Revati Kumar

Solvate ionic liquids (SILs), equimolar mixtures of lithium salts and chelating glymes, are promising electrolytes for energy storage due to their high thermal stability and low vapor pressure. However, the low lithium transference numbers and modest ionic conductivities of SILs limit device performance. Here, long-timescale molecular dynamics simulations combined with transport characterization identify the Li+-Li+ structural decorrelation as the key descriptor governing ion transport in SILs composed of lithium bis(trifluoromethanesulfonyl)imide and glymes of various lengths. These SILs have extended ionic networks in which anions bridge multiple Li+ centers, generating long-lived structural correlations. SIL ion transport is initiated by speciation exchange, which disrupts the ionic network and leads to structural decorrelation on nanosecond timescales. Decreasing glyme chain length increases anion bridging, slows structural decorrelation, and decreases ionic conductivity, while increasing lithium transference. These results establish structural decorrelation as a key descriptor of transport in SILs. While the anion-mediated decorrelation is demonstrated for SILs, this framework is general and can be applied to any high concentration electrolyte in which transport emerges from the reorganization of correlated ionic networks.

Glyme-based electrolytes have emerged as promising candidates for sodium-ion batteries owing to their ability to form stable solvation structures around Na+ via multidentate coordination with ether oxygen atoms. While the solvation and transport properties of sodium bis(trifluoromethanesulfonyl)imide (NaTFSI) in short-chain glymes have been systematically investigated, solutions of pentaglyme (G5) remain poorly understood and, to the best of our knowledge, are unexplored in binary glyme mixtures. Herein, molecular dynamics (MD) simulations are used to investigate the solvation structure, ion association, and transport properties of NaTFSI across six systems: monoglyme (G1), triglyme (G3), and pentaglyme (G5), as well as three binary G1:G3 mixtures of varying composition. A combination of experiments and simulations is employed to provide a comprehensive structural and dynamic characterization, including ionic conductivity, viscosity, van Hove correlation functions, and Walden analysis. The results reveal that G3 preferentially displaces G1 from the Na+ coordination shell, even when present in small amounts, thereby generating free G1 molecules that reduce viscosity in G1-containing systems. Analysis of the Na+–Na+ spatial correlations reveals a glyme-bridged Na+–G3–Na+ structural motif unique to G3 and persisting across all G3-containing mixtures. In contrast, G5 exhibits fundamentally different dynamical behavior from G1(0.5):G3(0.5), despite providing the same oxygen-to-Na+ ratio, displaying the highest viscosity and lowest conductivity among all systems studied while maintaining long-lived Na+-Na+ structural correlations. Within the binary mixtures, the G1(0.25):G3(0.75) electrolyte achieves the optimal balance, exhibiting the smallest deviation from the ideal Walden line. These findings demonstrate that molecular architecture, as well as chain length, governs electrolyte behavior, providing fundamental insights for the rational design of glyme-based sodium-ion battery electrolytes.

Monomer sequence encodes the conformation and intra- and intermolecular interactions of sequence-defined polymers. Mixing different sequences represents an attractive strategy to modulate the intra- and interchain interactions to produce mesoscale assemblies with tunable size, geometry, and internal structures, provided that mixing different sequences is thermodynamically favored over self-sorting. In this study, we investigated the aqueous assembly of binary mixtures of sequence-defined peptoid block copolymers (BCPs) with discrete chain lengths and varying charge patterns, i.e., the charge number and relative positioning along the chains. Förster Resonance Energy Transfer (FRET) experiments revealed the dynamic coassembly of peptoid chains with varying charge patterns to produce hybrid micellar aggregates. Small-angle X-ray scattering (SAXS) analysis further showed that the size and aggregation number of the hybrid micelles can be controlled by adjusting the stoichiometry of distinct sequences in the solution. Moreover, the interfacial hydrophobicity of the micellar aggregates was tailorable by the molar ratio of distinct sequences, evidenced by the binding studies with 8-anilino-1-naphthalenesulfonic acid (ANS), whose fluorescence is sensitively dependent on the polarity of its environment. This study highlights the effectiveness of producing micellar assemblies with tunable structural characteristics and interfacial hydrophobicity by mixing sequence-defined peptoid chains with varying charge patterns. This strategy expands the accessible chemical design space from a limited sequence set, paving the way for high-throughput materials discovery of micellar assemblies for different applications, such as enhanced drug encapsulation and solubilization.

Interfacial water exhibits properties that differ markedly from those of the bulk. At charged interfaces, its response reflects a subtle interplay between surface-specific second-order and bulk field-induced third-order contributions. Graphene oxide (GO) nanosheets contain oxygenated defects that create coexisting aromatic and hydrophilic domains, which strongly influence the interfacial water structure, dynamics, and reactivity. Here, we use molecular dynamics simulations with the many-body MB-pol water potential to examine GO/water interfaces with a tunable surface charge. Nuclear quantum effects are incorporated using temperature-elevated path-integral coarse-graining simulations (Te-PIGS) enabling the direct computation of both homodyne-detected (intensity) and heterodyne-detected (phase-resolved) vibrational sum-frequency generation (vSFG) spectra. By explicitly accounting for the bulk third-order susceptibility, χ⁽³⁾, we show that the prominent lower-frequency band near 3200 cm^-1 in experimental vSFG spectra is dominated by the field-induced bulk χ⁽³⁾ response, whereas the surface-specific features are governed by the second-order susceptibility, χ⁽²⁾. These results clarify how surface-specific and bulk field-induced responses combine to shape vSFG spectra at GO/water interfaces, providing a molecular basis for assigning spectral features to distinct interfacial hydrogen-bonding motifs.

Ether-based electrolytes have gained increasing attention for energy storage based on their utility as solvate ionic liquids at high concentration and their role in forming effective cointercalation complexes at graphite electrodes. While transferrable atomistic models have been proposed to describe glyme ether solutions at varying concentration and backbone chain lengths, coarse-grained models have not been extensively explored. The need for coarse-grained descriptions is emphasized by the formation of potentially mesoscale aggregate structures which enable efficient ion transport. Herein we describe a simple approach to developing such models using a combination of a charge smearing for long-ranged electrostatics and Boltzmann Inversion to develop short-ranged tabulated potentials. The impact of long-ranged interactions on electrolyte structure and the ingredients to the coarse-grained models are discussed along with the importance of system selection for training the short-ranged portion. Overall, the final model shows good transferability for diglyme and monoglyme, which share an emphasis on ion association, but fails to capture the solvent separated ionic structure of triglyme electrolytes.

Anion exchange membranes (AEMs) play a vital role in the performance of water electrolyzers and fuel cells, yet their discovery and optimization remain challenging due to the complexity of structure–property relationships. In this study, we introduce a machine learning framework that leverages conditional graph neural networks (cGNNs) and descriptor-based models and a hybrid graph neural network (HGARE) to predict and interpret ionic conductivity. The descriptor-based pipeline employs principal component analysis (PCA), ablation, and SHAP analysis to identify factors governing anion conductivity, revealing electronic, topological, and compositional descriptors as key contributors. Beyond prediction, dimensionality reduction and clustering are performed by employing t-SNE and KMeans as well as SOM, which reveal distinct membranes clusters, some of which were enriched with high anion conductivity. Among graph-based approaches, the graph convolutional (GCN) achieved strong predictive performance, while the Hybrid Graph Autoencoder-Regressor Ensemble (HGARE) achieved the highest accuracy. Additionally, atom-level saliency maps from GCN provide spatial explanations for conductive behavior, revealing the importance of polarizable and flexible regions. This work contributes to the accelerated and data-driven design of high-performance AEMs.

Anion exchange membranes (AEMs) play a vital role in the performance of water electrolyzers and fuel cells, yet their discovery and optimization remain challenging due to the complexity of structure–property relationships. In this study, we introduce a machine learning framework that leverages both graph neural networks (GNNs) and descriptor-based models to predict and interpret ionic conductivity. To explore structural patterns beyond prediction, we applied dimensionality reduction and clustering by employing t-SNE and DBSCAN, as well as Self-Organizing Maps (SOM), which reveal distinct membranes clusters, some of which were enriched with high anion conductivity. The optimized graph convolutional network (GCN) achieves an R² score of 0.94 on the test set. While the descriptor-based model is slightly less accurate, it offers valuable insights through SHAP analysis, highlighting electronic, topological, and compositional descriptors as key drivers of conductivity. Additionally, atom-level saliency maps from GCN provide spatial explanations for conductive behavior, revealing the importance of polarizable and flexible regions. This work contributes to the accelerated and data-driven design of high-performance AEMs.

Interfacial design using strategically chosen linkers, like cysteamine, can effectively mitigate confinement effects in ionomer thin films and improve proton transport in targeted directions.

Mixtures of lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) in diglyme (G2), triglyme (G3) and tetraglyme (G4) at solvate ionic liquid (SIL) concentrations were investigated using classical molecular dynamics (cMD) simulations with a physically motivated force-field specifically developed for modeling these systems. The structural and dynamical properties of the mixtures were computed and analyzed. Lithium solvation shells, radial distribution functions, and X-ray structure factors were studied across the different SIL systems. Translational diffusion and rotational relaxation times were also evaluated, exhibiting similar trends with increasing glyme chain length. The results are consistent with experimental data and in good agreement with previous computational studies on G3 and G4. These findings validate the accuracy of the force field in modeling glyme systems and its use for describing the [Li(G2)_4/3][TFSI] mixture. Additionally, the thermal and electrochemical stability of these electrolytes were systematically examined. The thermal stability appears to be governed by cooperative interactions among glyme molecules, while the electrochemical stability is primarily influenced by Li⁺-anion interactions, which vary significantly with glyme chain length. Overall, the study sheds light on the crucial role of the anion in these glyme-based SILs and offers valuable insights into Li⁺-glyme systems at SIL concentrations, highlighting their promise as potential Li-ion battery electrolytes.