Transport Mechanisms Underlying Ionic Conductivity in Nanoparticle-Based Single-Ion Electrolytes

Kadulkar, Sanket; Milliron, Delia J.; Truskett, Thomas M.; Ganesan, Venkat

doi:10.1021/acs.jpclett.0c01937

Cited by 11 publications

(27 citation statements)

References 35 publications

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“…For example, more recently, a similar method has been applied to study ion transport in nanoparticle-based single-ion conductors. 115 Other dimensions in the design space of salt-doped polymers were explored by our group with a different approach to consider the strong ion to polymer interactions on a coarse-grained level. 54,116,117 Specifically, our CG model contains a 1/r 4 potential form, the same as that of an ion to an induced dipole, to generically represent ion size and dielectric dependent ion solvation.…”

Section: Homopolymer Electrolytesmentioning

confidence: 99%

“…They showed that as the polarity increases, a competition between better ion dissociation (smaller ion clusters and improved degree of uncorrelated ion motion) and slowed polymer dynamics arises, causing a nonmonotonic trend in total conductivity. , Such a CG simulation approach can be easily applied to other ion-containing polymer systems of interest. For example, more recently, a similar method has been applied to study ion transport in nanoparticle-based single-ion conductors …”

Section: Recent Applications and Advancesmentioning

confidence: 99%

“…Besides polyelectrolyte solutions, generic CG MD simulations are also commonly applied to study denser systems of polyelectrolyte complexes (polyelectrolyte systems with oppositely charged chains) − and polyelectrolyte gels. ,− In particular, such a simple CG model with explicit solvents was used in Gibbs ensemble simulations (in which two systems were simulated in MD to consider the two phases but with MC exchange moves used between to keep them in equilibrium) and reproduced experimental phase behavior of polyelectrolyte complex coacervates . Another recent study probed the effects of chain lengths on the dynamics of polyelectrolyte complex coacervates (the polyelectrolyte-rich liquid phase existing in equilibrium with more dilute solution) using generic CG MD .…”

Section: Recent Applications and Advancesmentioning

confidence: 99%

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Molecular Dynamics Simulations of Ion-Containing Polymers Using Generic Coarse-Grained Models

2021

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Molecular dynamics simulations with generic bead–spring models have been instrumental in revealing the molecular-scale behavior that underlies structure–property relationships of various types of polymeric systems. The generic, coarse-grained modeling approach does not include atomistic detail and is not focused on quantitatively matching the properties of a particular chemical system, though typically several parameters are included that can be tuned to consider different types of chemistries. Besides allowing access to longer length and time scales due to computational efficiency, this type of approach is advantageous in that the physical insight gained is often relevant across the entire class of related materials. The connectivity and number of beads in a generic bead–spring models can be adjusted in an obvious manner to describe the most basic homopolymer features (e.g., to consider different chain lengths and linear versus branched architectures). In uncharged copolymers or solvent-containing systems, the chemical interactions between components are considered by adjusting the strength of the various pairwise interparticle potentials (usually without changing their form). However, ions can have stronger and longer-ranged interactions with each other, with solvents, and with monomers that can require additional complexity be added to appropriately describe relevant phenomena in ion-containing polymeric systems. Recent efforts are pushing the boundaries of the generic coarse-grained approach by including additional ion–ion or ion–polymer interactions to more closely capture and analyze such phenomena, while still avoiding detailed and specific empirical adjustments that would make the model only apply to a single chemical system. In this perspective, we highlight a variety of recent molecular dynamics work that applies generic coarse-grained models to study ion-containing polymeric materials. We also discuss possible future directions and challenges of the generic coarse-graining approach in this area.

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Section: Homopolymer Electrolytesmentioning

confidence: 99%

Section: Recent Applications and Advancesmentioning

confidence: 99%

Section: Recent Applications and Advancesmentioning

confidence: 99%

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Molecular Dynamics Simulations of Ion-Containing Polymers Using Generic Coarse-Grained Models

2021

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“…[ 30 ] This is because the aggregation‐induced gelation effect of the porous LiF nanobox results in largely enhanced macroscopic viscosity but has a little influence on the microscopic viscosity of the prepared electrolyte. In the nanoparticle‐based electrolyte, [ 31 ] the microscopic viscosity is the dominant parameter to determine the ion diffusion coefficient according to the Stokes‐Einstein equation and further correlate to the ionic conductivity based on the Nernst‐Einstein equation. Therefore, the ion diffusion coefficient and ionic conductivity in our porous LiF nanobox based electrolyte are not greatly influenced by the concentration of porous LiF nanoboxes as that of the tested macroscopic viscosity (Note S1, Supporting Information).…”

Section: Resultsmentioning

confidence: 99%

Lithium Fluoride in Electrolyte for Stable and Safe Lithium‐Metal Batteries

et al. 2021

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Electrolyte engineering via fluorinated additives is promising to improve cycling stability and safety of high‐energy Li‐metal batteries. Here, an electrolyte is reported in a porous lithium fluoride (LiF) strategy to enable efficient carbonate electrolyte engineering for stable and safe Li‐metal batteries. Unlike traditionally engineered electrolytes, the prepared electrolyte in the porous LiF nanobox exhibits nonflammability and high electrochemical performance owing to strong interactions between the electrolyte solvent molecules and numerous exposed active LiF (111) crystal planes. Via cryogenic transmission electron microscopy and X‐ray photoelectron spectroscopy depth analysis, it is revealed that the electrolyte in active porous LiF nanobox involves the formation of a high‐fluorine‐content (>30%) solid electrolyte interphase layer, which enables very stable Li‐metal anode cycling over one thousand cycles under high current density (4 mA cm−2). More importantly, employing the porous LiF nanobox engineered electrolyte, a Li || LiNi0.8Co0.1Mn0.1O2 pouch cell is achieved with a specific energy of 380 Wh kg−1 for stable cycling over 80 cycles, representing the excellent performance of the Li‐metal pouch cell using practical carbonate electrolyte. This study provides a new electrolyte engineering strategy for stable and safe Li‐metal batteries.

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“…In this study, we demonstrate the above strategy by the application of deep learning approaches for predicting the diffusion coefficient of cations in single-ion conducting nanoparticle-based electrolytes. The choice of the system was inspired by a recent study of ours probing the mechanistic bases for ion transport observations reported in these electrolytes. Our results in such a context were consistent with the reported experimental behavior of electrolytes comprising silica nanoparticles, cofunctionalized with poly(ethylene glycol) ligands and tethered anionic species coupled to Li + ions, dispersed in ion-conducting tetraglyme solvent. , Our simulation results suggested that ionic conductivity in such systems is primarily controlled by cation diffusion along functionalized nanoparticle surfaces.…”

Section: Introductionmentioning

confidence: 99%

Prediction and Optimization of Ion Transport Characteristics in Nanoparticle-Based Electrolytes Using Convolutional Neural Networks

et al. 2021

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We develop a convolutional neural network (CNN) model to predict the diffusivity of cations in nanoparticle-based electrolytes, and use it to identify the characteristics of morphologies which exhibit optimal transport properties. The ground truth data is obtained from kinetic Monte Carlo (kMC) simulations of cation transport parameterized using a multiscale modeling strategy. We implement deep learning approaches to quantitatively link the diffusivity of cations to the spatial arrangement of the nanoparticles. We then integrate the trained CNN model with a topology optimization algorithm for accelerated discovery of nanoparticle morphologies that exhibit optimal cation diffusivities at a specified nanoparticle loading, and we investigate the ability of the CNN model to quantitatively account for the influence of interparticle spatial correlations on cation diffusivity. Finally, using data-driven approaches, we explore how simple descriptors of nanoparticle morphology correlate with cation diffusivity, thus providing a physical rationale for the observed optimal microstructures. The results of this study highlight the capability of CNNs to serve as surrogate models for structure--property relationships in composites with monodisperse spherical particles, which can in turn be used with inverse methods to discover morphologies that produce optimal target properties. File list (2)download file view on ChemRxiv Prediction and optimization of ion transport characteristics ... (5.51 MiB) download file view on ChemRxiv Supplementary Information.pdf (2.33 MiB)

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Transport Mechanisms Underlying Ionic Conductivity in Nanoparticle-Based Single-Ion Electrolytes

Cited by 11 publications

References 35 publications

Molecular Dynamics Simulations of Ion-Containing Polymers Using Generic Coarse-Grained Models

Molecular Dynamics Simulations of Ion-Containing Polymers Using Generic Coarse-Grained Models

Lithium Fluoride in Electrolyte for Stable and Safe Lithium‐Metal Batteries

Prediction and Optimization of Ion Transport Characteristics in Nanoparticle-Based Electrolytes Using Convolutional Neural Networks

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