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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Beyond Local Solvation Structure: Nanometric Aggregates in Battery Electrolytes and their Effect on Electrolyte Properties

Electrolytes are an essential component of all electrochemical storage and conversion devices, such as batteries. In the history of battery development, the complex nature of electrolytes has often been a bottleneck. Fundamental knowledge of electrolyte systems encompasses elucidation of structure-property relationships of the solution species. Recently, nanometric aggregates have been observed in several classes of electrolytes, including super-concentrated, redox-flow, multivalent, polymer, and ionic liquid-based electrolytes. Compared with the well-studied local solvation structures such as contact ion pairs and solvent-separated ions, these aggregates impose unique effects on the ion distribution and transport both within bulk electrolytes and at electrode/electrolyte interfaces. This Perspective highlights the discovery of the aggregates in various battery electrolytes and their impact on electrolyte properties. We also present an outlook for future studies of this emerging field of nanometric aggregates and the need for the development of new experimental and computational tools to study their properties.

Yu, Zhou↗
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 OSTI2025

Mesocrystal growth through oriented sliding and attachment of nanoplates

Oriented attachment is a critical, yet poorly understood, crystal growth pathway based on the self-assembly of nanocrystals. During oriented attachment, solvent-separated particles align and coalesce through forces that enable precise rotation and translation. While prior studies emphasized intragap forces driving crystallographic alignment, the forces enabling uniform stacking and superlattice formation remain unclear. Here, we demonstrate how macroscopic gibbsite mesocrystals emerge from nanoplates guided into staggered positions by directional sliding. Electron microscopy and X-ray scattering reveal the monoclinic superlattice structure, based on nanoplate stacking with a uniform ≈50° stagger along the gibbsite [010] direction. In situ liquid-cell TEM captures preferential sliding along the gibbsite [010] direction, decelerating with increasing particle overlap. Molecular dynamics simulations reveal that this staggered arrangement corresponds to a global free-energy minimum, rather than full alignment. The simulations also confirm that sliding along the [010] direction is energetically favored and provide insight into the role of interfacial water in achieving long-range ordered assemblies. These insights highlight the energy landscape’s role in oriented attachment, with implications for material synthesis and hierarchical structures in nature.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Reactive force fields for aqueous and interfacial magnesium carbonate formation

Here we develop Mg/C/O/H ReaxFF parameter sets for two environments: an aqueous force field for magnesium ions in solution and an interfacial force field for minerals and mineral–water interfaces. Since magnesium is highly ionic, we choose to fix the magnesium charge and model its interaction with C/O/H through Coulomb, Lennard-Jones, and Buckingham potentials. We parameterize the forcefields against several crystal structures, including brucite, magnesite, magnesia, magnesium hydride, and magnesium carbide, as well as Mg 2+ water binding energies for the aqueous forcefield. Then, we test the forcefield for other magnesium-containing crystals, solvent separated and contact ion-pairs and single-molecule/multilayer water adsorption energies on mineral surfaces. We also apply the forcefield to the forsterite–water and brucite–water interface that contains a bicarbonate ion. We observe that a long-range proton transfer mechanism deprotonates the bicarbonate ion to carbonate at the interface. Free energy calculations show that carbonate can attach to the magnesium surface with an energy barrier of about 0.22 eV, consistent with the free energy required for aqueous Mg–CO 3 ion pairing. Also, the diffusion constant of the hydroxide ions in the water layers formed on the forsterite surface are shown to be anisotropic and heterogeneous. These findings can help explain the experimentally observed fast nucleation and growth of magnesite at low temperature at the mineral–water–CO 2 interface in water-poor conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Integrated machine learning-molecular dynamics framework for electrolyte property prediction

Electrochemical stability windows determine the operating range of battery electrolytes, yet accurate prediction remains challenging because stability emerges from statistical ensembles of local solvation environments rather than single ground-state molecular structures. Traditional density functional theory calculations on energy-minimized clusters cannot capture the thermal variations in local coordination environments and geometries that govern decomposition, while SMILES-based machine learning methods lack explicit representation of three-dimensional solvation structure and ion pairing. Here, we introduce a structure-aware machine learning framework that predicts frontier orbital energies (HOMO and LUMO) directly from molecular dynamics-sampled solvation configurations, achieving sub-0.6 eV accuracy at computational costs 3–4 orders of magnitude lower than first-principles methods. Across twelve representative battery electrolytes, we demonstrate that solvent-separated and contact ion pairs exhibit strong size- and local chemistry dependent electronic stability, with variations in coordination shifts of HOMO or LUMO level by 2–3 eV, and that extended solvation structure and partially desolvated environment further modulate stability by up to 3 eV. By encoding the statistical nature of electrochemical failure through ensemble sampling of explicit solvation geometries, our approach enables high-throughput screening and rational design of next-generation battery electrolytes with mechanistic understanding of structure–property relationships.

Energy - Storage↗
DOE OSTI2022

Exploring the use of the pentaphenylcyclopentadienyl ligand in uranium chemistry: the crystal structure of (C

The reaction of UI3 with NaC5Ph5 in THF generated (η5-C5Ph5)UI2(THF)2 which was characterized by X-ray crystallography. The complex has a four-legged piano stool structure with a 2.539 Å U– (C5Ph5 ring centroid) distance that is slightly longer than the 2.504 Å distance in (η5-C5Me5)UI2(THF)3 which has a larger formal coordination number. Although the synthesis of the mono-cyclopentadienyl complex was facile, no bis-cyclopentadienyl complexes were isolated from reactions using two equivalents of the pentaphenylcyclopentadienyl ligand per uranium. In one UI3/2NaC5Ph5 synthesis, the X-ray crystal structure of (THF)3Na(η5-C5Ph5) was obtained. Attempts to reduce (η5-C5Ph5)UI2(THF)2 did not give evidence for a UII product. KC8 reductions of the in situ reaction of NdI3 and NaC5Ph5, analogous to the synthesis of (η5-C5Ph5)UI2(THF)2, yielded an X-ray crystal structure of a solvent-separated ion pair (SSIP), [K(18-crown-6)(THF)2][C5Ph5].

Chemistry↗
DOE OSTI2020

Structures of five salt forms of disulfonated monoazo dyes

The structures of five s -block metal salt forms of three disulfonated monoazo dyes are presented. These are poly[di-μ-aqua-diaqua[μ 4 -3,3'-(diazane-1,2-diyl)bis(benzenesulfonato)]disodium(I)], [Na 2 (C 12 H 8 N 2 O 6 S 2 )(H 2 O) 4 ] n , (I), catena -poly[[tetraaquacalcium(II)]-μ-3,3'-(diazane-1,2-diyl)bis(benzenesulfonato)], [Ca(C 12 H 8 N 2 O 6 S 2 )(H 2 O) 4 ] n , (II), catena -poly[[[diaquacalcium(II)]-μ-2-(4-amino-3-sulfonatophenyl)-1-(4-sulfonatophenyl)diazenium] dihydrate], {[Na(C 12 H 10 N 3 O 6 S 2 )(H 2 O) 2 ]·2H 2 O} n , (III), hexaaquamagnesium bis[2-(4-amino-3-sulfonatophenyl)-1-(4-sulfonatophenyl)diazenium] octahydrate, [Mg(H 2 O) 6 ](C 12 H 10 N 3 O 6 S 2 ) 2 ·8H 2 O, (IV), and poly[[{μ 2 -4-[2-(4-amino-2-methyl-5-methoxyphenyl)diazen-1-yl]benzene-1,3-disulfonato}di-μ-aqua-diaquabarium(II)] dihydrate], {[Ba(C 14 H 13 N 3 O 7 S 2 )(H 2 O) 4 ]·2H 2 O} n , (V). Compound (III) is that obtained on crystallizing the commercial dyestuff Acid Yellow 9 [74543-21-8]. The Mg species is a solvent-separated ion-pair structure and the others are all coordination polymers with bonds from the metal atoms to sulfonate groups. Compound (I) is a three-dimensional coordination polymer, (V) is a two-dimensional coordination polymer and both (II) and (III) are one-dimensional coordination polymers. The coordination behaviour of the azo ligands and the water ligands, the dimensionality of the coordination polymers and the overall packing motifs of these five structures are contrasted to those of monosulfonate monoazo congers. It is found that (I) and (II) adopt similar structural types to those of monosulfonate species but that the other three structures do not.

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

Mechanochemically accelerated deconstruction of chemically recyclable plastics

Plastics redesign for circularity has primarily focused on monomer chemistries enabling faster deconstruction rates concomitant with high monomer yields. Yet, during deconstruction, polymer chains interact with their reaction medium, which remains underexplored in polymer reactivity. Here, we show that, when plastics are deconstructed in reaction media that promote swelling, initial rates are accelerated by over sixfold beyond those in small-molecule analogs. This unexpected acceleration is primarily tied to mechanochemical activation of strained polymer chains; however, changes in the activity of water under polymer confinement and bond activation in solvent-separated ion pairs are also important. Together, deconstruction times can be shortened by seven times by codesigning plastics and their deconstruction processes.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Electrolyte Solvation and Ionic Association. VII. Correlating Raman Spectroscopic Data with Solvate Species

Raman spectroscopy is a widely utilized method for determining information about the solvate species present in electrolyte solutions. Difficulties arise, however, when interpreting the Raman data in terms of correlating the spectroscopic vibrational bands with specific solvate species. Examples are provided to demonstrate how erroneous conclusions may be drawn with regard to the identity and population (distribution) of the solvent-separated ion pair (SSIP), contact ion pair (CIP) and aggregate (AGG) solvate species frequently mentioned in the scientific literature.

contact ion pairs↗
DOE OSTI2021

Ion Pairing, Clustering and Transport in a LiFSI-TMP Electrolyte as Functions of Salt Concentration using Molecular Dynamics Simulations

Battery capacity is highly related to ion-pairing mechanisms in electrolytes, since a cluster formation can lead to dead Li formation, reducing the number of charge carriers and leading to capacity fading. We use molecular dynamics simulations to model an electrolyte comprising trimethyl phosphate (TMP) solvent and a lithium bis(fluorosulfonyl)imide (LiFSI) salt, exploring effects of salt concentration on solvation and ion-transport. We simulate the LiFSI-TMP electrolyte for salt concentrations of 0.7, 1.43 and 3.82 molar. A statistical analysis was performed to study ion-pairing, clustering, diffusivity, conductivity, and coordination of Li-ions, providing insights into relations between molecular structures and transport properties. Molecular structure of ionic components changes as concentration increases, from a predominant solvent separated ion pair (SSIP) and contact ion pair (CIP) to aggregate salt (AGG) and ionic cluster formation. Given the formation of the ionic cluster, the diffusion mechanism followed by Li-ions changes from a hopping/exchange to a vehicular mechanism as concentration increases; this is reflected in a decrease of ionic conductivities. Ionicity was also calculated to reveal how the ionic motion changes from an uncorrelated to a correlated one as the salt concentration increases. Furthermore, we also compared our results with experimental calculations performed for similar electrolyte systems

25 ENERGY STORAGE↗
DOE OSTI2023

IDAES-PSE 2.1.0 Release

The Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform is a versatile computational environment offering extensive process systems engineering (PSE) capabilities for optimizing the design and operation of complex, interacting technologies and systems. IDAES enables users to efficiently search vast, complex design spaces to discover the lowest cost, most environmentally sustainable solutions while supporting the full process modeling lifecycle, from conceptual design to dynamic optimization and control. The extensible, open platform empowers users to create models of novel processes and rapidly develop custom analyses, workflows, and end-user applications. IDAES-PSE 2.1.0 Release Highlights New IDAES Examples Repository Starting with this release, the IDAES examples are developed in the new IDAES/examples repository. Along with many content and usability improvements, the most significant changes are: To install the examples, after installing IDAES, run pip install idaes-examples The idaes get-examples command, previously used for this, has been removed The HTML version is now available at https://idaes-examples.readthedocs.io The previous URL, https://idaes.github.io/examples-pse, will not be updated and may be removed at some point in the future For more details, refer to the resources available at IDAES/examples. Removal of Non-Functional Apps A review of the code in the idaes/apps and idaes/models_extra folders was undertaken, and a number of tools were identified as being outdated or non-functional and no longer supported by their development teams. Due to this, the following tools have been removed: idaes/apps/alamopy_depr (note that the new ALAMOpy interface remains avaialble in idaes/core/surrogates) idaes/apps/helmet idaes/apps/ripe idaes/apps/roundingRegression idaes/models_extra/carbon_capture Pyomo 6.6 This version of IDAES is the first requiring Pyomo 6.6. This version of Pyomo contains multiple internal improvements and refactorings. While for the majority of cases this should have positive or no impact on solvability of IDAES models, we are aware of a small number of models that have been affected as a result of these changes. For more information, refer to the Pyomo 6.6.1 release notes. Other highlights Model Initialization A prototype API for a new approach to initializing IDAES models is now available which makes available some new techniques for initializing models. This is documented in the Initializing Models Reference Guide Modular Properties Framework Support for some transport properties Helmholtz Equation of State properties Better error checking for case where unit models are set to include phase equilibrium but the property package is set to support only a single phase Multi-Stream Contactor model: a new base model for systems involving contacting of two or more streams with mass transfer. This model is intended to be used as the foundation for models such as membrane separators, solvent extraction and other similar processes. This is documented in the Multi-Stream Contactor Reference Guide idaes/models_extra/power_generation report() methods for unit models using Helmholtz equation of state General Code Maintenance Streamlining of dependencies and creation of new optional dependency groupings to support non-core tools General linting of codebase to ensure compliance with most pylint checks Spell checking of all code and doc strings Removal of backward compatibility code for Python 2

IDAES↗
DOE OSTI2024

Combinatorial Membrane Synthesis: Fundamentals of Hybrid Metal-Organic Brush (MOB) Membranes for Organic Solvent Nanofiltration (Renewal)

(a) Goals: The goals of our current 3-year research project are (i) expansion of the novel modification technique Single Electron Transfer- Living Radical Polymerization (SET-LRP) to graft polymer brushes on a polyimide stable support followed by characterization and testing of the ensuing membrane (ii) stiffen brush network utilizing (a) metal organic frameworks and (b) covalent bonds formed as a result of crosslinking the grafted brush network. (b) Approach: The approach taken for this research was: (i) to graft branches terminated with a carboxyl group on each bristle (or bristle branch) allowing (a) metal immobilization to form MOF-like structures between branches or (b) crosslink bristles using aliphatic or aromatic diamines; (ii) to test the performance of the membranes using organic dyes, pure solvents (alcohols) and industrially relevant organic solvent separations; and (iii) to characterize these modified brush membranes. (c) Findings: The significant findings from the past one year are summarized below (papers resulting from this work are listed below and in D. PUBLICATIONS and relevant references are found in E. REFERENCES.

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

Oxides and Nitrides with Asymmetric Pore Structure from Block Copolymer Co–Assembly and Non–Solvent Induced Phase Separation

Materials combining an asymmetric pore structure with mesopores everywhere enable high surface area accessibility and fast transport, making them attractive for e.g., energy conversion and storage applications. Block copolymer (BCP)/inorganic precursor co-assembly combined with non-solvent induced phase separation (NIPS) provides a route to materials in which a mesoporous top surface layer merges into an asymmetric support with graded porosity along the film normal and mesopores throughout. Here, the co-assembly and non-solvent-induced phase separation (CNIPS) of poly(isoprene)-b-poly(styrene)-b-poly(4-vinylpyridine) (ISV) triblock terpolymer and titanium dioxide (TiO 2 ) sol-gel nanoparticlesare reported. Heat-treatment in air results in free-standing asymmetric porous TiO 2 . Further thermal processing in ammonia results in free-standing asymmetric porous titanium nitride (TiN). processing changes alter structural membrane characteristics is demonstrated. Changing the CNIPS evaporation time results in various membrane cross-sections ( finger-like to sponge-like). Further, oxide and nitride material composition, crystallinity, and porosity are tuned by varying thermal processing conditions. Finally, thermal processing condition effects are probed on phase-pure asymmetric nitride membrane behavior using cyclic voltammetry to elucidate their influence, e.g., on specific capacitance. Results provide further insights into improving asymmetric and porous materials for applications including energy conversion and storage, separation, and catalysis and motivate a further expansion of CNIPS to other (in)organic materials.

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

Engineering Redox Flow Battery Electrodes with Spatially Varying Porosity Using Non‐Solvent‐Induced Phase Separation

Redox flow batteries (RFBs) are a promising electrochemical platform for efficiently and reliably delivering electricity to the grid. Within the RFB, porous carbonaceous electrodes facilitate electrochemical reactions and distribute the flowing electrolyte. Tailoring electrode microstructure and surface area can improve RFB performance, lowering costs. Electrodes with spatially varying porosity may increase electrode utilization and provide surface area in reaction‐limited zones; however, the efficacy of such designs remains an open area of research. Herein, a non‐solvent‐induced phase‐separation (NIPS) technique that enables the reproducible synthesis of macrovoid‐free electrodes with well‐defined across‐thickness porosity gradients is described. The monotonically varying porosity profile is quantified and the physical properties and surface chemistries of porosity‐gradient electrodes are compared with macrovoid‐containing electrode, also synthesized by NIPS. Then, the electrochemical and fluid dynamic performance of the porosity‐gradient electrodes is evaluated, exploring the effect of changing the direction of the porosity gradient and benchmarking against the macrovoid‐containing electrode. Lastly, the performance is examined in a vanadium RFB, finding that the porosity‐gradient electrode outperforms the macrovoid electrode, is independent of gradient direction, and performs favorably compared to advanced electrodes in the contemporary literature. It is anticipated that the approach motivates further exploration of microstructurally tailored electrodes in electrochemical systems.

25 ENERGY STORAGE↗
DOE OSTI2021

Non-Solvent Induced Phase Separation Enables Designer Redox Flow Battery Electrodes

Porous carbonaceous electrodes are performance-defining components in redox flow batteries (RFBs), where their properties impact the efficiency, cost, and durability of the system. The overarching challenge is to simultaneously fulfill multiple seemingly contradictory requirements—i.e., high surface area, low pressure drop, and facile mass transport—without sacrificing scalability or manufacturability. Here, non-solvent induced phase separation (NIPS) is proposed as a versatile method to synthesize tunable porous structures suitable for use as RFB electrodes. The variation of the relative concentration of scaffold-forming polyacrylonitrile to pore-forming poly(vinylpyrrolidone) is demonstrated to result in electrodes with distinct microstructure and porosity. Tomographic microscopy, porosimetry, and spectroscopy are used to characterize the 3D structure and surface chemistry. Flow cell studies with two common redox species (i.e., all-vanadium and Fe2+/3+) reveal that the novel electrodes can outperform traditional carbon fiber electrodes. It is posited that the bimodal porous structure, with interconnected large (>50 µm) macrovoids in the through-plane direction and smaller (<5 µm) pores throughout, provides a favorable balance between offsetting traits. Although nascent, the NIPS synthesis approach has the potential to serve as a technology platform for the development of porous electrodes specifically designed to enable electrochemical flow technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Solvent Screening for Separation Processes Using Machine Learning and High-Throughput Technologies

As the chemical industry shifts toward sustainable practices, there is a growing initiative to replace conventional fossil-derived solvents with environmentally friendly alternatives such as ionic liquids (ILs) and deep eutectic solvents (DESs). Artificial intelligence (AI) plays a key role in the discovery and design of novel solvents and the development of green processes. This review explores the latest advancements in AI-assisted solvent screening with a specific focus on machine learning (ML) models for physicochemical property prediction and separation process design. Additionally, this paper highlights recent progress in the development of automated high-throughput (HT) platforms for solvent screening. Finally, this paper discusses the challenges and prospects of ML-driven HT strategies for green solvent design and optimization. To this end, this review provides key insights to advance solvent screening strategies for future chemical and separation processes.

Artificial intelligence↗
DOE OSTI2024

Development of Transformational Solvents for CO2 Separations

The primary objective of this CRADA activity is to use a combined molecular modeling and experimental validation approach to refine and develop transformational solvents for carbon capture. PNNL’s role on this project is currently funded by the Department of Energy’s (DOE) Office of Fossil Energy (OFE). PNNL is developing advanced molecular modeling based on their CO2BOLs solvent platform as a demonstration solvent for the activity; the model was developed and compared against measured data for CO2BOL derivatives. Here, a CRADA with PNNL and GE will leverage their current molecular models and apply them to solvent classes that operate on carbamate chemistry, specifically GE’s aminosilicone solvent class. The molecular models will be used to predict physical and thermodynamic properties, such as viscosity, as a means to predict advanced formulations with reduced viscosity compared to current aminosilicone derivatives, enabling optimized thermodynamic and kinetic metrics for economical carbon capture for this class of materials. Together, PNNL and GE will develop a comprehensive means of linking molecular modeling parameters to intermediate physical properties as a means to improve solvent performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗