The rate at which rechargeable batteries can be charged and discharged is governed by the selective transport of the working ions through the electrolyte. Conductivity, the parameter commonly used to characterize ion transport in electrolytes, reflects the mobility of both cations and anions. The transference number, a parameter introduced over a century ago, sheds light on the relative rates of cation and anion transport. This parameter is, not surprisingly, affected by cation-cation, anion-anion, and cation-anion correlations. In addition, it is affected by correlations between the ions and neutral solvent molecules. Computer simulations have the potential to provide insights into the nature of these correlations. We review the dominant theoretical approaches used to predict the transference number from simulations by using a model univalent lithium electrolyte. In electrolytes of low concentration, one can obtain a quantitative model by assuming that the solution is made up of discrete ion-containing clusters-neutral ion pairs, negatively and positively charged triplets, neutral quadruplets, and so on. These clusters can be identified in simulations using simple algorithms, provided their lifetimes are sufficiently long. In concentrated electrolytes, more clusters are short-lived and more rigorous approaches that account for all correlations are necessary to quantify transference. Elucidating the molecular origin of the transference number in this limit remains an unmet challenge.

Electrolytes used in rechargeable batteries must enable rapid translation of the working ion between macroscopically separated electrodes. These electrolytes are, however, usually designed and synthesized using atomic-level insights. Whether the ideal electrolyte for a particular battery is a solid or a liquid remains an important unresolved question, especially as solids with conductivities comparable with liquids are discovered. To help resolve such questions, we present the first steps toward a unified framework for relating atomic and continuum scale phenomena. Solvation shells in liquids are entities that translate with the working ion for a short while before they break up due to Brownian motion. By contrast, solvation cages in classical solids and polymers cannot not translate with the working ion. Mobility of the entities that make up the cages and shells, which is quantified by an order parameter, is shown to influence translation of the working ion on continuum length scales.

While electric fields primarily result in migration of charged species in electrolytic solutions, the solutions are dynamically heterogeneous. Solvent molecules within the solvation shells of the cation will be dragged by the field while free solvent molecules will not. We combine electrophoretic NMR measurements of ion and solvent velocities under applied electric fields with molecular dynamics simulations to interrogate different solvation motifs in a model liquid electrolyte. Measured values of the cation transference number (t_{+}^{0}) agree quantitatively with simulation-based predictions over a range of electrolyte concentrations. Solvent-cation interactions strongly influence the concentration-dependent behavior of t_{+}^{0}. We identify a critical concentration at which most of the solvent molecules lie within solvation shells of the cations. The dynamic heterogeneity of solvent molecules is minimized at this concentration where t_{+}^{0} is approximately equal to zero.

Electrolytes in lithium-ion batteries comprise solvent mixtures, but analysis of ion transport is always based on treating the solvents as a single-entity. We combine electrophoretic NMR (eNMR) measurements and molecular dynamics (MD) simulations to quantify electric-field-induced transport in a concentrated solution containing LiPF₆ salt dissolved in an ethylene carbonate/ethyl methyl carbonate (EC/EMC) mixture. The selective transport of EC relative to EMC is reflected in the difference between two transference numbers, defined as the fraction of current carried by cations relative to the velocity of each solvent species. This difference arises from the preferential solvation of cations by EC and its dynamic consequences. The simulations reveal the presence of a large variety of transient solvent-containing clusters which migrate at different velocities. Rigorous averaging over different solvation environments is essential for comparing simulated and measured transference numbers. Our study emphasizes the necessity of acknowledging the presence of four species in mixed-solvent electrolytes.

Molecular dynamics (MD) simulations, density functional theory (DFT) calculations, and ¹H NMR spectroscopy were performed to gain a complementary understanding of the concentrated Li-ion electrolyte system, lithium bis(trifluoromethanesulfonyl)imide (Li[TFSI]) dissolved in tetraglyme. The computational methods provided the concentration dependence of differing solvation structure motifs by reference to changes in the corresponding NMR spectra. By combining both the computational and experimental methodologies, we show that the various solvation structures, dominated by the coordination between the tetraglyme (G4) solvent and lithium cation, directly influence the chemical shift separation of resonances in the ¹H NMR spectra of the solvent. Thus, the ¹H NMR spectra can be used to predict the fraction of tetraglyme involved in the solvation process, with quantitative agreement with solvation fraction predictions from MD simulation snapshots. Overall, our results demonstrate the reliability of a hybrid computational and experimental methodology to understand the solvation structure and hence transport mechanism of LiTFSI-G4 electrolytes in the low concentration region.

Bottom-up understanding of transport describes how molecular changes alter species concentrations and electrolyte voltage drops in operating batteries. Such an understanding is essential to predictively design electrolytes for desired transport behavior. We herein advocate building a structure-property-performance relationship as a systematic approach to accurate bottom-up understanding. To ensure generalization across salt concentrations as well as different electrolyte types and cell configurations, the property-performance relation must be described using Newman's concentrated solution theory. It uses Stefan-Maxwell diffusivity, _ij , to describe the role of molecular motions at the continuum scale. The key challenge is to connect _ij to the structure. We discuss existing methods for making such a connection, their peculiarities, and future directions to advance our understanding of electrolyte transport.

Nanoparticle organic hybrid materials (NOHMs) have been proposed as excellent electrolytes for combined CO₂ capture and electrochemical conversion due to their conductive nature and chemical tunability. However, CO₂ capture behavior and transport properties of these electrolytes after CO₂ capture have not yet been studied. Here, we use a variety of nuclear magnetic resonance (NMR) techniques to explore the carbon speciation and transport properties of branched polyethylenimine (PEI) and PEI-grafted silica nanoparticles (denoted as NOHM-I-PEI) after CO₂ capture. Quantitative ¹³C NMR spectra collected at variable temperatures reveal that absorbed CO₂ exists as carbamates (RHNCOO^- or RR'NCOO^-) and carbonate/bicarbonate (CO₃^2-/HCO₃^-). The transport properties of PEI and NOHM-I-PEI studied using ¹H pulsed-field-gradient NMR, combined with molecular dynamics simulations, demonstrate that coulombic interactions between negatively and positively charged chains dominate in PEI, while the self-diffusion in NOHM-I-PEI is dominated by silica nanoparticles. These results provide strategies for selecting adsorbed forms of carbon for electrochemical reduction.

Dehydration has been a key limiting factor for the operation of conductive hydrogels in practical application. Here, we report self-healable ionic skins that can self-regulate their internal moisture level by capturing extenral moistures via hygroscopic ion-coordinated polymer backbones through antipolyelectrolyte effect. Results show the ionic skin can maintain its mechanical and electrical functions over 16 months in the ambient environment with high stretchability (fracture stretch ∼2216 %) and conductivity (23.5 mS/cm). The moisture self-regulating capability is further demonstrated by repeated exposures to harsh environments such as 200°C heating, freezing, and vacuum drying with recovered conductivity and stretchability. Their reversible ionic and hydrogen bonds also enable self-healing feature as a sample with the fully cut-through damage can restore its conductivity after 24 h at 40 % relative humidity. Utilizing the ionic skin as a building block, self-healing flexible piezoelecret sensors have been constructed to monitor physiological signals. Together with a facile transfer-printing process, a self-powered sensing system with a self-healable supercapacitor and humidity sensor has been successfully demonstrated. These results illustrate broad-ranging possibilities for the ionic skins in applications such as energy storage, wearable sensors, and human-machine interfaces.