Esther S. Takeuchi

Experimental measurements and molecular dynamics (MD) simulations study the structural and rheological properties of a model fluorinated ester electrolyte, methyl 3,3,3-trifluoropropionate (MTFP) with LiFSI, over a wide concentration range (0.1–2.3 mol/kg). MD simulations using a calibrated Generalized AMBER Force Field report a transition from liquid-like solvation to anisotropic ion aggregation and intermediate-range structural ordering as the salt concentration increases. One-dimensional radial distribution functions and three-dimensional ion density maps link these structural changes to the experimentally observed evolution of macroscopic properties, including a sharp viscosity divergence near 1 mol/kg driven by percolation of ion aggregates. The results provide a mechanistic foundation for designing ester-based localized high-concentration electrolytes, a class of materials that remains significantly underexplored despite compelling practical advantages in safety, oxidative stability, and low-temperature performance. Using the aforementioned electrolyte as a model system, this study establishes a modeling approach which highlights the importance of the solvent molecular geometry in directing ion organization and electrolyte rheology.

High‐capacity, large‐volume‐change anode materials offer attractive energy density but undergo pronounced volumetric swings during cycling, which can fracture the binder network, induce electrode cracking, and trigger sustained parasitic reactions and rapid capacity fade. This work reports an all‐aqueous, dual‐conductivity binder based on poly(vinyl alcohol) (PVA) and the organic mixed ionic/electronic conductor poly[3‐(potassium‐4‐butanoate)thiophene‐2,5‐diyl] (P3KBT), combined with freeze‐thaw (FT) processing to stabilize both the electrode framework and requisite interfacial interactions. FT cycles performed on the blend induce the formation of crystalline PVA domains, while P3KBT interacts with both PVA and the active material via physicochemical interactions. Collectively, these interactions create a percolated mixed ionic‐electronic conductive network. Using Fe ₃ O ₄ as a model high‐capacity anode active material, the PVA/P3KBT electrode delivers >96% capacity retention over 400 cycles, together with markedly enhanced adhesion, mechanical robustness, and rate capability relative to PVA controls. Cross‐sectional analysis shows that the post‐cycling thickness increase of FT‐treated electrodes (∼172%) is dramatically lower than that of non‐FT analogues (up to ∼362%), indicating more effective mitigation of pulverization and delamination. Surface characterization further reveals a more uniform, compact, and crack‐free surface for FT‐treated electrodes, consistent with suppressed side reactions and a stabilized electrode architecture.

As battery function has improved over the past decades, so too has our ability to interrogate the underlying electrochemical processes. Thermal energy generation is a key aspect of batteries affecting electrochemical function, safety, and lifespan. The heat generated by a battery is related to thermodynamics, kinetics, structural changes, and side reactions; measurements of heat production can therefore provide insight into any of these subjects. Isothermal microcalorimetry (IMC) can quantify heat flow within functioning systems with microwatt (or better) accuracy and precision. The ability to simultaneously measure heat production and electrochemical properties allows for fundamental scientific investigations as well as practical measurements on laboratory scale and commercial cells with different form factors. Herein, we review the use of isothermal microcalorimetry for probing battery thermal effects associated with the (dis)charge reaction, phase changes, and parasitic reactions. This work serves as an introduction for unfamiliar readers and summarizes the research that has been done over the years. We begin with a brief history of IMC as it relates to battery research, followed by an overview of the applicable theory, and highlight extensive examples from the literature. Opportunities for future research directions involving application and advances for the use of IMC are also presented.

Aqueous Zn||MnO ₂ batteries are promising for large‐scale energy storage but suffer from capacity fading due to irreversible transformations at the MnO ₂ cathode. Achieving structural reversibility under long‐term cycling remains a central challenge. Here, we show that tuning the electrolyte solvation environment can directly stabilize Mn redox chemistry and lattice evolution in mild‐aqueous Zn||ε‐MnO ₂ batteries. Using dimethyl sulfoxide (DMSO) as a co‐solvent to lower water activity and reorganize the solvation structure, we reveal through operando X‐ray diffraction that the modified electrolyte promotes reversible Zn ²⁺ intercalation despite slower transport kinetics. Complementary X‐ray photoelectron spectroscopy shows that conventional aqueous electrolytes induce strong Mn‐valence gradients between surface and bulk, whereas DMSO suppresses these gradients and maintains uniform redox states. Collectively, these results demonstrate that electrolyte solvation engineering can mitigate parasitic reactions and enable structurally reversible MnO ₂ redox processes, establishing a holistic pathway to enhance the long‐term stability of mild‐aqueous Zn‐based batteries.

A physics-based continuum model is developed for analysis of the electrochemical behavior of Zn/NaV ₃ O ₈ aqueous batteries by combining experimental Galvanostatic Intermittent Titration Technique (GITT) data with numerical simulations. The model is calibrated using discharge open-circuit potential (OCP) data and validated over a wide range of current densities from 50 to 2000 mA g ⁻¹ . Comparison of different OCP formulations shows that the discharge-derived OCP provides the best agreement with experimental voltage profiles, while charge and averaged OCP models result in larger fitting errors. Parameter estimation using Sobol sampling reveals well-constrained reaction rate constants for Zn ²⁺ and H ⁺ intercalation, with Zn ²⁺ insertion dominating the discharge process at the tested operating conditions. The model accurately reproduces discharge voltage behavior across all tested rates, demonstrating strong predictive capability for rate-dependent performance. However, the model cannot capture the large voltage hysteresis observed during charge, suggesting a path-dependent nature of intercalation in Zn/NaV ₃ O ₈ . These results show that discharge-based modeling provides reliable discharge predictions while indicating the need for hysteresis-aware approaches to achieve full-cycle simulation accuracy.

Nickel‐rich lithium nickel manganese cobalt oxide, NMC (LiNi _x Mn _y Co _z O ₂ ), materials are desirable positive electrodes in lithium ion batteries, providing high capacity and energy density. The mesoscale structure of NMC materials is commonly a polycrystalline aggregate, providing opportunity for short lithium ion transport distance of the small primary particles, yet facile material handling due to the larger secondary particles. On (de)lithiation the NMC unit cell changes volume, where the anisotropic strain can result in secondary particle fracture. This secondary particle fracture process during cycling has been associated with several degradation modes of NMC materials in LIBs. In this work, a milling process was determined whereby the secondary particles could be pre‐fractured with retention of the parent primary particle crystallographic structure, crystallite size, and morphology, providing the ability to unambiguously determine the influence of NMC secondary particle size and mesostructure on resultant functional behavior. The bulk and interfacial properties and electrochemistry of as‐received commercially obtained polycrystal NMC811 (LiNi _0.8 Mn _0.1 Co _0.1 O ₂ , PC NMC) and its milled MPC NMC counterpart were compared. The smaller secondary particle size, higher surface area MPC NMC was found to result in 1) greater cathode tortuosity, 2) reduced surface Ni on the cycled MPC cathodes consistent with surface reconstruction, and 3) increased Ni deposition on the anodes from cathode‐anode crosstalk. These factors manifested in unfavorable electrochemical behavior of MPC with lower functional capacity at both 1C and C/10 rates and higher impedance over extended moderate voltage (dis)charge cycling.

Aqueous Zn/MnO₂ batteries have garnered significant interests owing to their abundance, high theoretical specific capacity, safety, and low cost. However, large-scale application of these systems is limited by the incomplete understanding of the MnO₂ reaction chemistry. The different crystal lattice structures among MnO₂ polymorphs contribute to the variations in reported reaction mechanisms. Among them, ε-MnO₂ polymorph, the dominant phase in electrolytic manganese dioxide (EMD), is notably observed during the charge cycles of aqueous Zn/MnO₂ batteries. In this work, we investigate the electrochemical behavior of an ε-MnO₂ cathode synthesized via electrodeposition from a ZnSO₄ and MnSO₄ electrolyte_, onto a 3-dimensional carbon cloth substrate. Proton intercalation emerges as the dominant charge storage mechanism, critically enabling the reversibility of ε-MnO₂ during cycling, as revealed by operando synchrotron X-ray diffraction and X-ray absorption spectroscopy. Additionally, a proton-coupled dissolution/redeposition pathway operates alongside minor Zn²⁺ intercalation, as quantified by Rietveld refinement. Morphological and chemical heterogeneities are studied by transmission X-ray microscopy further validates this reaction mechanism. These mechanistic insights provide the foundation for rationally designing Zn/MnO₂ batteries with optimized proton dynamics and charge transfer, advancing these systems as a viable solution for safe, cost-effective grid-scale energy storage.

Machine learning (ML) provides powerful pathways for predicting spectroscopic observables from atomic structures, but its broader impact depends on making model predictions interpretable in terms of physical and chemical principles. Here, we introduce a physics-guided graph neural network (GNN) model that predicts Zn K-edge X-ray spectroscopy (XAS) spectra of aqueous ZnCl₂ solutions. Training data are generated from ab initio XAS calculations on molecular dynamics snapshots obtained using a machine learning interatomic potential. The GNN reproduces experimental spectra across concentrations from dilute (<0.1 m) to highly concentrated (30 m, "water-in-salt") regimes and scales efficiently to large, disordered liquid systems beyond the reach of conventional ab initio approaches. Gradient-based attribution analysis reveals that the model learns physically meaningful structure-spectrum relationships. Ligand-specific attributions reflect orbital hybridization patterns and the origin of the excitations derived from the density functional theory. Bond-length attributions recover spectral shifts consistent with multiple-scattering theory. This work bridges data-driven prediction with electronic-structure theory, establishing a general paradigm for interpretable ML that links atomic structure, electronic structure, and spectroscopic observables.

Accurately predicting solid electrolyte interphase (SEI) formation requires explicitly resolving the electric double layer (EDL) structure, which deviates significantly from that of the bulk electrolyte. Although an established molecular dynamics (MD) and Density Functional Theory (DFT) framework can model SEI formation by evaluating reduction reactions of local clusters in the EDL, it suffers from a combinatorial computational bottleneck. To overcome this limitation, we introduce a machine-learning-accelerated simulation workflow (MD-DFT-ML), integrating a gradient-boosted regression model trained on EDL composition data to efficiently predict reduction potentials. We apply this framework to seven fluorinated electrolytes comprising fluorinated anions, a fluorinated ester solvent, two types of diluent (ion-solvating ester vs. non-solvating ether), and an FEC additive. The analysis shows that the EDL selectively accumulates cation-binding species; consequently, the non-cation-binding ether diluent rarely enters the EDL and makes minimal contributions to SEI formation. DFT calculations on statistically representative EDL clusters provide reduction potentials and fluorine release pathways, while the ML model, which substantially reduces the DFT workload, predicts cluster reduction energies with a mean absolute error of 0.1 eV. The combined MD-DFT-ML approach also quantifies contributions from different sources to LiF formation in the SEI. This methodology establishes a generalizable route for predictive electrolyte and interphase design for next-generation electrochemical energy-storage systems.

High-entropy materials, especially high-entropy alloys and oxides, have sparked immense interest in the past few years due to their novel and curiously complex stoichiometries and structures, thereby allowing for the potential to generate materials with highly tunable and functional properties. Many of the studies on high entropy oxides have focused on five-cation-containing oxides produced via solid-state syntheses. Herein, we report on the practicality of the solution-based synthesis of 9-element high entropy oxide nanoparticles, incorporating Al, Co, Cu, Fe, Mg, Mn, Ni, Ti, and Zn, and have probed reaction conditions that influence the stability of the resulting single-phase spinel crystal structure. The separate but important discrete effects of (a) solvent, (b) surfactant, and (c) heating method, as well as (d) constituent cation composition, respectively, have been systematically explored and correlated with their resulting electrochemical properties and their microstructure, as revealed by complementary HR-TEM analysis. It has been found that nanoparticles characterized by single-phase spinel chemical compositions can be reliably reproduced with a wide variety of cations.