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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Computational and experimental analysis of structural and Thermophysical properties of LiX-KX (X = chloride, iodide, or bromide) molten salts

Molten salts containing lithium (Li) and potassium (K) halides, including chlorides (Cl), bromides (Br), and iodides (I), are pivotal in advanced technological applications including nuclear reactor technologies, thermal batteries, and industrial pyrochemical processes. Despite their significance, the availability of the temperature-dependent structural and thermophysical properties of these molten salts remain scarce. Here, this study addresses this knowledge gap by predicting and measuring the key properties such as coordination number, radial distribution function, density, heat capacity, and volumetric thermal expansion of molten LiI-KI, LiBr-KBr and LiCl-KCl eutectic mixtures. Utilizing ab initio molecular dynamics (AIMD) simulations, we predict these properties of LiX-KX (X=Br,I, Cl) across various temperatures, with chlorides as a benchmark. Experimental measurements using Archimedes methods and Differential Scanning Calorimetry validate the densities and heat capacities of these molten salts. Our comprehensive analysis of the structural and thermophysical properties provides critical insights into the behavior of these molten salts at various temperatures, enhancing the understanding necessary for their application in advanced technologies.

36 - MATERIALS SCIENCE↗
DOE OSTI2025

Overcoming sparse datasets with multi-task learning as applied to high entropy alloys

Abstract The design of novel High Entropy Alloys for use in high-temperature applications is an area of active interest due to their potential to provide exceptional properties compared to conventional alloys. Since the increased popularity of machine learning, an important cog in the design process has been training surrogate models on alloy properties. However, these Single-Task models are trained on individual mechanical properties and do not take advantage of the relatedness between properties. Multi-Task models can capture the interdependencies between tasks, leading to potentially more accurate predictions for all tasks. In this paper, we investigate if Multi-Task models can show improvement over Single-Task models when used for predicting the mechanical properties of these alloys. To ensure fair evaluation between the models, we apply L 0 regularization and skip connections to the models, which allows them to adjust the number of model parameters and depth for optimal performance. We find that the Multi-Task models can leverage task relationships to perform better than Single-Task models, especially for high amounts of missing data in the tasks. Furthermore, adding simple auxiliary targets can boost Multi-Task performance even further despite not being effective as input descriptors to single-task models themselves. We anticipate that the proposed strategies can achieve more accurate predictions and consequently enable better design capabilities for such data-constrained domains without incurring much additional computational cost.

Debnath, Arindam (ORCID:0000000194274499)↗
DOE OSTI2024

Prediction of Dielectric Constant in Series of Polymers by Quantitative Structure-Property Relationship (QSPR)

This work is devoted to the investigation of dielectric permittivity which is influenced by electronic, ionic, and dipolar polarization mechanisms, contributing to the material’s capacity to store electrical energy. In this study, an extended dataset of 86 polymers was analyzed, and two quantitative structure–property relationship (QSPR) models were developed to predict dielectric permittivity. From an initial set of 1273 descriptors, the most relevant ones were selected using a genetic algorithm, and machine learning models were built using the Gradient Boosting Regressor (GBR). In contrast to Multiple Linear Regression (MLR)- and Partial Least Squares (PLS)-based models, the gradient boosting models excel in handling nonlinear relationships and multicollinearity, iteratively optimizing decision trees to improve accuracy without overfitting. The developed GBR models showed high R2 coefficients of 0.938 and 0.822, for the training and test sets, respectively. An Accumulated Local Effect (ALE) technique was applied to assess the relationship between the selected descriptors—eight for the GB_A model and six for the GB_B model, and their impact on target property. ALE analysis revealed that descriptors such as TDB09m had a strong positive effect on permittivity, while MLOGP2 showed a negative effect. These results highlight the effectiveness of the GBR approach in predicting the dielectric properties of polymers, offering improved accuracy and interpretability.

Ascencio-Medina, Estefania↗
DOE OSTI2024

A novel digital lifecycle for Material‐Process‐Microstructure‐Performance relationships of thermoplastic olefins foams manufactured via supercritical fluid assisted foam injection molding

Abstract This research significantly enhances the applicability of thermoplastic olefins (TPOs) in the automotive industry using supercritical N 2 as a physical foaming agent, effectively addressing the limitations of traditional chemical agents. It merges experimental results with simulations to establish detailed material‐process‐microstructure‐performance (MP2) relationships, targeting 5–20% weight reductions. This innovative approach labeled digital lifecycle (DLC) helps accurately predict tensile, flexural, and impact properties based on the foam microstructure, along with experimentally demonstrating improved paintability. The study combines process simulations with finite element models to develop a comprehensive digital model for accurately predicting mechanical properties. Our findings demonstrate a strong correlation between simulated and experimental data, with about a 5% error across various weight reduction targets, marking significant improvements over existing analytical models. This research highlights the efficacy of physical foaming agents in TPO enhancement and emphasizes the importance of integrating experimental and simulation methods to capture the underlying foaming mechanism to establish material‐process‐microstructure‐performance (MP2) relationships. Highlights Establishes a material‐process‐microstructure‐performance (MP2) for TPO foams Sustainably produces TPO foams using supercritical (ScF) N 2 with 20% lightweighting Shows enhanced paintability for TPO foam improved surface aesthetics Digital lifecycle (DLC) that predicts both foam microstructure and properties DLC maps process effects & microstructure onto FEA mesh for precise prediction

Engineering↗
DOE OSTI2023

Ab Initio Modeling on The Thermodynamic and Temperature-Dependent Elastic Properties of Subsystems of The FCC FeNiCoCr Medium Entropy Alloys (MEAs)

The stability, phonon spectra, thermodynamic, and temperature-dependent elasticity of subsystems of the FCC FeNiCoCr MEAs are systematically studied by the ab initio approach. Especially, the quasi-harmonic approximation (QHA) and the innovative Zentropy theory were utilized to predict the thermodynamic properties and elastic properties of FeNi, NiCo, FeNiCo, and FeNiCoCr MEAs with the consideration of magnetic transition. With the ensemble of the partition function based on the multiplicity of each magnetic configuration, the current work successfully reproduced the Curie temperature and the Schottky anomaly of heat capacity of these four MEAs purely based on the ab initio input. Meanwhile, the elastic properties of these alloys at finite temperatures are also successfully predicted with the consideration of magnetic transition. The overall results agree well with the available experimental data and CALPHAD prediction.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Search for 3D topological superconductors using laser-based spectroscopy (Final Technical Report)

The three-dimensional topological superconductor (3D TSC) is a novel quantum phase of matter that is predicted to exhibit exotic thermal and electrical properties, which may serve applications ranging from precision thermal management and sensing technologies to fault-tolerant quantum information processing. Over the past decade, there has been an intensive effort to realize 1D and 2D TSCs by artificially engineering them from combinations of already existing materials. For example, coupling conventional superconductors to spin-orbit coupled 1D nanowires or to 2D topological insulator surfaces by the proximity effect provides a route to realizing 1D and 2D TSCs respectively. However no artificial engineering strategy is available for a 3D TSC. Therefore, current searches are limited to intrinsically superconducting compounds whose topological properties are difficult to predict. The overarching goal of this project was to realize and to identify 3D TSCs in bulk single crystals using novel laser-based spectroscopic techniques. One major thrust was to develop ultralow temperature and ultra-high-resolution angle-resolved photoemission spectroscopy (ARPES) to directly measure the dispersion of Majorana excitations that are predicted to exist on the surfaces of 3D TSCs. Another major thrust was to develop a rational route to realize 3D TSCs by identifying their precursor phases. It is theoretically proposed that the critical fluctuations of certain ordered electronic phases can mediate Cooper pairing in odd-parity channels and lead to topological superconductivity. Therefore, 3D TSCs may potentially emerge upon suppressing these inversion symmetry broken precursor phases to a critical point with external perturbations such as pressure, strain or even light. To test this hypothesis, we studied candidate materials using a suite of symmetry-sensitive optical probes including rotational anisotropy optical second harmonic generation (RA-SHG) and time-resolved coherent phonon spectroscopy, all operable under high-pressure and low temperature environments.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Artificial Intelligence Guided Studies of van der Waals Magnets

A materials informatics framework to explore a large number of candidate van der Waals (vdW) materials is developed. In particular, in this study a large space of monolayer transition metal halides is investigated by combining high-throughput density functional theory calculations and artificial intelligence (AI) to accelerate the discovery of stable materials and the prediction of their magnetic properties. Here, the formation energy is used as a proxy for chemical stability. Semi-supervised learning is harnessed to mitigate the challenges of sparsely labeled materials data in order to improve the performance of AI models. This approach creates avenues for the rapid discovery of chemically stable vdW magnets by leveraging the ability of AI to recognize patterns in data, to learn mathematical representations of materials from data and to predict materials properties. Using this approach, previously unexplored vdW magnetic materials with potential applications in data storage and spintronics are identified.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Final Technical Report

This research project aims to understand the structure and properties of hidden intermediates in amorphous oxides grown by atomic layer deposition (ALD), and apply what is learned to achieve predictive phase and property control in the ALD synthesis of amorphous oxide thin films. The one-year renewal is to further this understanding to understand how amorphous TiO2 thin films by ALD turn into crystalline phase in correlation to the presence of medium range ordering (MRO) and other impurities. This fundamental study is expected to produce a predictive synthesis science for amorphous thin films with substantially improved uniformity and thereby achieve higher stability as a new surface coating strategy used for electrochemical catalysis. In this final technical report, we summarize the key achievements in this project to meet the research goals. This includes (1) a new approach to achieve homogeneous nanometer-scale amorphous coating leading to a new record of lifetime for photoelectrochemical hydrogen fuel generation; (2) A advancement in scanning transmission electron microcopy (STEM) that led to new understanding of intermediate phase in amorphous films; and (3) a computational model for understanding the local structure ordering in amorphous TiO2 films. In addition, this project also yielded relevant technical innovations including a development of bioresorbable zinc primary batteries that can self-degrade after depletion; and a ferroelectric membrane that provides active dendrites suppression to substantially improve the lifetime of rechargeable batteries. Collaboration with Lam Research also yield a new understanding of how built-in strain influences the amorphous films’ quality, bringing direct impacts to semiconductor manufacturing industry.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Commutative Algebra Modeling in Materials Science – A Case Study on Metal–Organic Frameworks (MOFs)

Metal-organic frameworks (MOFs) are a class of important crystalline and highly porous materials whose hierarchical geometry and chemistry hinder interpretable predictions in materials properties. Commutative algebra is a branch of abstract algebra that has been rarely applied in data and material sciences. We introduce the first ever commutative algebra modeling and prediction in materials science. Specifically, category-specific commutative algebra (CSCA) is proposed as a new framework for MOF representation and learning. It integrates element-based categorization with multiscale algebraic invariants to encode both local coordination motifs and global network organization of MOFs. These algebraically consistent, chemically aware representations enable compact, interpretable, and data efficient modeling of MOF properties such as Henry’s constants and uptake capacities for common gases. Compared to traditional geometric and graph-based approaches, CSCA achieves comparable or superior predictive accuracy while substantially improving interpretability and stability across data sets. By aligning commutative algebra with the chemical hierarchy, the CSCA establishes a rigorous and generalizable paradigm for understanding structure and property relationships in porous materials and provides a nonlinear algebra-based framework for data-driven material discovery.

Khaemba, Caleb S.↗
DOE OSTI2023

Long-term variability in immersion-mode marine ice-nucleating particles from climate model simulations and observations

Abstract. Ice-nucleating particles (INPs) in the Southern Ocean (SO) atmosphere have significant impacts on cloud radiative and microphysical properties. Yet, INP prediction skill in climate models remains poorly understood, in part because of the lack of long-term measurements. Here we show, for the first time, how model-simulated INP concentrations compare with year-round INP measurements during the Macquarie Island Cloud Radiation Experiment (MICRE) campaign from 2017–2018. We simulate immersion-mode INP concentrations using the Energy Exascale Earth System Model version 1 (E3SMv1) by combining simulated aerosols with recently developed deterministic INP parameterizations and the native classical nucleation theory (CNT) for mineral dust in E3SMv1. Because MICRE did not collect aerosol measurements of super-micron particles, which are more effective ice nucleators, we evaluate the model's aerosol fields at other high-latitude sites using long-term in situ observations of dust and sea spray aerosol. We find that the model underestimates dust and overestimates sea spray aerosol concentrations by 1 to 2 orders of magnitude for most of the high-latitude sites in the Southern Hemisphere. We next compare predicted INP concentrations with concentrations of INPs collected on filter samples (typically for 2 or 3 d) and processed offline using the Colorado State University ice spectrometer (IS) in immersion freezing mode. We find that when deterministic parameterizations for both dust and sea spray INPs are used, simulated INPs are within a factor of 10 of observed INPs more than 60 % of the time during summer. Our results also indicate that the E3SM's current treatment of mineral dust immersion freezing in the SO is impacted by compensating biases – an underprediction of dust amount was compensated by an overprediction of its effectiveness as INPs. We also perform idealized droplet freezing experiments to quantify the implications of the time-dependent behavior assumed by the E3SM's CNT-parameterization and compare with the ice spectrometer observations. We find that the E3SM CNT 10 s diagnostic used in this study is a reasonable approximation of the exact formulation of CNT, when applied to ice spectrometer measurements in low-INP conditions similar to Macquarie Island. However, the linearized 10 s diagnostic underestimates the exact formula by an order of magnitude or more in places with high-INP conditions like the Sahara. Overall, our findings suggest that it is important to correct the biases in E3SM's simulated dust life cycle and update E3SM's INP parameterizations. INP prediction errors of 2 to 3 orders of magnitude can have considerable impacts on the simulated cloud and radiative properties in global climate models. On comparing INP concentrations during MICRE against ship-based campaigns, Measurements of Aerosols, Radiation, and Clouds over the Southern Ocean (MARCUS) and Antarctic Circumnavigation Expedition (ACE), we find that INPs from the latter are significantly higher only in regions closer to Macquarie Island. This alludes to the fact that physical, chemical and biological processes affecting INP concentrations as stimulated by the island could be partly responsible for the high INP concentrations observed at Macquarie Island during the MICRE campaign. Therefore, improvements to both aerosol simulation and INP parameterizations are required to adequately simulate INPs and their cloud impacts in E3SM. It will be helpful to include a parallel measurement of the size-resolved aerosol composition and explore opportunities for long-term measurement platforms in future field campaigns studying INP sources in remote marine regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Comparing the Expense and Accuracy of Methods to Simulate Atomic Vibrations in Rubrene

Atomic vibrations can inform about materials properties from hole transport in organic semiconductors to correlated disorder in metal–organic frameworks. Currently, there are several methods for predicting these vibrations using simulations, but the accuracy–efficiency tradeoffs have not been examined in depth. In this work, rubrene is used as a model system to predict atomic vibrational properties using six different simulation methods: density functional theory, density functional tight binding, density functional tight binding with a Chebyshev polynomial-based correction, a trained machine learning model, a pretrained machine learning model called ANI-1, and a classical forcefield model. The accuracy of each method is evaluated by comparison to the experimental inelastic neutron scattering spectrum. All methods discussed here show some accuracy across a wide energy region, though the Chebyshev-corrected tight-binding method showed the optimal combination of high accuracy with low expense. We then offer broad simulation guidelines to yield efficient, accurate results for inelastic neutron scattering spectrum prediction.

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

Determination of Molecular Structure and Dynamics of Molten Salts by Advanced Neutron and X-ray Scattering Measurements and Computer Modeling

The design and development of fully functional Molten-Salt Reactors (MSR) require detailed knowledge of the molten salt properties in order to understand and predict the salt’s behavior. Fundamental properties of interest include molecular structure, speciation, and dynamics (such as diffusion coefficients) of salt components and dissolved corrosion and fission products. Computer modeling is necessary to predict changes in physical and chemical properties due to irradiation, burning of dissolved fuel, and corrosion. The modeling requires experimental data, and advanced neutron and x-ray scattering and spectroscopy provide the most reliable and direct determination of the structure (Pair-Distribution Functions, PDF), and dynamics of ions in the melt. This project dealt with both fluoride and chloride salts. The PDFs have been measured by a combination of neutron and x-ray diffraction. We utilized the techniques of isotope substitutions, a very powerful tool available for neutron-scattering, to extract the details of the liquid structure. Although similar measurements have been done before, modern advanced neutron and x-ray-scattering techniques allow collecting the data at much higher resolution and in a wider range of temperatures. Importantly, we were among the first to study fluoride salts by neutron scattering. The importance of impurities and their effects on salt properties have become apparent recently and so new methods of salt purification were developed. We took advantage of these developments to produce reliable data, which have been used for computer simulations of both clean salts and those with added fission and corrosion products most relevant for MSRs. Ab initio molecular dynamics simulations have been performed to understand the multi-component liquid solution, in particular solubility of impurities and thermodynamic interactions in relation to the ionic-cluster structure of the fluid. We applied machine learning to regress from the simulation and experimental data in order to develop a fast-acting model that can handle molten salt with an arbitrary (≥ 10) number of chemical elements and be able to predict chemical potential as a function of composition and temperature. This project resulted in a number of experimental and computer-simulation publications, a patent application, and numerous conference presentations (American Physical Society, American Chemical Society, and The Electrochemical Society among others). Multiple students and postdocs participated and collaborated on aspects of this project. This project seeded new collaborations between MIT and other institutions, such as the University of Massachusetts Lowell, the University of Illinois Urbana-Champaign, the University of California Berkeley, and Oak Ridge and Los Alamos National Labs. As such, this project has had a broad and lasting impact beyond its original scientific scope.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Epitaxial Multifunctional Oxide Heterostructures (DOE Final Technical Report)

The conducting interface that forms between the two band insulators LaAlO 3 (LAO) and SrTiO 3 (STO) continues to attract tremendous attention nearly 15 years after its discovery. Until recently, most of the work on this system has been done in samples grown in the (001) crystal orientation. More recently, interest has focused on samples grown in the (110) and (111) crystal orientations. While the underlying cubic crystal structure is the same, the lattice symmetry at the interface in each case is different, raising the possibility of different properties. In particular, the symmetry of the Ti atoms at the (111) interface is hexagonal, similar to graphene, leading to predictions of topological properties of the band structure. The major part of this proposal is devoted to exploring the properties of (111) LAO/STO interfaces and (111) (La 0.3 Sr 0.7 )(Al 0.65 Ta 0.35 )/STO (LSAT/STO) interfaces, where we have recently discovered interesting new effects.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Ne viii in the Warm-hot Circumgalactic Medium of FIRE Simulations and in Observations

Abstract The properties of warm-hot gas around ∼ L * galaxies can be studied with absorption lines from highly ionized metals. We predict Ne viii column densities from cosmological zoom-in simulations of halos with masses in ∼10 12 and ∼10 13 M ☉ from the Feedback in Realistic Environments (FIRE) project. Ne viii traces the volume-filling, virial-temperature gas in ∼10 12 M ☉ halos. In ∼10 13 M ☉ halos the Ne viii gas is clumpier, and biased toward the cooler part of the warm-hot phase. We compare the simulations to observations from the COS Absorption Survey of Baryon Harbors (or CASBaH) and COS Ultraviolet Baryon Survey (or CUBS). We show that when inferring halo masses from stellar masses to compare simulated and observed halos, it is important to account for the scatter in the stellar-mass–halo-mass relation, especially at M ⋆ ≳ 10 10.5 M ☉ . Median Ne viii columns in the fiducial FIRE-2 model are about as high as observed upper limits allow, while the simulations analyzed do not reproduce the highest observed columns. This suggests that the median Ne viii profiles predicted by the simulations are consistent with observations, but that the simulations may underpredict the scatter. We find similar agreement with analytical models that assume a product of the halo gas fraction and metallicity (relative to solar) ∼0.1, indicating that observations are consistent with plausible circumgalactic medium temperatures, metallicities, and gas masses. Variants of the FIRE simulations with a modified supernova feedback model and/or active galactic nuclei feedback included (as well as some other cosmological simulations from the literature) more systematically underpredict Ne viii columns. The circumgalactic Ne viii observations therefore provide valuable constraints on simulations that otherwise predict realistic galaxy properties.

Wijers, Nastasha A. (ORCID:0000000163747185)↗
DOE OSTI2023

Understanding structure-processing relationships in metal additive manufacturing via featurization of microstructural images

Understanding and predicting accurate property-structure-processing relationships for additively manufactured components is important for both forward and inverse design of robust, reliable parts and assemblies. While direct mapping of process parameters to properties is sometimes plausible, it is often rendered difficult due to poor microstructural control. Exploring the direct relationship between processing conditions and microstructural features can thus provide significant physical insights and aid the overall design process. Here, in this study, we develop an automated high-throughput framework to simulate an uncertainty-aware additive manufacturing (AM) process, characterize microstructural images, and extract meaningful features/descriptors. A kinetic Monte Carlo (KMC) based model of the AM process is used to simulate microstructural evolution for a diverse set of experimentally relevant processing conditions. We perform a parametric study to explore the relationship between microstructural features and processing conditions. Our results indicate that a many-to-one mapping can exist between processing conditions and typical descriptors; therefore, multiple descriptors are thus necessary to unambiguously represent microstructural images. Our work provides crucial quantitative and qualitative in-formation that would aid in the selection of features for microstructural images. Featurized microstructures could then be utilized to build data-driven models for predictive control of microstructures and thereby properties of additively manufactured components.

36 MATERIALS SCIENCE↗
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↗
DOE OSTI2022

Deep learning the properties of inorganic perovskites

We report the ability to accurately and quickly predict the stability of materials and their structural and electronic properties remains a grand challenge in materials science. Density functional theory is widely used as a means of predicting these material properties, but is known to be computationally expensive and scales as the cube of the number of electrons in the material’s unit cell. In this article, for a previously published dataset of inorganic perovskites, we show that a single neural network model using only the elemental properties of the compounds’ constituents can predict lattice constants to within 0.1 Å, heat of formation to within 0.2 eV, and band gaps to within 0.7 eV RMSE. We also compare the performance of the trained network to two widely used regression techniques, namely random forest and Kernel ridge regression, and find that the neural network’s predictions are more accurate for each of the properties. The simultaneous accurate prediction of multiple key properties of technologically relevant materials is promising for rational design and optimization in known and novel chemical spaces.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Effect of stoichiometry and hydration level on water domain size and transport in poly(aryl piperidinium) alkaline anion-exchange membranes

Alkaline water electrolysis holds promise in decarbonizing the global economy by enabling renewable hydrogen production with non-precious group metal catalysts. Anion exchange membranes are an important component of alkaline water electrolyzers and would ideally be durable while allowing for high hydroxide conductivity. The poly(aryl piperidinium) (PAP) class of polymers has attracted recent interest due to their good mechanical robustness and high ionic conductivity. Here, in this work, we perform atomistic molecular dynamics (MD) simulations of several PAP polymers at experimentally relevant hydration levels and polymer ion exchange capacities (IECs) to gain nanoscale insight into their properties and to help elucidate the trade-offs that result from tuning the IECs through the polymer stoichiometry. Our MD-predicted macroscopic polymer properties were found to be in good agreement with experimentally available polymer swelling ratios, water-occupied volumes, X-ray scattering, and ionic conductivities. The models show that for hydration levels greater than 8H 2 O per cation a single water cluster will form that percolates through the system. The growth in water cluster size results in large polymer swelling, the creation of larger channels with widths of 7 Å or larger, and nanophase separation between the hydrophilic domains and the polymer with characteristic length scales of approximately 20–30 Å. The experimentally observed lack of a strong X-ray scattering peak at low wavevectors can be explained by a cancellation between the polymer-polymer/water-water and polymer-water correlations and not a loss in nanophase separation. The overlap in coordination environments of the hydroxide oxygen and polymer nitrogen atoms implies that vehicular diffusion between cationic groups could play a role in hydroxide transport. The polymers' hydroxide and water diffusion constants increase by approximately an order of magnitude between hydration levels of 8 and 20H 2 O per cation. However, there are diminishing returns in hydroxide diffusion constant once the IEC exceeds 2.4 meq/g.

08 HYDROGEN↗