 6 concentrations, thereby mitigating PC co-intercalation into the graphite. However, Li + ion transport is hindered in the HCEs, which increases impedance at both the oxide-positive and graphite-negative electrodes.
Thermal conductivity, specific heat, viscosity, and density are critical properties for understanding thermal hydraulics of heat transfer fluids (HTFs) like molten salts. These properties are important for design and safe operation of any HTF loop. These characterizations are required to benchmark computational chemical models that are used to predict properties, but existent data are limited. These measurements are extremely difficult to make due to the operating temperature range and corrosive properties of the salt.Density measurements are needed to predict volumetric behavior. Thermal expansion mismatches can cause mechanical failure of structural materials and important components that are in contact with the molten salt. All of these properties are related to heat transfer in HTFs. The heat generated in a reactor must be high enough to derive power; however it should not be so hot that it cannot be safely removed via the HTF at an appropriate rate. It is important to understand the thermal hydraulics required to operate at the desired power generation level and to avoid situations in which thermal shifts can damage components,cause failure of structural materials, and/or lead to accidents. Understanding these thermal properties can aid in component and process design and can contribute to optimized operational and safety parameters.
This project demonstrated the technical feasibility of gasifying blends of coal, biomass and mixed waste plastics in a high pressure entrained-flow gasifier for production of hydrogen. This was achieved by creating slurries of coal, biomass pyrolysis liquids and liquefied plastic oil. Specific objectives of the project included (1) determining compositions of coal-biomass-plastic mixtures that produce a stable slurry suitable for pumping to high pressure, (2) designing and testing a novel burner to effectively atomize the mixed feedstock slurry in a pressurized gasifier, and (3) acquiring first-of-a-kind performance data for pressurized, oxygen-blown entrained-flow gasification of slurried blends of coal, biomass and plastic. A key focus of the project was preparation of mixtures of coal, biomass and plastic. To facilitate easy pumping to high pressure, slurries were created using (i) bioliquid resulting from a well-established rapid thermal treatment pyrolysis process, (ii) oil produced from waste plastic via an available thermal conversion process, and (iii) ground coal. Lab-scale tests were conducted to evaluate viscosity and stability/propensity for the mixtures to separate over time. Larger batches of the most promising slurry compositions were prepared and subjected to pumping studies. It was found that mixtures containing less than about 40% coal and less than about 30% plastic oil were stable and of low enough viscosity to facilitate pumping to high pressure. A high-performance hot oxygen burner (HOB) was designed and fabricated for use in an existing 1 ton/day pressurized entrained-flow gasifier. Ex-situ testing demonstrated that the HOB provides excellent atomization of the slurries and creates a very uniform and robust flame. Performance of the HOB in the gasifier was exceptional and resulted in better conversion to syngas than any previous generation of burner. Fabricating the HOB for this project was challenging due to the relatively small scale of the gasifier. Performance in larger-scale systems is expected to be even better. Gasification of mixtures of coal, bioliquid and plastic oil proved to be relatively easy. The gasifier was able to be started up, pressurized and transitioned to the blended slurry feedstock with no technical difficulty. Syngas rich in CO and H 2 , with relatively low concentrations of CO 2 , was produced. During startup and at low gasification temperatures, soot formation was observed, as is typical for gasification of petroleum-based feedstocks. No slag buildup was observed under the conditions tested. The project was successful in demonstrating the technical feasibility of the concept. Future development should broaden the range of feedstocks under consideration, more deeply explore performance of the gasifier, and evaluate scale-up and implementation strategies.
Predicting properties such as heating value, ash fusion temperature, and mineral ash composition from Laser-Induced Breakdown Spectroscopy (LIBS) data can make gasifiers more flexible to different feedstocks. Understanding these feedstock properties in-situ improves feedstock conversion modelling methods that allow for consistent operation, higher carbon conversion, and reduced fouling and erosion rates. The purpose of this study is to demonstrate methods for model creation that take LIBS data as predictor features and estimate higher order material properties as a function of feedstock material properties. Six samples were chosen to represent a mixture of abundant and carbon rich waste materials. LIBS measurements were performed on these samples for elemental wavelengths and intensity values. Laboratory analytical results were obtained for each sample’s heating value, proximate and ultimate analysis, mineral ash composition, ash fusion temperatures, and viscosity temperatures. Thermal conductivity was measured using a HotDisk TPS 2500S. LIBS measurements were processed and used as predictor features for machine learning (ML) models to predict the sample’s material properties. Predictor feature selection algorithms, particularly minimum redundancy maximum relevance (mRMR), reduced the dimensionality of ML models. Many modelling methods such as Gaussian process regression (GPR), regression tree, neural networks (NN), and support vector machines (SVM) were demonstrated to be effective at predicting higher order properties; however, mRMR with GPR stood out as a clear winning combination.
Nonaqueous redox flow batteries use liquid electrolytes containing redox-active organic molecules (redoxmers) as their energy storage medium. To maximize energy density, the redoxmer concentration needs to be maximized while maintaining low viscosity and high ionic conductivity. During charge, a redoxmer molecule pairs with an ion in the electrolyte while another ion migrates across the membrane to maintain electric neutrality. In a crowded electrolyte, this reconstitution changes physical and chemical properties of the solution. To explore these behaviors, a phenothiazine redoxmer fully miscible with acetonitrile was used, and electrochemical charge was mimicked by chemical oxidation. The solutions were examined using small-angle X-ray scattering, nuclear magnetic resonance, and conductometry and modeled using classical molecular dynamics. Overall, our study indicates that physical and structural properties of redoxmer solutions in both states of charge make it exceedingly difficult to increase the redoxmer concentrations over 2 M at any temperature without compromising dynamic properties of such solutions. The cause for this limitation is proximity to a gel-like regime in which fluidity, diffusivity, and ionic conductivity exponentially decrease with increasing concentration. This tendency is compounded by non-Arrhenius behavior of the electrolyte: a small increase in the concentration outruns gains in fluidity and conductivity at a higher temperature. Thus the properties of crowded electrolytes generally make it impossible to operate when gel-like behavior sets in. Pushing the redoxmer concentration to 2.5-3 M might be possible for small redoxmer molecules, but it would require the use of ionic liquid electrolytes at 340-360 K. (C) 2021 Elsevier B.V. All rights reserved.
During the expansion of a heavy ion collision, the system passes close to the $\textit{O}$(4) critical point of QCD, and thus the fluctuations of the order parameter $(σ, \overrightarrow{π}$) are expected to be enhanced. In this work, our goal is to compute how these enhanced fluctuations modify the transport coefficients of QCD near the pseudocritical point. We also make a phenomenological estimate for how chiral fluctuations could effect the momentum spectrum of soft pions. We first formulate the appropriate stochastic hydrodynamic equations close to the $\textit{O}$(4) critical point. Then, working in mean field, we determine the correlation functions of the stress tensor and the currents which result from this stochastic real-time theory, and use these correlation functions to determine the scaling behavior of the transport coefficients. The hydrodynamic theory also describes the propagation of pion waves, fixing the scaling behavior of the dispersion curve of soft pions. We present scaling functions for the shear viscosity and the charge conductivities near the pseudocritical point and estimate the absolute magnitude of the critical fluctuations to these parameters and the bulk viscosity. Using the calculated pion dispersion curve, we estimate the expected critical enhancement of soft pion yields, and this estimate provides a plausible explanation for the excess seen in experiment relative to ordinary hydrodynamic computations. Our results motivate further phenomenological and numerical work on the implications of chiral symmetry on real-time properties of thermal QCD near the pseudocritical point.
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.
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.
Based on a 14-moment closure for nonresistive (general-) relativistic viscous plasmas, here we describe a new numerical scheme that is able to handle all first-order dissipative effects (heat conduction, bulk and shear viscosities), as well the anisotropies induced by the presence of magnetic fields. The latter is parametrized in terms of a thermal gyrofrequency or, equivalently, a thermal Larmor radius and allows to correctly capture the thermal Hall effect. By solving an extended Israel-Stewart-like system for the dissipative quantities that enforces algebraic constraints via stiff-relaxation, we are able to cast all first-order dissipative terms in flux-divergence form. This allows us to apply traditional high-resolution shock capturing methods to the equations, making the system suitable for the numerical study of highly turbulent flows. We present several numerical tests to assess the robustness of our numerical scheme in flat spacetime. The 14-moment closure can seamlessly interpolate between the highly collisional limit found in neutron star mergers, and the highly anisotropic limit of relativistic Braginskii magnetohydrodynamics appropriate for weakly collisional plasmas in black-hole accretion problems. We believe that this new formulation and numerical scheme will be useful for a broad class of relativistic magnetized flows.
Water is widely recognized as critical to the tunability and electrochemical stability of nonaqueous solvents such as deep eutectic solvents (DESs) and ionic liquids (ILs). Traditionally, the water content of these solvents has been controlled by either drying or adding small amounts of water to control their bulk properties to meet specific application requirements. The total water content by itself, does not provide sufficient information about the chemical reactivity and molecular interactions within DES– and IL–water mixtures. In this concept article, water activity is highlighted as a thermodynamically more rigorous descriptor to quantify the influence of the co‐solvent water on DES– and IL–water mixtures. Water activity relates measurable physical properties, such as vapor pressure, density, viscosity, electrochemical stability, and conductivity of DESs and ILs to the underlying molecular interactions between their components. Furthermore, water activity of DESs and ILs correlates with changes in local solvent structures and thermodynamic excess properties, including excess molar volume, enthalpy, and Gibbs energy.
Redoxmers are organic active molecules storing electrochemical energy in nonaqueous redox flow batteries (NRFBs). Increasing the solubility of redoxmers is an important approach for increasing energy density of NRFBs as effective redoxmer concentration determines how much electricity can be stored in a given volume. Molecular engineering redoxmers towards liquid forms is regarded as one promising strategy as liquid redoxmers represent an extreme scenario where fluidity is maintained at maximum concentration using a minimum amount of supporting solvents. In our Perspective, recent examples of liquid redoxmers as well as their development strategy will be discussed.
Explore the source record for details and available documents.
Concentrated electrolytes are attracting significant attention because the solvation structures could stabilize the interface, encouraging novel electrolyte development for high-voltage and long-cycle-life batteries. Saturated electrolytes, which have the highest salt concentrations, have been rarely studied because of their shortcomings of high viscosity and low ionic conductivity. Nevertheless, the exciting solvation structure in saturated solution is still worth studying, significantly broadening the comprehensive understanding of the solvation processes. In this work, we investigate the saturated lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) dissolved in seven different organic solvents, including propylene carbonate (PC), tetrahydrofuran (THF), acetonitrile (ACN), dimethylformamide (DMF), 1,2-dimethoxyethane (DME), diethylene glycol dimethyl ether (Diglyme), and tetraethylene glycol dimethyl ether (Tetraglyme). The combined small/wide-angle X-ray scattering and Raman spectroscopy are employed to study the global and local solvation structure. Here, this work demonstrates a method for detecting the structure of liquids, which will facilitate the study of structure–performance relationships and the screening of new electrolytes.
Room-temperature ionic liquids (RTILs) are synthetic electrolytes that have a large electrochemical stability window, making them attractive candidates for electric double-layer capacitor (EDLC) applications. Due to their high viscosities and low ionic conductivities, RTILs are often diluted with organic solvent for practical use. Here, we study the effects of dilution on the performance of RTIL EDLCs using a simple mean-field model. We find that dilution diminishes the unfavorable hysteresis that results from a spontaneous surface charge separation (SSCS). As a result, the RTIL concentration can be used to modulate the proximity to the SSCS transition, and maximize capacitance. The interplay between the concentration and the correlation strength gives rise to complex zero-potential phase behavior, including a tricritical point and a λ-line, very similar to the Blume–Capel dilute Ising model. Additionally, electrodes that are solvophilic aid in the prevention of SSCS by drawing solvent molecules to the electrode and displacing ions. Solvophilic electrodes give rise to a phase transition at finite potential where the surface charge rapidly increases with a small increase in potential, leading to a substantial increase in capacitance and energy storage.
We present a comprehensive study of transport coefficients including DC electrical conductivity and related optical properties, electrical contribution to the thermal conductivity, and the shear viscosity via ab initio molecular dynamics and density functional theory calculations on the “priority 1” cases from the “Second Charged-Particle Transport Coefficient Workshop” [Stanek et al., Phys. Plasmas (to be published 2024)]. The purpose of this work is to carefully document the entire workflow used to generate our reported transport coefficients, up to and including our definitions of finite size and statistical convergence, extrapolation techniques, and choice of thermodynamic ensembles. In pursuit of accurate optical properties, we also present a novel, simple, and highly accurate algorithm for evaluating the Kramers–Kronig relations. These heuristics are often not discussed in the literature, and it is hoped that this work will facilitate the reproducibility of our data.
Abstract The physical chemistry of iron at the inner-core conditions is key to understanding the evolution and habitability of Earth and super-Earth planets. Based on full first-principles simulations, we report cooperative diffusion along the longitudinally fast ⟨ 111 ⟩ directions of body-centered cubic (bcc) iron in temperature ranges of up to 2000–4000 K below melting and pressures of ∼300–4000 GPa. The diffusion is due to the low energy barrier in the corresponding direction and is accompanied by mechanical and dynamical stability, as well as strong elastic anisotropy of bcc iron. These findings provide a possible explanation for seismological signatures of the Earth’s inner core, particularly the positive correlation between P wave velocity and attenuation. The diffusion can also change the detailed mechanism of core convection by increasing the diffusivity and electrical conductivity and lowering the viscosity. The results need to be considered in future geophysical and planetary models and should motivate future studies of materials under extreme conditions.