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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

The active CGCG 077-102 NED02 galaxy within the Abell 2063 galaxy cluster

Context.Within the framework of investigating the link between the central super massive black holes in the cores of galaxies and the galaxies themselves, we detected a variable X-ray source in the center of CGCG 077-102 NED02, which is a member of the CGCG 077-102 galaxy pair within the Abell 2063 cluster of galaxies. Aims.Our goal is to combine X-ray and optical data to demonstrate that this object harbors an active super massive black hole in its core, and to relate this to the dynamical status of the galaxy pair within the Abell 2063 cluster. Methods.We usedChandraandXMM-Newtonarchival data to derive the X-ray spectral shape and variability. We also obtained optical spectroscopy to detect the expected emission lines that are typically found in active galactic nuclei. Finally, we used public ZTF imaging data to investigate the optical variability. Results.There is no evidence of multiple X-ray sources or extended components within CGCG 077-102 NED02. Single X-ray spectral models fit the source well. We detect significant, nonrandom inter-observation 0.5–10 keV X-ray flux variabilities, for observations separated by ∼4 days for short-term variations and by up to ∼700 days for long-term variations. Optical spectroscopy points toward a passive galaxy for CGCG 077-102 NED01 and a Seyfert for CGCG 077-102 NED02. The classification of CGCG 077-102 NED02 is also consistent with its X-ray luminosity of over 10 42 erg s −1 . We do not detect short-term variability in the optical ZTF light curves. However, we find a significant long-term stochastic variability in theg-band that can be well described by the damped random walk model with a best-fit characteristic damping timescale ofτ DRW = 30 −12 +28 days. Finally, the CGCG 077-102 galaxy pair is deeply embedded within the Abell 2063 potential, with a long enough history within this massive structure to have been affected by the influence of this cluster for a long time. Conclusions.Our observations point toward a moderately massive black hole in the center of CGCG 077-102 NED02 of ∼10 6 M ⊙ . As compared to another similar pair in the literature, CGCG 077-102 NED02 is not heavily obscured, perhaps because of the surrounding intracluster medium ram-pressure stripping.

Astronomy & Astrophysics↗
DOE OSTI2025

Unique Conductivity Behavior in Water-In-Salt Electrolytes Driven by Ion Clusters

Understanding and predicting ion transport in aqueous electrolytes are crucial for advanced energy storage and biophysics, and many emergent technologies yet remain elusive. Herein, we introduce a unified framework to quantitatively describe and predict electrolyte conductivity that shifts from conventional molar concentration-based metrics to a volume fraction-based approach. Through analyzing a variety of electrolyte solutions via this perspective, we observe a universal conductivity peak at a 37% volume fraction. Small-angle X-ray scattering (SAXS) and molecular dynamics (MD) simulations reveal that nanometer-scale ion clusters drive this general behavior. Moreover, key geometric features of the ion transport pathwayssuch as pore size, tortuosity, and connectivityfollow a consistent dependence with respect to the volume fraction, reinforcing the argument for the universal conductivity trend. This paradigm shift opens new avenues for designing high-performance electrolytes and provides transformative insights for advancing studies in many fields, wherein molecular aggregates dictate transport properties.

Nguyen, Huong T. D.↗
DOE OSTI2024

Simulating water dynamics related to pedogenesis across space and time: Implications for four-dimensional digital soil mapping

Digital soil mapping (DSM) relies on machine-learning and geostatistics to represent soil property observations across space. DSM techniques are powerful but often empirical, being limited to the quality and density of point samples. Water dynamics are closely related to soil variability, and the physics that govern water movement are well known. Hydrological properties can hence be simulated by physical models through space and time, unveiling key characteristics about soils. We propose the use of hydrologic models to map soils across the surface (2D), depth (1D), and time (1D)–which provides a 4D approach to digital soil mapping (4DSM). The Distributed Hydrology Soil Vegetation Model (DHSVM) was applied to a watershed currently under pasture. Moisture sensors and wells were installed at different depths in the watershed on summit, sideslope and toeslope positions to validate the model. DHSVM simulations of soil moisture distribution and depth to saturation were performed during the hydrological year (October 2008-September 2009). Clusters of similar pixels based on soil moisture values were determined using Dynamic Time Warping (DTW) to align temporal data and K-means. Clustering was performed both seasonally and for the entire year. Temporal patterns simulated by DHSVM matched measurements given by moisture sensors and wells. Seasonal clusters differed from the annual cluster. Distinct clusters were observed for each season and with depth, showing that spatiotemporal soil variability is lost when statically assessing soils. Spatiotemporal clusters corroborated field observations of fragipan occurrence not explicitly spatially mapped by Soil Survey Geographic Database (SSURGO). If a connection can be made between water and soils, static and dynamic soil variability can be predicted using physically based hydrologic models. Hydrologic models can benefit soil mapping by enabling reliable 4D simulation of water dynamics, which are fundamental to soil variability and soil classification and directly relate to biological, physical and chemical soil processes not captured by typical soil sampling protocols.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Nature of molybdenum carbide surfaces for catalytic hydrogen dissociation using machine-learned potentials: an ensemble-averaged perspective

Molybdenum carbides with an electronic structure similar to noble metals have gained attention as a promising low-cost catalyst for biomass valorization and the hydrogen evolution reaction. However, our fundamental understanding of the catalyst surface and how different phases of these catalysts behave at varying reaction conditions is limited to ground state density functional theory calculations as ab initio molecular dynamics (AIMD) is computationally prohibitive at relevant length and time scales. Here, in this work, we train a multi-atomic cluster expansion (MACE) machine-learned interatomic potentials (MLIP) to study hydrogen dissociation and dynamics over Mo, δ-MoC, α-Mo 2 C, and β-Mo 2 C surfaces at varying temperatures and hydrogen partial pressures. Our simulations identify unique and different molecular and atomic hydrogen adsorption sites on different surfaces that do not depend on the temperature. At low hydrogen pressures, the surface coverage is monolayer, which transitions to two-layer adsorption at higher pressures. We find that atomic hydrogen diffusion and recombinations are preferred over molybdenum atom hollow sites, while the diffusion over carbon-terminated facets was negligible, signifying particularly strong C–H interactions. In contrast, molecular hydrogen adsorption occurs mostly atop Mo or the bridging sites. At a comparable hydrogen loading, β-Mo 2 C (001) is the most active surface for hydrogen dissociation reaction. This work provides insights into the dynamic nature of the hydrogen dissociation chemistry and the diversity of hydrogen adsorption sites on molybdenum carbides.

08 HYDROGEN↗
DOE OSTI2025

Web-based wide-area monitoring platform for ringdown and clustering analytics in power systems

This paper introduces an open-source research platform for monitoring the Mexican interconnected power grid, allowing real-time processing and information extraction of the grid’s dynamic condition. Moreover, the platform is a Python-based development that embeds different ringdown and clustering analytics tools. In the case of ringdown analysis, the modal information can be extracted using some of the most known algorithms, i.e., Prony analysis, eigensystem realization algorithm (ERA), and matrix pencil (MP). For clustering analysis, the coherent behaviour of generator and non-generator buses is provided by applying recent state-of-the-art techniques such as affinity propagation, K-means, hierarchical agglomerative clustering, and typicality data analysis. The results of up to 93 PMUs show that this open-source platform suits researchers’ and engineers’ power system dynamic analysis requirements.

Clustering↗
DOE OSTI2026

Dehydration-Driven Ion Aggregation and the Onset of Gelation in ZnCl2 Solution

A minimal model of ionic aggregation in concentrated ZnCl2 is developed, guided by molecular dynamics simulations with a machine-learned potential. It explicitly incorporates solvent-site depletion, correlated chloride binding, and allows for loops within Zn–Cl clusters. Dehydration drives two coordination-controlled structural changes: a crossover at Z=2 from predominantly isolated Zn-containing species to Cl-bridged clusters and the onset of gelation near Z≈3. The model reproduces the concentration-dependent trends observed in the molecular dynamics simulations with two fitted parameters, while the cluster-size distribution at the highest concentration is consistent with three-dimensional percolation scaling over the accessible range of cluster sizes.

25 ENERGY STORAGE↗
DOE OSTI2024

Computational multiphysics modeling of radioactive aerosol deposition in diverse human respiratory tract geometries

The evaluation of aerosol exposure relies on generic mathematical models that assume uniform particle deposition profiles over the human respiratory tract and do not account for subject-specific characteristics. Here we introduce a hybrid-automated computational workflow that generates personalized particle deposition profiles in 3D reconstructed human airways from computed tomography scans using Computational Fluid and Particle Dynamics simulations. This is the first large-scale study to consider realistic airways variability, where 380 lower and 40 upper human respiratory tract 3D geometries are reconstructed and parameterized. The data is clustered into nine groups using random forest regression. Computational fluid and particle dynamics simulations are conducted on these representative geometries using a realistic heavy-breathing respiratory cycle and radioactive iodine-131 as a source term. Monte Carlo radiation transport simulations are performed to obtain detailed energy deposition maps. Our findings emphasize the importance of personalized studies, as minor respiratory tract variations notably influence deposition patterns rather than global parameters of the lower airways, observing more than 30% variance in the mass deposition fraction.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2025

Many-body Nuclear Dynamics

The principal goal of this work was to better understand the fusion of neutron-rich nuclei a topic relevant to the fields of both nuclear physics and nuclear astrophysics. The work provides insight into the structure and reactions of neutron-rich nuclei namely the extent of their neutron density distribution and its polarizability. By comparing the fusion excitation functions for a chain of isotopes with a common target nucleus changes in the attractive nuclear potential are assessed. This change in the attractive potential is related to changes in the neutron density distribution with increasing number of neutrons or changes in fusion dynamics with increasing neutron number. The impact of the pairing of valence neutrons and protons on the fusion cross-section is also examined. Measurement of an isotopic chain is a powerful tool to address this topic. The experimental program made use of several different accelerator facilities. The core of the experimental work involved experiments at the ReA3 accelerator and the Facility for Rare Isotope Beams (FRIB) situated at Michigan State University, at GANIL, the French national nuclear physics laboratory, and the University of Notre Dame. At ReA3 the degree to which α-clusters associated with fusion of 28,30,32 Si + 28 Si result from the collision dynamics or reflect an initial α-cluster structure was explored. Alpha clusters observed in fusion reactions exceed the predictions of the standard statistical model. This experiment provides a measure of how clusterization is impacted by the increasing neutron-richess of the system. In the experiment at GANIL we investigated fusion in 19 O + 12 C and 20 O + 12 C. This experiment utilized the recently developed active-target detector MuSIC@Indiana. Using this proven, highly efficient, active-target detector we measured the fusion excitation function for these neutron-rich systems. Grounded by these experimental measurements, theoretical models were used to examine the role of unpaired valence neutrons on the fusion cross-section at energies just above the fusion barrier.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

DONKEY: A Flexible and Accurate Algorithm for Clustering

We propose an accurate clustering algorithm suitable for the varied and multidimensional data sets that correspond to temporal snapshots from on-the-fly nonadiabatic trajectory-based simulations of photoexcited dynamics. The algorithm approximates the underlying probability density function using variable kernel density estimation, with local maxima corresponding to cluster centers. Each data point is then assigned to one of the maxima by employing a maximization procedure. Finally, clusters artificially separated by minor fluctuations in the probability density are merged. The algorithm does not require parameter tuning, which ensures flexibility and reduces the risk of bias. It is tested on several synthetic data sets, where it consistently outperforms conventional clustering algorithms. As a final example, the algorithm is applied to the excited dynamics of the norbornadiene ⇌ quadricyclane (C 7 H 8 ) molecular photoswitch, demonstrating how distinct reaction pathways can be identified.

algorithms↗
DOE OSTI2024

Dynamic Transformation of High-Architectural Nanocrystal Superlattices upon Solvent Molecule Exposure

The cluster-based body-centered-cubic superlattice (cBCC SL) represents one of the most complicated structures among reported nanocrystal assemblies, comprised of 72 truncated tetrahedral quantum dots per unit cell. Our previous report revealed that truncated tetrahedral quantum dots within cBCC SLs possessed highly controlled translational and orientational order owing to an unusual energetic landscape based on the balancing of entropic and enthalpic contributions during the assembly process. However, the cBCC SL’s structural transformability and mechanical properties, uniquely originating from such complicated nanostructures, have yet to be investigated. Herein, we report that cBCC SLs can undergo dynamic transformation to face-centered-cubic SLs in response to post-assembly molecular exposure. We monitored the dynamic transformation process using in situ synchrotron-based small-angle X-ray scattering, revealing a dynamic transformation involving multiple steps underpinned by interactions between incoming molecules and TTQDs’ surface ligands. Furthermore, our mechanistic study demonstrated that the precise configuration of TTQDs’ ligand molecules in cBCC SLs was key to their high structural transformability and unique jelly-like soft mechanical properties. While ligand molecular configurations in nanocrystal SLs are often considered minor features, our findings emphasize their significance in controlling weak van der Waals interactions between nanocrystals within assembled SLs, leading to previously unremarked superstructural transformability and unique mechanical properties. Finally, our findings promote a facile route toward further creation of soft materials, nanorobotics, and out-of-equilibrium assemblies based on nanocrystal building blocks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Cyclic phase transition of substrate-modulated two-dimensional dusty plasma driven by gyroscopic forces

Langevin dynamical simulations are performed to investigate the formation of clusters and voids of a two-dimensional periodic substrate (2DPS)-modulated two-dimensional dusty plasma (2DDP) driven by a gyroscopic force. It is discovered that, as the frequency of the gyroscopic force decreases gradually, the substrate-modulated 2DDP undergoes the cyclic transition between the ordered cluster and void phases. Between the observed ordered cluster and void phases, the studied 2DDP exhibits a more uniform arrangement of particles. The discovered cyclic transition is attributed to the symmetry of the time-averaged potential landscape induced by the 2DPS in the reference frame of the moving particle, as confirmed by superimposing the particle locations on the effective potential landscape under various conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Multiscale Physics of Atomic Nuclei from First Principles

Atomic nuclei exhibit multiple energy scales ranging from hundreds of MeV in binding energies to fractions of an MeV for low-lying collective excitations. As the limits of nuclear binding are approached near the neutron and proton drip lines, traditional shell structure starts to melt with an onset of deformation and an emergence of coexisting shapes. It is a long-standing challenge to describe this multiscale physics starting from nuclear forces with roots in quantum chromodynamics. Here, we achieve this within a unified and nonperturbative quantum many-body framework that captures both short- and long-range correlations starting from modern nucleon-nucleon and three-nucleon forces from chiral effective field theory. The short-range (dynamic) correlations which account for the bulk of the binding energy are included within a symmetry-breaking framework, while long-range (static) correlations (and fine details about the collective structure) are included by employing symmetry projection techniques. Our calculations accurately reproduce—within theoretical error bars—available experimental data for low-lying collective states and the electromagnetic quadrupole transitions in 20−30 Ne. In addition, we reveal coexisting spherical and deformed shapes in 30 Ne, which indicates the breakdown of the magic neutron number 𝑁 = 20 as the key nucleus 28 O is approached, and we predict that the drip line nuclei 32,34 Ne are strongly deformed and collective. By developing reduced-order models for symmetry-projected states, we perform a global sensitivity analysis and find that the subleading singlet 𝑆-wave contact and a pion-nucleon coupling strongly impact nuclear deformation in chiral effective field theory. The techniques developed in this work clarify how microscopic nuclear forces generate the multiscale physics of nuclei spanning collective phenomena as well as short-range correlations and allow one to capture emergent and dynamical phenomena in finite fermion systems such as atom clusters, molecules, and atomic nuclei.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Structural origin of disorder-induced ion conduction in NaFePO 4 cathode materials

Diffusion in NaFePO 4 can be enhanced through amorphization. Based on computations using DFT and machine learning potentials, we ascribe this phenomenon to the formation of less constrained Na-ion environments upon disordering. Most modern battery technologies depend on solid-state crystalline cathode materials. However, some of these materials are constrained by the low ionic conductivity of their most stable phases. An example of this is maricite (NaFePO 4 ). Interestingly, experiments have shown that maricite can improve its rate capability through disordering (amorphization). However, experimental characterization of amorphous cathode materials remains a major challenge, hindering a clear understanding of the structural origin of the disorder-induced improvement in sodium-ion mobility. To address this, we here employ molecular dynamics simulations by first training a machine learning potential for NaFePO 4 based on the atomic cluster expansion approach and a batch active learning potential parameterization scheme. This potential is then applied to explore the structural and dynamical properties of NaFePO 4 glasses as cathode materials. Specifically, we investigate the effect of glass structure on sodium-ion diffusion, revealing the relative influences of short-range and medium-range order features. We find significant heterogeneity in sodium-ion diffusivity in the glass, with fast-conducting ions residing in less constrained atomic environments with fewer P and Fe neighbors. These more mobile ions are also surrounded by larger ring-type structures. Overall, the results and developed approach present promising avenues for developing high-performance glassy cathodes for next-generation batteries.

Christensen, Rasmus↗
DOE OSTI2025

SZ–X-Ray Surface Brightness Fluctuations in the SPT-XMM Clusters

The hot plasma in galaxy clusters, the intracluster medium, is expected to be shaped by subsonic turbulent motions, which are key for heating, cooling, and transport mechanisms. The turbulent motions contribute to the nonthermal pressure, which, if not accounted for, consequently imparts a hydrostatic mass bias. Accessing information about turbulent motions is thus of major astrophysical and cosmological interest. Characteristics of turbulent motions can be indirectly accessed through surface brightness fluctuations. This study expands on our pilot investigations of surface brightness fluctuations in the Sunyaev–Zel’dovich and in X-ray data by examining, for the first time, a large sample of 60 clusters using both SPT-SZ and XMM-Newton data and spans the redshift range 0.2 < z < 1.5, thus constraining the respective pressure and density fluctuations within 0.6 R $_{500}$. We deem density fluctuations to be of sufficient quality for 32 clusters, finding mild correlations between the peak of the amplitude spectra of density fluctuations and various dynamical parameters. We infer turbulent velocities from density fluctuations with an average Mach number $\mathcal{M}$ 3D = 0.52 ± 0.14, in agreement with numerical simulations. For clusters with inferred turbulent Mach numbers from fluctuations in both pressure, $\mathcal{M}$ P , and density, $\mathcal{M}$ ρ , we find broad agreement between $\mathcal{M}$ P and $\mathcal{M}$ ρ . Our results suggest either a bimodal or a skewed unimodal Mach number distribution, with the majority of clusters being turbulence-dominated (subsonic) while the remainder are shock-dominated (supersonic).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

XRISM/Resolve view of Abell 2319: Turbulence, sloshing, and ICM dynamics

Here, we present results from XRISM/Resolve observations of the core of the galaxy cluster Abell 2319, focusing on its kinematic properties. The intracluster medium (ICM) exhibits temperatures of approximately 8 keV across the core, with a prominent cold front and a high-temperature region (⁠~11 keV) in the north-west. The average gas velocity in the 3' x 4' region around the brightest cluster galaxy (BCG) covered by two Resolve pointings is consistent with that of the BCG to within 40 km s -1 ⁠ and we found modest average velocity dispersion of 230–250 km s -1 ⁠. On the other hand, spatially resolved spectroscopy reveals interesting variations. A blueshift of up to ~230 km s -1 ⁠ is observed around the east edge of the cold front, where the gas with the lowest specific entropy is found. The region further south inside the cold front shows only a small velocity difference from the BCG; however, its velocity dispersion is enhanced to ~400 km s -1 ⁠, implying the development of turbulence. These characteristics indicate that we are observing sloshing motion with some inclination angle following BCG and that gas phases with different specific entropy participate in sloshing with their own velocities, as expected from simulations. No significant evidence for a high-redshift ICM component associated with the subcluster Abell 2319B was found in the region covered by the current Resolve pointings. These results highlight the importance of sloshing and turbulence in shaping the internal structure of Abell 2319. Further deep observations are necessary to better understand the mixing and turbulent processes within the cluster.

Astronomy and AstroPhysics↗
DOE OSTI2025

Identifying High Ionic Conductivity Compositions of Ionic Liquid Electrolytes Using Features of the Solvation Environment

Binary mixtures of ionic liquids with molecular solvents are gaining interest in electrochemical applications due to the improvement in their performance over neat ionic liquids. Dilution with suitable molecular solvents can reduce the viscosity and facilitate faster diffusion of ions, thereby yielding substantially higher ionic conductivity than that for a pure ionic liquid. Although viscosity and diffusion coefficients typically behave as monotonic functions of concentration, ionic conductivity often passes through a peak value at an optimum molar ratio of the molecular solvent to the ionic liquid. The ionic conductivity maximum is generally explained in terms of a balance between the ease of charge transport and the concentration of the charge carriers. In this work, fluctuation in the local environment surrounding an ion is invoked as a plausible explanation for the ionic conductivity mechanism with a binary mixture of 1-ethyl-3-methylimidazolium tetrafluoroborate and ethylene glycol as an example. The magnitude of the dynamism in the local environment is captured by measuring the spatial and temporal features of the solvation environment. Standard deviation in the number of ions in the solvation environment serves as a spatial feature, while the cage correlation lifetimes for oppositely charged ions within the first solvation shell serve as a temporal feature. Large standard deviations in the cluster ion population and short cage correlation lifetimes are indicators of highly dynamic ionic environment at the molecular level and consequently yield high ionic conductivity. Such compositions were found to be in good agreement with the optimum ionic liquid mole fractions obtained through experimental measurement. Short cage correlation lifetimes enable the identification of optimum mixture compositions using simulation trajectories significantly shorter than those required to implement the Nernst–Einstein or Einstein formalisms for calculating ionic conductivity. We validated the applicability of this approach across force fields and in six ionic liquid-molecular solvent electrolytes formed with combination of cations, anions, and solvents. We offer a computationally efficient approach of screening ionic liquid-molecular solvent binary mixture electrolytes to identify molar ratios that yield high ionic conductivity.

25 ENERGY STORAGE↗
DOE OSTI2024

Permutation-adapted complete and independent basis for atomic cluster expansion descriptors

Atomic cluster expansion (ACE) methods provide a systematic way to describe particle local environments of arbitrary body order. For practical applications it is often required that the basis of cluster functions be symmetrized with respect to rotations and permutations. Existing methodologies yield sets of symmetrized functions that are over-complete. These methodologies thus require an additional numerical procedure, such as singular value decomposition (SVD), to eliminate redundant functions. In this work, it is shown that analytical linear relationships for subsets of cluster functions may be derived using recursion and permutation properties of generalized Wigner symbols. From these relationships, subsets (blocks) of cluster functions can be selected such that, within each block, functions are guaranteed to be linearly independent. It is conjectured that this block-wise independent set of permutation-adapted rotation and permutation invariant (PA-RPI) functions forms a complete, independent basis for ACE. Along with the first analytical proofs of block-wise linear dependence of ACE cluster functions and other theoretical arguments, numerical results are offered to demonstrate this. The utility of the method is demonstrated in the development of an ACE interatomic potential for tantalum. Using the new basis functions in combination with Bayesian compressive sensing sparse regression, some high degree descriptors are observed to persist and help achieve high-accuracy models.

Angular momentum↗
DOE OSTI2024

Cellulose acetate membranes exhibit exceptional monovalent to divalent cation selectivities

Salt transport properties of cellulose acetate membranes are reported for a series of chloride salts with mono- valent and divalent cations (LiCl, NaCl, MgCl 2 , CaCl 2 ). Measurements include salt permeability and sorption, with diffusivity values calculated from the permeability and sorption results. We report an exceptionally high LiCl/MgCl 2 selectivity of 750:1. Salts with similar valence (LiCl and NaCl; MgCl 2 and CaCl 2 ) have similar transport properties. The high monovalent/divalent selectivity arises from differences in both sorption and diffusion, with a LiCl/MgCl 2 solubility selectivity of about 11 and a diffusivity selectivity of about 70. Atomistic molecular dynamics simulations show that ions tend to reside in isolated clusters of water. Increasing ion charge strengthens ion–water interactions relative to ion–polymer interactions, explaining the reduced sorption of divalent ions. Diffusion of ions through the membrane occurs via hop-like motion between water clusters. Lithium diffuses faster than magnesium due to weaker ion–water coordination for lithium, which allows for greater mobility within water clusters and more frequent hopping. Altogether, our atomistic simulations suggest that the high LiCl/MgCl 2 selectivity is linked to cellulose acetate’s high water/salt selectivity and is a consequence of low water content and relatively uniform water distribution.

36 MATERIALS SCIENCE↗