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

Multiscale modeling of solute diffusion in triblock copolymer membranes

We develop a multiscale simulation model for diffusion of solutes through porous triblock copolymer membranes. The approach combines two techniques: self-consistent field theory (SCFT) to predict the structure of the self-assembled, solvated membrane and on-lattice kinetic Monte Carlo (kMC) simulations to model diffusion of solutes. Solvation is simulated in SCFT by constraining the glassy membrane matrix while relaxing the brush-like membrane pore coating against the solvent. The kMC simulations capture the resulting solute spatial distribution and concentration-dependent local diffusivity in the polymer-coated pores; we parameterize the latter using particle-based simulations. We apply our approach to simulate solute diffusion through nonequilibrium morphologies of a model triblock copolymer, and we correlate diffusivity with structural descriptors of the morphologies. We also compare the model's predictions to alternative approaches based on simple lattice random walks and find our multiscale model to be more robust and systematic to parameterize. Our multiscale modeling approach is general and can be readily extended in the future to other chemistries, morphologies, and models for the local solute diffusivity and interactions with the membrane.

A Brush-like Li-Ion Exchange Polymer as Potential Artificial Solid Electrolyte Interphase for Dendrite-Free Lithium Metal Batteries

Artificial polymeric solid electrolyte interfaces (APSEIs) are an emerging material that enables use of a lithium metal anode as a lithium metal battery technique with high energy density. However, the poor ionic conductivity, low lithium transference number, and bad compatibity with lithium metal anode lead to a large dissipative loss of energy capacity. Here we report that, by properly constructing a brush-like structure in cellulose nanofibril (CNF) based APSEIs, a good ion-aggregation morphology with interconnected ionic conducting channels can be built, such that the Li-ion conduction in the APSEI layer becomes highly efficient. The optimal approach to constructing such an ionic highway is proved computationally using coarse-grained molecular dynamics (CGMD) simulations and implemented experimentally based on transmission electron microscopy (TEM) and atomic force microscopy (AFM). In addition, Li-ion exchange structures and hydroxyl-abundant structures endow the APSEIs with good ability to suppress dendrite growth and excellent compatibility with the anode surface.

Perspective: Morphology and ion transport in ion-containing polymers from multiscale modeling and simulations

Ion-containing polymers are soft materials composed of polymeric chains and mobile ions. Over the past several decades they have been the focus of considerable research and development for their use as the electrolyte in energy conversion and storage devices. Recent and significant results obtained from multiscale simulations and modeling for proton exchange membranes (PEMs), anion exchange membranes (AEMs), and polymerized ionic liquids (polyILs) are reviewed. The interplay of morphology and ion transport is emphasized. We discuss the influences of polymer architecture, tethered ionic groups, rigidity of the backbone, solvents, and additives on both morphology and ion transport in terms of specific interactions. Novel design strategies are highlighted including precisely controlling molecular conformations to design highly ordered morphologies; tuning the solvation structure of hydronium or hydroxide ions in hydrated ion exchange membranes; turning negative ion-ion correlations to positive correlations to improve ionic conductivity in polyILs; and balancing the strength of noncovalent interactions. The design of single-ion conductors, well-defined supramolecular architectures with enhanced one-dimensional ion transport, and the understanding of the hierarchy of the specific interactions continue as challenges but promising goals for future research.

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

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 || LiNi_0.8 Co_0.1 Mn_0.1 O₂ 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.

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

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 that exhibit optimal transport properties. The ground truth data are obtained from kinetic Monte Carlo (kMC) simulations of cation transport parametrized 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, by 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.

Colloidal Nanocrystal Gels from Thermodynamic Principles

Gels assembled from solvent-dispersed nanocrystals are of interest for functional materials because they promise the opportunity to retain distinctive properties of individual nanocrystals combined with tunable, structure-dependent collective behavior. By incorporating stimuli-responsive components, these materials could also be dynamically reconfigured between structurally distinct states. However, nanocrystal gels have so far been formed mostly through irreversible aggregation, which has limited the realization of these possibilities. Meanwhile, gelation strategies for larger colloidal microparticles have been developed using reversible physical or chemical interactions. These approaches have enabled the experimental navigation of theoretically predicted phase diagrams, helping to establish an understanding of how thermodynamic behavior can guide gel formation in these materials. However, the translation of these principles to the nanoscale poses both practical and fundamental challenges. The molecules guiding assembly can no longer be safely assumed to be vanishingly small compared to the particles nor large compared to the solvent.In this Account, we discuss recent progress toward the assembly of tunable nanocrystal gels using two strategies guided by equilibrium considerations: (1) reversible chemical bonding between functionalized nanocrystals and difunctional linker molecules and (2) nonspecific, polymer-induced depletion attractions. The effective nanocrystal attractions, mediated in both approaches by a secondary molecule, compete against stabilizing repulsions to promote reversible assembly. The structure and properties of the nanocrystal gels are controlled microscopically by the design of the secondary molecule and macroscopically by its concentration. This mode of control is compelling because it largely decouples nanocrystal synthesis and functionalization from the design of interactions that drive assembly. Statistical thermodynamic theory and computer simulation have been applied to simple models that describe the bonding motifs in these assembling systems, furnish predictions for conditions under which gelation is likely to occur, and suggest strategies for tuning and disassembling the gel networks. Insights from these models have guided experimental realizations of reversible gels with optical properties in the infrared range that are sensitive to the gel structure. This process avoids time-consuming and costly trial-and-error experimental investigations to accelerate the development of nanocrystal gel assemblies.These advances highlight the need to better understand interactions between nanocrystals, how interactions give rise to gel structure, and properties that emerge. Such an understanding could suggest new approaches for creating stimuli-responsive and dissipative assembled materials whose properties are tunable on demand through directed reconfiguration of the underlying gel microstructure. It may also make nanocrystal gels amenable to computationally guided design using inverse methods to rapidly optimize experimental parameters for targeted functionalities.