Search NASA⌕ Search

SEARCH · Search NASA

Results for “predictive thermodynamics”

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.

At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Structurally Constrained Evolutionary Algorithm for the Discovery and Design of Metastable Phases

Metastable materials are abundant in nature and technology, showcasing remarkable properties that inspire innovative materials design. However, traditional crystal structure prediction methods, which rely solely on energetic factors to determine a structure’s fitness, are not suitable for predicting the vast number of potentially synthesizable phases that represent a local minimum corresponding to a state in thermodynamic equilibrium. Here, we present a new approach for the prediction of metastable phases with specific structural features, and interface this method with the XTALOPT evolutionary algorithm. Our method relies on structural features that include the local crystalline order (e.g., the coordination number or chemical environment), and symmetry (e.g., Bravais lattice and space group) to filter the breeding pool of an evolutionary crystal structure search. The effectiveness of this approach is benchmarked on three known metastable systems: XeN 8 , with a two-dimensional polymeric nitrogen sublattice, brookite TiO 2 , and a high pressure BaH 4 phase that was recently characterized. Additionally, a newly predicted metastable melaminate salt, P1¯WC 3 N 6 , was found to possess an energy that is lower than two phases proposed in a recent computational study. Here, the method presented here could help in identifying the structures of compounds that have already been synthesized, and developing new synthesis targets with desired properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Optimal thermodynamic conditions to minimize kinetic by-products in aqueous materials synthesis

Abstract Phase diagrams offer substantial predictive power for materials synthesis by identifying the stability regions of target phases. However, thermodynamic phase diagrams do not offer explicit information regarding the kinetic competitiveness of undesired by-product phases. Here we propose a quantitative and computable thermodynamic metric to identify synthesis conditions under which the propensity to form kinetically competing by-products is minimized. We hypothesize that thermodynamic competition is minimized when the difference in free energy between a target phase and the minimal energy of all other competing phases is maximized. We validate this hypothesis for aqueous materials synthesis through two empirical approaches: first, by analysing 331 aqueous synthesis recipes text-mined from the literature; and second, by systematic experimental synthesis of LiIn(IO 3 ) 4 and LiFePO 4 across a wide range of aqueous electrochemical conditions. Our results show that even for synthesis conditions that are within the stability region of a thermodynamic Pourbaix diagram, phase-pure synthesis occurs only when thermodynamic competition with undesired phases is minimized.

Wang, Zheren (ORCID:0000000227429451)↗
DOE OSTI2023

Ab initio modeling and thermodynamics of hydrated plutonium oxalates

An ab initio study on the plutonium oxalate hydrates: Pu 2 (C 2 O 4 ) 3 ·10H 2 O and Pu(C 2 O 4 ) 2 ·6H 2 O, using PBE exchange-correlation with D3 dispersion correction and Hubbard correction for the plutonium atoms, was performed and compared to experimental vibrational spectral and thermodynamic property values. Here we demonstrated that this technique can accurately predict the experimental infrared spectra Pu(III) oxalate hydrate, as well as the Raman peak of PuO 2 (used to calculate the thermodynamic properties of the oxalates). For Pu(IV) oxalate hydrate we found that our predicted structure agreed qualitatively with PXRD measurements, the only available experimental determination of the structure. Using this method at standard temperature and pressure, we predicted standard enthalpies of formation of -6,755 kJ mol -1 and -3,923 kJ mol -1 and standard Gibbs free energy of formation of -5,899 kJ mol -1 and -3,386 kJ mol -1 for Pu 2 (C 2 O 4 ) 3 ·10H 2 O and Pu(C 2 O 4 ) 2 ·6H 2 O, respectively.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Co-ion specific effect on sodium halides sorption and transport in a cross-linked poly(p-styrene sulfonate-co-divinylbenzene) for membrane applications

Here, sodium halide sorption and transport in a cation exchange membrane based on cross-linked poly(p-styrene sulfonate-co-divinylbenzene) (CR61) were investigated experimentally and theoretically. At fixed co-ion valence, sodium halide partitioning between aqueous electrolyte solutions and ion exchange polymers is only minimally sensitive to co-ion chemical identity. NaCl, NaF and NaI exhibit similar solubility coefficients in CR61, which agrees reasonably well with predictions of the Donnan-Manning model, especially at high external salt concentrations. Ion activity coefficients in the membrane were evaluated and ion sorption thermodynamics were investigated. Ion diffusion coefficients in CR61 were predicted using a recently developed framework based on the Manning diffusion model, which accounts for electrostatic effects on ion diffusion, and the Mackie-Meares model, which accounts for tortuosity/area effects on ion diffusion in swollen polymers. Model predictions agree reasonably well with the experimental data, especially at high external salt concentrations. The observed decrease in salt diffusion coefficients in CR61 relative to values in aqueous solutions is due primarily to tortuosity/area effects. Salt diffusion coefficients correlate with co-ion hydrated radius and membrane water content. Finally, salt permeability coefficients predicted using the solution-diffusion model exhibit good agreement with experimental data, especially at external salt concentration greater than 0.1 mol/L.

42 ENGINEERING↗
DOE OSTI2021

Modeling in High Temperature Corrosion: A Review and Outlook

Realizing higher operating temperatures to increase efficiency of future applications for energy conversion and storage while minimizing cost is a challenge for development of high-temperature materials. Simultaneous optimization of mechanical properties and corrosion resistance continues to be a difficult task but is essential due to the need to significantly accelerate the transition between technology readiness levels in the future. Furthermore, oxidation-induced degradation will be a critical life-limiting mechanism at increased operating temperatures. Suitable high-temperature materials cannot be solely identified by time-consuming experiments and reliable computational methods incorporating the relevant physics of processes must be considered to complement the experimental efforts. In the present work, a review of the methods employed to model oxidation-induced material degradation described in literature will be discussed. Furthermore, their capability to predict lifetime and aid in material selection will be evaluated.

36 MATERIALS SCIENCE↗
DOE OSTI2021

dGPredictor: Automated fragmentation method for metabolic reaction free energy prediction and de novo pathway design

Group contribution (GC) methods are conventionally used in thermodynamics analysis of metabolic pathways to estimate the standard Gibbs energy change ( Δ r G ′ o ) of enzymatic reactions from limited experimental measurements. However, these methods are limited by their dependence on manually curated groups and inability to capture stereochemical information, leading to low reaction coverage. Herein, we introduce an automated molecular fingerprint-based thermodynamic analysis tool called dGPredictor that enables the consideration of stereochemistry within metabolite structures and thus increases reaction coverage. dGPredictor has comparable prediction accuracy compared to existing GC methods and can capture Gibbs energy changes for isomerase and transferase reactions, which exhibit no overall group changes. We also demonstrate dGPredictor’s ability to predict the Gibbs energy change for novel reactions and seamless integration within de novo metabolic pathway design tools such as novoStoic for safeguarding against the inclusion of reaction steps with infeasible directionalities. To facilitate easy access to dGPredictor, we developed a graphical user interface to predict the standard Gibbs energy change for reactions at various pH and ionic strengths. The tool allows customized user input of known metabolites as KEGG IDs and novel metabolites as InChI strings ( https://github.com/maranasgroup/dGPredictor ).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Understanding and Predicting the Spatially Resolved Adsorption Properties of Nanoporous Materials

Using knowledge from statistical thermodynamics and crystallography, we develop an image–image translation model, called SorbIIT, that uses three-dimensional grids of adsorbate–adsorbent interaction energies as input to predict the spatially resolved loading surface of nanoporous materials over a broad range of temperatures and pressures. SorbIIT consists of a closed-form differential model for loading-surface prediction and a U-Net to generate spatial differential distributions from the energy grids. SorbIIT is trained using the energy grids and adsorbate distributions (obtained from high-throughput simulations) of 50 synthesized and 70 hypothetical zeolites and applied for predicting the adsorption of carbon dioxide, hydrogen sulfide, n-butane, 2-methylpropane, krypton, and xenon in other zeolites from 256 to 400 K. In conclusion, employing a quadratic isotherm model for the local differentiation, SorbIIT yields mean R 2 values of 0.998 for total adsorption and 0.6904 for local adsorption with a resolution of 0.2 Å, and a value of 0.721 for the structural similarity of the local loading distribution.

Sun, Yangzesheng↗
DOE OSTI2023

A Physics-Based Data-Driven Approach for Modeling of Environmental Degradation in Elastomers

Abstract Elastomers are now commonly used in a number of industries, including aerospace, structure, transportation, shipbuilding, and automotive, due to their excellent workability, formability, and flexibility. During their activity, elastomers are subjected to harsh environmental conditions, which decreases their resilience. False predictions made early in their lives can have major financial and environmental implications. Elastomers’ performance and properties, such as strength, durability, and density, are influenced by chemical changes in these materials, known as degradation, which occurs over time. This process can alter the morphology of a polymer matrix as well as cause chain scission and cross-linking, resulting in different behaviors than that of the unaged material. To demonstrate the effect of thermaloxidative aging on the mechanical behavior of elastomers, several experimental and theoretical models have been proposed. In view of the large volume of experimental data available on micro-structural evolution in the course of aging, we propose a physics-based data-driven approach to overcome the shortcomings of both phenomenological and micro-mechanical models. This work presents a novel thermodynamically consistent, multiagent machine-learned model for predicting the constitutive behavior of cross-linked elastomers during environmental aging, such as thermo-oxidative and hydrolytic aging for various states of deformation. Single mechanism degradation changes the polymer matrix over time where it is causing chain scission, reduction of cross-links, and morphology change. To capture the idealized Mullins effect and permanent set due to the effect of single aging mechanisms on nonlinear mechanical responses of elastomers, we propose a data-driven model for simulating inelastic elements in a polymer matrix. By using a sequential order reduction, we were able to reduce the 3D stress-strain tensor mapping problem to a small number of super-constrained 1D mapping problems. To systematically classify such mapping problems into a few categories, an assembly of multiple replicated conditional neural network learning agents (L-agents) is used based on our recent work. Each category is represented by a different type of agent. The effect of deformation history, aging time, and aging temperature is captured by this model. The model is validated using a broad collection of data, ranging from our experimental results to data from the literature. In addition, thermodynamic consistency and frame independence are investigated. The most significant achievements of this model are its precision, simplicity, and prediction of inelasticity under various states of deformation. The model’s accuracy and simplicity make it a good option for commercial and industrial applications. Conveniently, due to the model modular nature, it can be expanded in the future to include viscoelasticity and non-isotropic formation for better precision.

Ghaderi, Aref↗
DOE OSTI2023

Experimental and computational design tools for industrial drying processes: Challenges in process‐limit prediction

Abstract The coating and drying of inks and slurries are important steps to manufacture a plethora of products. Drying processes, particularly, comprise energy‐intensive steps that affect product cost and quality. Prior work has highlighted failures of various multicomponent diffusivity models to conserve mass in dryer modeling and challenges in predicting process limits given variability in published values of key thermodynamic parameters. Herein, we develop a computational model and benchtop drying experiments to investigate these concerns for drying polymer‐laden coatings. Model predictions of process limits in a single‐zone drying oven demonstrate that published variability in Flory–Huggins parameter yields large variations in predicted operating temperatures above which blistering occurs. This indicates that caution should be exercised when choosing approaches to obtain or predict the Flory–Huggins parameter, and that both benchtop drying experiments and a set of additional experiments, such as sorption experiments, are needed to fully characterize and optimize a given drying process.

Parrish, Chance↗
DOE OSTI2021

Influence of Protection Layers on Thermal Stability of Nitride Thin Films

Nitrides are commonly studied for their properties relevant to numerous coating applications, and have been recently used to synthesize newly predicted materials in thin film form. However, thermodynamic stability of such nitride materials is difficult to experimentally evaluate, due to limited thermal budget of thin films. Here we show that thermal stability of nitride films can be extended using protection layers. Specifically, we find that zirconium nitride (ZrN) thin films are stable up to at least 1200°C annealing temperature in N2 atmosphere, if aluminum nitride (AlN) diffusion barriers and capping layers are used. X-ray diffraction (XRD) measurements show the expected thermodynamically stable rocksalt structure of ZrN, and scanning electron microscopy (SEM) confirms compact microstructure of these ZrN films. Without such protection layers, transient bixbyite Zr2ON2 phase and high-temperature ZrSi2 phase are observed after annealing by XRD, SEM, and auger electron spectroscopy (AES), due to side reaction with native surface oxide (ZrOx) and substrate (Si) respectively. These results demonstrate that protection layers are beneficial to increase the thermal budget of nitride thin films, for evaluating thermodynamic stability of new nitride materials, for capturing transient metastable phases during crystallization, and for using functional nitride coatings in extreme environments.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A Gaussian process autoregressive model capturing microstructure evolution paths in a Ni–Mo–Nb alloy

Additive manufacturing is increasingly being employed to produce components of complex geometries in structural alloys because of the expected energy savings associated with the near-net-shape capability and the ability to build in novel internal features that are not possible with many conventional manufacturing approaches. However, because of the extreme thermal conditions encountered, the non-equilibrium microstructures produced during powder bed-based additive manufacturing processes must be subjected to custom post-heat treatment processes to recover the target mechanical properties. Phase-field models and simulation techniques have matured to a state where the microstructure evolution paths, and the morphologies of the resulting precipitate phases can be predicted reasonably accurately, considering alloy-specific thermodynamic and kinetic aspects of the nucleation and growth processes. However, phase-field simulations are computationally intensive, which precludes the ability to apply the simulations directly to the length scale of the entire component. Therefore, it is highly desirable to develop low-computational-cost surrogate models that effectively capture the physics at the microstructural length scale, while facilitating the design of optimized processing conditions resulting in location-specific targeted microstructures at the component scale. The work presented here demonstrates the application of the materials knowledge system framework to develop a surrogate model that effectively captures the microstructural path during annealing of a Ni–Mo–Nb alloy containing different Mo and Nb compositions known to segregate during solidification under additive manufacturing conditions. Specifically, the surrogate model built in this work is based on a Gaussian process autoregressive model informed by statistical representation of simulated microstructures using two-point correlations and dimensionality reduction through principal component analysis. In conclusion, this surrogate model is shown to capture the bifurcation of the microstructural path during precipitation, which yields a microstructure dominated by the $\gamma^{\prime\prime}$ phase at high Nb concentrations and the $\delta$ phase at low Nb concentrations.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Harnessing on-machine metrology data for prints with a surrogate model for laser powder directed energy deposition

In this study, we leverage the massive amount of multi-modal on-machine metrology data generated from Laser Powder Directed Energy Deposition (LP-DED) to construct a comprehensive surrogate model of the 3D printing process. By employing Dynamic Mode Decomposition with Control (DMDc), a data-driven technique, we capture the complex physics inherent in this extensive dataset. This physics-based surrogate model emphasizes thermodynamically significant quantities, enabling us to accurately predict key process outcomes. The model ingests 21 process parameters, including laser power, scan rate, and position, while providing outputs such as melt pool temperature, melt pool size, and other essential observables. Furthermore, it incorporates uncertainty quantification to provide bounds on these predictions, enhancing reliability and confidence in the results. We then deploy the surrogate model on a new, unseen part and monitor the printing process as validation of the method. Our experimental results demonstrate that the predictions align with actual measurements with high accuracy, confirming the effectiveness of our approach. Furthermore, this methodology not only facilitates real-time predictions but also operates at process-relevant speeds, establishing a basis for implementing feedback control in LP-DED.

Digital twins↗
DOE OSTI2025

Methanogenesis and Acetogenesis in Hydrogenotrophy with Carbonate Minerals: Dependence on Mineral Surface Area, Biofilm Growth, and Microbial Community

The production, storage, and use of hydrogen are anticipated to grow substantially to achieve energy and climate goals. Consequently, microbial communities in many terrestrial and subsurface Earth environments could be exposed to elevated hydrogen concentrations. Hydrogen stimulates metabolic processes that reduce aqueous chemical species, such as bicarbonate or sulfate, that can exchange with solid mineral phases, but the controls on microbial hydrogenotrophy with mineral sources of electron acceptors are not fully understood. Herein, we applied laboratory experiments and biogeochemical modeling to study the response of a natural microbial community to an elevated partial pressure of hydrogen in the presence of carbonate minerals of varying composition, solubility, and size. Experimental incubations and simulation results showed that hydrogen consumption by microbial communities was initially dominated by sulfate reduction and, subsequently, transitioned to acetogenesis and methanogenesis. The rates of acetogenesis and methanogenesis were not correlated with the solubility of carbonate minerals. Instead, we observed strong linear correlations between the rates and surface area of carbonate minerals. Methane and acetate production slowed down in all incubations after about 2 weeks of incubation, although biogeochemical modeling predicted that the metabolic processes were not thermodynamically limited. Electron microscopy and infrared spectroscopy showed that biofilms with diverse microorganisms grew on the carbonates during this period. The methane δ13C value significantly increased, consistent with slower growth at elevated pH. This work highlights that microbial communities form biofilm on carbonate mineral surfaces as a response to hydrogen and that biofilm formation could pose a strong kinetic limitation to hydrogenotrophic metabolism utilizing carbonate minerals.

Acetobacterium↗
DOE OSTI2022

Ensemble Simulations and Experimental Free Energy Distributions: Evaluation and Characterization of Isoxazole Amides as SMYD3 Inhibitors

Optimization of binding affinities for ligands to their target protein is a primary objective in rational drug discovery. Herein, we report on a collaborative study that evaluates various compounds designed to bind to the SET and MYND domain-containing protein 3 (SMYD3). SMYD3 is a histone methyltransferase and plays an important role in transcriptional regulation in cell proliferation, cell cycle, and human carcinogenesis. Experimental measurements using the scintillation proximity assay show that the distributions of binding free energies from a large number of independent measurements exhibit non-normal properties. We use ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent) and TIES (thermodynamic integration with enhanced sampling) protocols to predict the binding free energies and to provide a detailed chemical insight into the nature of ligand-protein binding. Our results show that the 1-trajectory ESMACS protocol works well for the set of ligands studied here. Although one unexplained outlier exists, we obtain excellent statistical ranking across the set of compounds from the ESMACS protocol and good agreement between calculations and experiments for the relative binding free energies from the TIES protocol. ESMACS and TIES are again found to be powerful protocols for the accurate comparison of the binding free energies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Intrinsic Defects in MoS 2 Grown by Pulsed Laser Deposition: From Monolayers to Bilayers

Pulsed laser deposition (PLD) can be considered a powerful method for the growth of two-dimensional (2D) transition-metal dichalcogenides (TMDs) into van der Waals heterostructures. However, despite significant progress, the defects in 2D TMDs grown by PLD remain largely unknown and yet to be explored. In this work, we combine atomic resolution images and first-principles calculations to reveal the atomic structure of defects, grains, and grain boundaries in mono- and bilayer MoS 2 grown by PLD. We find that sulfur vacancies and MoS antisites are the predominant point defects in 2D MoS 2 . We predict that the aforementioned point defects are thermodynamically favorable under a Mo-rich/S-poor environment. The MoS 2 monolayers are polycrystalline and feature nanometer size grains connected by a high density of grain boundaries. In particular, the coalescence of nanometer grains results in the formation of 180° mirror twin boundaries consisting of distinct 4- and 8-membered rings. We show that PLD synthesis of bilayer MoS 2 results in various structural symmetries, including AA' and AB, but also turbostratic with characteristic moiré patterns. Moreover, we report on the experimental demonstration of an electron beam-driven transition between the AB and AA' stacking orientations in bilayer MoS 2 . These results provide a detailed insight into the atomic structure of monolayer MoS 2 and the role of the grain boundaries on the growth of bilayer MoS 2 , which has importance for future applications in optoelectronics.

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

Unravelling the water oxidation mechanism on NaTaO 3 -based photocatalysts

Understanding the detailed kinetics and mechanisms of water oxidation is crucial because water oxidation is the bottleneck reaction in overall water splitting. In this work, we investigated photocatalytic water oxidation on NaTaO 3 -based photocatalysts. With in situ FT-IR spectroscopy, two new water oxidation intermediates with IR absorption peaks centered at 1058 and 935 cm –1 were observed and assigned to Ta(VI)=O and Ta(O 2 ) peroxo species, respectively. With H 2 18 O as the reactant and NaTa 16 O 3 as the photocatalyst, both 16 O 18 O and 18 O 2 were evolved continuously from overall water splitting as analyzed by mass spectroscopy (MS), demonstrating that the lattice O atoms in NaTaO 3 -based photocatalysts participate in the formation of O 2 during the reaction. DFT results are consistent with the formation of Ta=O, 16 O 18 O and 18 O 18 O intermediates and products, while predicting that several water oxidation pathways are thermodynamically accessible for water oxidation on NaTaO 3 . Based on these observations, we propose a three-step two-site mechanism for photocatalytic water oxidation on NaTaO 3 -based photocatalysts in which lattice oxygen plays a key role. The elucidation of the present mechanism should help in broadly understanding water oxidation on semiconductor-based photocatalysts.

14 SOLAR ENERGY↗
DOE OSTI2022

A theoretical investigation of the hydrolysis of uranium hexafluoride: the initiation mechanism and vibrational spectroscopy

Depleted uranium hexafluoride (UF 6 ), a stockpiled byproduct of the nuclear fuel cycle, reacts readily with atmospheric humidity, but the mechanism is poorly understood. Here we compare several potential initiation steps at a consistent level of theory, generating underlying structures and vibrational modes using hybrid density functional theory (DFT) and computing relative energies of stationary points with double-hybrid (DH) DFT. A benchmark comparison is performed to assess the quality of DH-DFT data using reference energy differences obtained using a complete-basis-limit coupled-cluster (CC) composite method. The associated large-basis CC computations were enabled by a new general-purpose pseudopotential capability implemented as part of this work. Dispersion-corrected parameter-free DH-DFT methods, namely PBE0-DH-D3(BJ) and PBE-QIDH-D3(BJ), provided mean unsigned errors within chemical accuracy (1 kcal mol -1 ) for a set of barrier heights corresponding to the most energetically favorable initiation steps. The hydrolysis mechanism is found to proceed via intermolecular hydrogen transfer within van der Waals complexes involving UF 6 , UF 5 OH, and UOF 4 , in agreement with previous studies, followed by the formation of a previously unappreciated dihydroxide intermediate, UF 4 (OH) 2 . The dihydroxide is predicted to form under both kinetic and thermodynamic control, and, unlike the alternate pathway leading to the UO 2 F 2 monomer, its reaction energy is exothermic, in agreement with observation. Finally, harmonic and anharmonic vibrational simulations are performed to reinterpret literature infrared spectroscopy in light of this newly identified species.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗