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.
We introduce HANNA, the first hybrid neural network model that strictly complies with all thermodynamic consistency criteria for predicting activity coefficients and outperforms current benchmark methods in terms of accuracy and applicability.
Predicting the functional properties of many molecular systems relies on understanding how atomistic interactions give rise to macroscale observables. However, current attempts to develop predictive models for the structural and thermodynamic properties of condensed-phase systems often rely on extensive parameter fitting to empirically selected functional forms whose effectiveness is limited to a narrow range of physical conditions. Here, we illustrate how these traditional fitting paradigms can be superseded using machine learning. Specifically, we use the results of molecular dynamics simulations to train machine learning protocols that are able to produce the radial distribution function, pressure, and internal energy of a Lennard-Jones fluid with increased accuracy in comparison to previous theoretical methods. The radial distribution function is determined using a variant of the segmented linear regression with the multivariate function decomposition approach developed by Craven et al. [J. Phys. Chem. Lett. 11, 4372 (2020)]. The pressure and internal energy are determined using expressions containing the learned radial distribution function and also a kernel ridge regression process that is trained directly on thermodynamic properties measured in simulation. The presented results suggest that the structural and thermodynamic properties of fluids may be determined more accurately through machine learning than through human-guided functional forms.
Theories of strongly stretched polymer brushes, particularly the parabolic brush theory, are valuable for providing analytically tractable predictions for the thermodynamic behavior of surface-grafted polymers in a wide range of settings. Furthermore, the parabolic brush limit fails to describe polymers grafted to convex curved substrates, such as the surfaces of spherical nanoparticles or the interfaces of strongly segregated block copolymers. It has previously been shown that strongly stretched curved brushes require a boundary layer devoid of free chain ends, requiring modifications of the theoretical analysis. While this “end-exclusion zone” has been successfully incorporated into the descriptions of brushes grafted onto the outer surfaces of cylinders and spheres, the behavior of brushes on surfaces of arbitrary curvature has not yet been studied. We present a formulation of the strong-stretching theory for molten brushes on the surfaces of arbitrary curvature and identify four distinct regimes of interest for which brushes are predicted to possess end-exclusion zones, notably including regimes of positive mean curvature but negative Gaussian curvature. Through numerical solutions of the strong-stretching brush equations, we report predicted scaling of the size of the end-exclusion zone, the chain end distribution, the chain polarization, and the free energy of stretching with mean and Gaussian surface curvatures. Through these results, we present a comprehensive picture of how the brush geometry influences the end-exclusion zones and exact strong-stretching free energies, which can be applied, for example, to model the full spectrum of brush geometries encountered in block copolymer melt assembly.
This study proposes a graded structure via additive manufacturing for divertor and first wall blanket applications in fusion reactors. Materials were selected based on thermodynamic calculations to operate from 1100 °C at the plasma-facing level to 550 °C at the structural steel level. Conventional joining methods often lead to failures due to discrete reaction layers with significant mechanical property differences. Using laser beam-directed energy deposition (LB-DED), this study demonstrates the fabrication of a VCrTi-Gr91 steel functionally graded component through a novel process parameter optimization framework. A systematic approach included powder characterization, single-track depositions, and construction of printability maps. Near full-density specimens of each interlayer were additively manufactured, and a transition from V-based alloys to reduced activation ferritic martensitic steels was achieved. Computational material selection of interlayer alloys and thermodynamic/diffusion kinetics simulations prevented most interface incompatibilities. A brittle intermetallic formed at one interface, causing cracking, which was not predicted by current thermodynamic models. Transition alloy design approach was updated with a more recent database and a mitigation strategy has been proposed to eliminate the formation of deleterious intermetallic phases. Ultimately, LB-DED has proven effective for producing multi-material graded systems for fusion applications, with the demonstrated process parameter optimization framework applicable to various materials.
Conspectus Given the universal importance of electrolyte solutions, it is natural to expect that we have a nearly complete understanding of the fundamental properties of these solutions (e.g., the chemical potential) and that we can therefore explain, predict, and control the phenomena occurring in them. In fact, reality falls short of these expectations. But, recent advances in the simulation and modeling of electrolyte solutions indicate that it should soon be possible to make progress toward these goals. In this Account, we will discuss the use of first-principles interaction potentials based in quantum mechanics (QM) to enhance our understanding of electrolyte solutions. Specifically, we will focus on the use of quantum density functional theory (DFT) combined with molecular dynamics simulation (DFT-MD) as the foundation for our approach. The overarching concept is to understand and accurately reproduce the balance between local or short-ranged (SR) structural details and long-range (LR) correlations, allowing the prediction of the thermodynamics of both single ions in solution as well as the collective interactions characterized by activity/osmotic coefficients. In doing so, relevant collective motions and driving forces characterized by chemical potentials can be determined. Here, in this Account, we will make the case that understanding electrolyte solutions requires a faithful QM representation of the SR nature of the ion–ion, ion–water, and water–water interactions. However, the number of molecules that is required for collective behavior makes the direct application of high-level QM methods that contain the best SR physics untenable, making methods that balance accuracy and efficiency a practical goal. Alternatives such as continuum solvent models (CSMs) and empirically based classical molecular dynamics have been extensively employed to resolve this problem but without yet overcoming the fundamental issue of SR accuracy. We will demonstrate that accurately describing the SR interaction is imperative for predicting both intrinsic properties, namely, at infinite dilution, and collective properties of electrolyte solutions. DFT has played an important role in our understanding of condensed phase systems, e.g., bulk liquid water, the air–water interface, ions in bulk, and at the air–water interface. This approach holds huge promise to provide benchmark calculations of electrolyte solution properties that will allow for the development and improvement of more efficient methods, as well as an enhanced understanding of fundamental phenomena. However, the standard protocol using the generalized gradient approximation with van der Waals (vdW) correction requires improvement in order to achieve a high level of quantitative accuracy. Simply simulating with higher level DFT functionals may not be the best route considering the significant computational cost. Alternative methods of incorporating information from higher levels of QM should be explored; e.g., using force matching techniques on small clusters, where high level benchmark calculations are possible, to develop ideal correction terms to the DFT functional is a promising possibility. We argue that DFT with statistical mechanics is becoming an increasingly useful framework enabling the prediction of collective electrolyte properties.
The characterization of kerogen nanopores is crucial for predicting the geostorage capacity and recoverability of natural gas in unconventional gas shale reservoirs. Towards this end, a powerful technique is presented which integrates 2D NMR T 1 -T 2 relaxation measurements with molecular dynamics (MD) simulations of hydrocarbons confined in the nanopores of kerogen. The integrated NMR-MD technique is demonstrated using T 1 -T 2 measurements of kerogen isolates and organic-rich chalks saturated with heptane, together with MD simulations of heptane completely dissolved in a realistic kerogen model. The NMR-MD results are used to extract the swelling ratio and nanopore size distribution of kerogen as a function of depth in the reservoir. The effects of organic nanoconfinement on the T 1 relaxation dispersion and T 2 residual dipolar coupling of heptane are investigated, as well as the effect of downhole effective stress on the kerogen nanopore size as a function of depth and compaction. Potential applications in partially depleted gas shale reservoirs are discussed, including CO 2 utilization/geostorage, geostorage of green H 2 , and integration of the NMR-MD technique with thermodynamic models for predicting the competitive sorption of gas mixtures in kerogen.
The thermal history developed in laser powder bed fusion (LPBF) processes has been shown to be complex resulting in equally complex microstructures and mechanical properties. Microscopic observations and Vickers micro-hardness mapping measurements were carried out on diff ;erent section planes of LPBF alloy 718 cuboids. Three-dimensional finite element analysis was used to simulate thermal history and to predict the residual stress distribution in the as-built material. Computational thermodynamics was used to predict the micro-segregation and nucleation driving force of various phases in the bulk and in segregated regions. Varied heat-treatments such as simulated hot isostatic pressing, and double aging were applied. Their influence on the microstructure, microsegregation, precipitate formation, and micro-hardness variations of LPBF alloy 718 were investigated. Hardness map results showed heterogeneous micro-hardness on the xy- and xz-planes of the as-built parts where the bottom plane and center regions had larger hardness of ~315 HV 0.5 while the top plane and contours showed hardness of ~300 HV 0.5 . It was found that the aging treatment increased the overall hardness of the as-built condition from ~310 HV 0.5 to 470 HV 0.5 but also increased the hardness gradient throughout the coupon. After simulated hot isostatic pressing process (i.e., without applied pressure) at 1020 °C for 4 h followed by water quench (HIPWQ), the hardness gradient and hardness was minimized (~210 HV 0.5 ) as the microstructure transitioned from heterogeneous columnar grains in the as-built condition to more uniform recrystallized grains. A double aging treatment was applied to enhance hardness from ~210 HV 0.5 to ~440 HV 0.5 . Finally, HIPWQ followed by double aging produced a homogeneous microstructure and more uniform hardness map with enhanced mechanical properties in LPBF alloy 718 coupons.
The corrosion behavior of 316 L stainless steel (SS) was studied via the natural circulation of molten eutectic NaCl-MgCl 2 salt through a microloop. The post-corrosion tested 316 L SS microloop sections were characterized with microscopy techniques to determine the microstructural and microchemical changes that occurred at the alloy/salt interface. It was found that 316 L SS showed heterogeneous dissolution at the hot-leg, whereas deposition of corrosion products occurred at the cold-leg. For the first time, experimentally obtained molten salt flow-induced corrosion of 316 L SS results were combined with computational thermodynamic-kinetic models to validate the dissolution and deposition in terms of elemental compositional changes at the alloy/salt interface. The thermodynamic-kinetic modeling predicted that the heterogeneous dissolution of Cr and Fe from the hot-leg section of 316 L SS persisted throughout the salt circulation. The model also estimated that, despite Cr deposition starting earlier than Fe, the total redeposition of Fe is expected to be significantly greater than that of Cr over the circulation of salt. Furthermore, the modeling accurately predicted the subsurface enrichment of Mo which is attributed to the reduced Cr activity and the relatively higher diffusion rate of Mo within the alloy matrix. Here, the agreement between modeling and experimental results confirms that Fe chlorides dissolve at the hot-leg and subsequently deposit at the cold-leg due to activity changes driven by the thermal gradient. By contrast, Cr was not detected in the cold-leg deposits, which is attributed to its weaker temperature dependence on activity, limiting its redeposition under these conditions.
High-throughput computational studies of lanthanide and actinide chemistry with density-functional theory are complicated by the need for Hubbard U corrections, which ensure localization of the f-electrons, but can lead to metastable states. This work presents a systematic investigation of the effects of both Hubbard U value and metastable states on the predicted structural and thermodynamic properties of four uranium compounds central to the field of nuclear fuels: UC, UN, UO 2 , and UCl 3 . We also assess the impact of the exchange-hole dipole moment (XDM) dispersion correction on the computed properties. Overall, the choice of Hubbard U value and inclusion of a dispersion correction cause larger variations in the computed geometric properties than result from metastable states. Further, the weak dependence of structure optimization on metastable states should simplify future high-throughput calculations on actinides. Conversely, addition of the dispersion correction is found to offset the repulsion introduced by the Hubbard U term and provides greatly improved agreement with experiment for both cell volumes and heats of formation. The XDM dispersion correction is largely invariant to the chosen U value, making it a robust dispersion correction for actinide systems.
Provided is a comprehensive description of a band gap thermodynamic model, which predicts and explains key features of photosegregation in lead-based, mixed-halide perovskites. The model gives a prescription for illustrating halide migration driven by photocarrier energies. Where possible, model predictions are compared with experimental results. Free energy derivations are provided for three assumptions: (1) halide mixing in the dark, (2) a fixed number of photogenerated carriers funneling to and localizing in low band gap inclusions of the alloy, and (3) the statistical occupancy of said inclusions from a bath of thermalized photocarriers in the parent material. Model predictions include excitation intensity (I exc )-dependent terminal halide stoichiometries (x terminal ), temperature-independent x terminal , excitation intensity thresholds (I exc,threshold ) below which photosegregation is suppressed, and reduced segregation in nanocrystals as compared to thin films. Here, the model also offers insight into kinetically manipulating photosegregation rates via control of underlying mediators and rationalizes asymmetries in forward and reverse photosegregation rate constants/activation energies. What emerges is a cohesive framework for understanding ubiquitous photosegregation in mixed-halide perovskites and a rational basis by which to manage the phenomenon.
Complex borohydrides such as Mg(BH 4 ) 2 offer one of highest capacities to chemically store hydrogen for onboard applications; however, it suffers greatly from kinetic constraints that prevent realization of full capacity and reversibility. Understanding these kinetic limitations solely from experiments is extremely challenging due to the unusual complexity of various competing elemental reaction steps involved during the de/rehydrogenation reaction. This work aims to map out the energetics associated with initial dehydrogenation of Mg(BH 4 ) 2 from first-principles simulations and to identify the preferred reaction pathways. Our calculations suggest the rate-limiting step during BH 4 – –B 3 H 8 – conversion is the formation of the B 2 H 7 – intermediate. We further emphasize and clarify that the B 3 H 8 – and H – intermediates, formed during initial Mg(BH 4 ) 2 decomposition, appear as molecular species that are embedded in the Mg–BH 4 –Mg matrix as evidenced in the nuclear magnetic resonance measurements and not as bulk MgH 2 and Mg(B 3 H 8 ) 2 as previously assumed in theoretical predictions of the thermodynamics.
Water absorption plays a key role in the performance of polymeric anion exchange membranes. It influences important properties such as ionic conductivity and mechanical strength and alters their performance as solid electrolytes in hydrogen electrochemical devices for energy conversion. However, computational approaches that address the relationship between the polymer design and the absorption process are scarce. In this work, we introduce a simple thermodynamic model to predict the water absorption isotherms of polyelectrolyte membranes in contact with a water vapor reservoir that incorporates the specific chemical design of the polymers. The model accurately predicts the water content and macrostructural properties of polynorbornene membranes as a function of the water activity and successfully captures the effect of various polymer design parameters. The energy of pairwise attractive interactions predicted by our model provides a means to interpret the absorption process at the molecular level. The model also reveals the most significant favorable and unfavorable contributions to the free energy and indicates that their balance is solely governed by the water volume fraction, regardless of the polymer design. This universal behavior leads to important implications in the search for better ion exchange membranes.
Thermodynamics-based models predict the structure of polymer-grafted nanoparticles (PGNs) as well as their assembly behavior based on geometric parameters such as particle size, degree of polymerization, and density of grafted chains. The role of microscopic polymer dynamics, such as the mobility of repeat units in the melt state, in the evolution of the structure and properties remains unknown. Brillouin light spectroscopy (BLS), due to its capability to concurrently discern the local and global elastic properties of PGN assemblies, enables the probing of microscopic processes, such as brush interdigitation, sensitive to the annealing of the assembly. For poly(methyl methacrylate) (PMMA)-grafted silica (SiO 2 ) PGNs in the dry powder state and annealed above the glass transition temperature, BLS revealed fully reversible local elasticity, indicative of limited interdigitation between adjacent PGNs. This contrasts with polystyrene (PS)−SiO 2 analogs that displayed ready (and irreversible) fusion of brush layers during annealing. The retardation of brush interdigitation in PMMA-grafted systems is surprising, given the similar thermomechanical properties of both polymers, and is rationalized as the consequence of higher friction between PMMA repeats compared to PS. Microscopic dynamics thus has a profound impact on the kinetic path of structure (and property) evolution and thus should be considered during the processing of PGNs into functional hybrid materials.
Electronic tuning of metal hydrides enables precise control over potentials, mechanisms, selectivity, and rates of electrocatalytic reactions by regulating bond dissociation free energies such as the hydricity (Δ$G$ H- ° ) and $pK$ a of the catalyst. Here, we investigate a series of electronically tuned ruthenium hydrido complexes that are isostructural at the metal center: [Ru(4,4'-R 2 -bpy) 2 (CO)H] + (R = CF 3 , Cl, H, CH 3 , and CH 3 O; bpy = 2,2'-bipyridine) (denoted as (R)Ru-H+). A substantial 22 kcal mol -1 hydricity range is available across five complexes in three stable oxidation states: (R)Ru-H + , (R)Ru-H 0 , and (R)Ru-H - . Thermodynamic and mechanistic predictions of electrocatalytic proton reduction were tested experimentally by reducing protons from weak acids to H 2 . Two mechanisms are observed, depending on the acid strength and the catalyst hydricity. The rate constants for hydride transfer and protonation of the catalyst were, in some cases, extracted from the analysis of cyclic voltammetry data. A key finding is a 400 mV decrease in the catalytic overpotential for H 2 production by using a doubly reduced electron-poor metal hydride instead of a singly reduced electron-rich metal hydride. In conclusion, the former also exhibits a higher rate constant for hydride transfer, representing a strategy to disconnect rate and free energy relationships.
Electrocoagulation (EC), an electrochemical water treatment process, is commonly used to remove particulate and colloidal matter from water. Here, in this study, we demonstrate that EC, when coupled with membrane filtration, is also capable of removing dissolved species, such as Ca + , Mg 2+ , and SiO x . The removal of such species is important for downstream membrane-based desalination treatment of the water that can suffer from reduced performance due to membrane scaling. Here, we describe how EC can be combined with a low-pressure membrane (LPM) system to offer efficient (and potentially universal) pretreatment for downstream membrane desalination. Synthetic water, simulating cooling tower blowdown (CTBD) with elevated concentrations of hardness and silicates (Ca: 418 ppm, Mg: 63 ppm, SiO 2 : 50 ppm) is treated using EC coupled to ultrafiltration (UF) to remove Mg up to 30 ± 1%, Ca up to 29 ± 1%, and silica up to 99 ± 1%. We evaluated the effectiveness of the EC-UF pretreatment system in reducing downstream RO scaling using thermodynamic modeling to predict the saturation index (SI) at the RO membrane/water interface. An SI value below zero (SI < 0) indicates under-saturated conditions (with respect to a particular mineral) where mineral scaling does not take place, which correlates with improved water recovery. Our findings suggest that the EC-UF pretreatment system was able to increase water recovery by up to 30%, compared to 0% recovery without pretreatment, under optimal conditions (feed solution pH of 7 and an EC charge loading of 1800 C/L). Finally, we conducted an economic analysis showing that implementing an EC-UF system for CTBD water could yield a cost benefit of up to $\$$14.13 per m 3 compared to direct brine disposal.
Of the few weberite-type Na-ion cathodes explored to date, Na 2 Fe 2 F 7 exhibits the best performance, with capacities up to 184 mAh/g and energy densities up to 550 Wh/kg reported for this material. However, the development of robust structure–property relationships for this material is complicated by its tendency to form as a mixture of metastable polymorphs, and transform to a lower-energy Na y FeF 3 perovskite compound during electrochemical cycling. Our first-principles-guided exploration of Fe-based weberite solid solutions with redox-inactive Mg 2+ and Al 3+ predicts an enhanced thermodynamic stability of Na 2 Mg x Fe 2–x F 7 as the Mg content is increased, and the x = 0.125 composition is selected for further exploration. We demonstrate that the monoclinic polymorph (space group C2/c) of Na 2 Fe 2 F 7 (Mg0) and of a new Mg-substituted weberite composition, Na 2 Mg 0.125 Fe 1.875 F 7 (Mg0.125), can be isolated using an optimized synthesis protocol. The impact of Mg substitution on the stability of the weberite phase during electrochemical cycling, and on the extent and rate of Na (de)intercalation, is examined. Irrespective of the Mg content, we find that the weberite phase is retained when cycling over a narrow voltage window (2.8–4.0 V vs Na/Na+). Over a wider voltage range (1.9–4.0 V), Mg0 shows steady capacity fade due to its transformation to the NayFeF3 perovskite phase, while Mg0.125 displays more reversible cycling and a reduced phase transformation. Yet, Mg incorporation also leads to kinetically limited Na extraction and a reduced overall capacity. These findings highlight the need for the continued compositional optimization of weberite cathodes to improve their structural stability while maximizing their energy density.
Fe 4 S 4 clusters play essential roles in nature, classically in electron transport but increasingly in newly discovered reactivity or catalysis. These roles have spurred interest in developing synthetic Fe 4 S 4 systems and while several molecular and material systems built from Fe 4 S 4 clusters have been developed, comparatively few examples of synthetic Fe 4 S 4 cluster-based catalysts exist. Herein, we present the use of an Fe 4 S 4 -based coordination polymer as a catalyst for the direct and selective electroreduction of CO 2 to CO. Computational studies suggest that the reaction proceeds through CO 2 binding to a reduced Fe 4 S 4 cluster, followed by a series of protonation, reduction, and H 2 O loss steps to yield a CO-bound cluster that can finally exchange with CO 2 to restart the catalytic cycle. CO bound clusters are predicted to be thermodynamically stable, suggesting that carbonyl species might be off-cycle intermediates. Mechanistic CV studies as well as in situ studies by IR spectroscopy provide evidence for carbonyl-ligated clusters, supporting these compounds as unusual examples of small molecule binding to Fe 4 S 4 clusters. Finally, this work establishes Fe 4 S 4 cluster-based coordination polymers as direct electrocatalysts for CO 2 reduction and provides mechanistic insights into how these species mediate catalytic conversions of small molecules.
The fundamental understanding of intermolecular interactions of ionic liquids (ILs) with water is essential in predicting IL–water thermodynamic properties. In this study, intermolecular or noncovalent interactions were studied for 1,3-dimethyl imidazolium [DMIM]+ cation and nitrate [NO 3 ] – anion with water, employing quantum mechanics and molecular dynamics simulations. Molecular dynamics simulations were performed using a revised multipolar polarizable force field. The effect of water on ionic liquids was evaluated in terms of thermodynamic and dynamic properties. Thermodynamic properties included liquid densities ρ, excess molar volumes ΔV E , and liquid structures g(r). Dynamic properties included self-diffusion coefficients D of mixture constituents as a function of water concentration. The density of ionic liquid–water mixtures monotonically decrease with increasing concentration of water. A negative excess volume was obtained for low and high water concentrations, demonstrating strong intermolecular interactions of water with ionic liquid components. Liquid structures of ionic liquid–water mixtures revealed a tendency for anions to interact with cations at shorter intermolecular distances when the water concentration is increased. Diffusion rates were found to increase for all mixture components with increase in the fraction of water. A significant change in the diffusion rate was found at ~0.3 weight fraction of water. However, the water self-diffusion coefficient was dominant at all concentrations. Furthermore, the ratio of water/anion and anion/cation self-diffusion coefficients was found to decrease linearly with increasing concentration of water molecules.