Electrolyte decomposition constitutes an outstanding challenge to long-life Li-ion batteries (LIBs) as well as emergent energy storage technologies, contributing to protection via solid electrolyte interphase (SEI) formation and irreversible capacity loss over a battery’s life. Major strides have been made to understand the breakdown of common LIB solvents; however, salt decomposition mechanisms remain elusive. In this work, we use density functional theory to explain the decomposition of lithium hexafluorophosphate (LiPF6) salt under SEI formation conditions. Our results suggest that LiPF6 forms POF3 primarily through rapid chemical reactions with Li2CO3, while hydrolysis should be kinetically limited at moderate temperatures. We further identify selectivity in the proposed autocatalysis of POF3, finding that POF3 preferentially reacts with highly anionic oxygens. These results provide a means of interphase design in LIBs, indicating that LiPF6 reactivity may be controlled by varying the abundance or distribution of inorganic carbonate species or by limiting the transport of PF6- through the SEI.

Interfacial reactions are notoriously difficult to characterize, and robust prediction of the chemical evolution and associated functionality of the resulting surface film is one of the grand challenges of materials chemistry. The solid-electrolyte interphase (SEI), critical to Li-ion batteries (LIBs), exemplifies such a surface film, and despite decades of work, considerable controversy remains regarding the major components of the SEI as well as their formation mechanisms. Here we use a reaction network to investigate whether lithium ethylene monocarbonate (LEMC) or lithium ethylene dicarbonate (LEDC) is the major organic component of the LIB SEI. Our data-driven, automated methodology is based on a systematic generation of relevant species using a general fragmentation/recombination procedure which provides the basis for a vast thermodynamic reaction landscape, calculated with density functional theory. The shortest pathfinding algorithms are employed to explore the reaction landscape and obtain previously proposed formation mechanisms of LEMC as well as several new reaction pathways and intermediates. For example, we identify two novel LEMC formation mechanisms: one which involves LiH generation and another that involves breaking the (CH₂)O-C(═O)OLi bond in LEDC. Most importantly, we find that all identified paths, which are also kinetically favorable under the explored conditions, require water as a reactant. This condition severely limits the amount of LEMC that can form, as compared with LEDC, a conclusion that has direct impact on the SEI formation in Li-ion energy storage systems. Finally, the data-driven framework presented here is generally applicable to any electrochemical system and expected to improve our understanding of surface passivation.

Modeling reactivity with chemical reaction networks could yield fundamental mechanistic understanding that would expedite the development of processes and technologies for energy storage, medicine, catalysis, and more. Thus far, reaction networks have been limited in size by chemically inconsistent graph representations of multi-reactant reactions (e.g. A + B → C) that cannot enforce stoichiometric constraints, precluding the use of optimized shortest-path algorithms. Here, we report a chemically consistent graph architecture that overcomes these limitations using a novel multi-reactant representation and iterative cost-solving procedure. Our approach enables the identification of all low-cost pathways to desired products in massive reaction networks containing reactions of any stoichiometry, allowing for the investigation of vastly more complex systems than previously possible. Leveraging our architecture, we construct the first ever electrochemical reaction network from first-principles thermodynamic calculations to describe the formation of the Li-ion solid electrolyte interphase (SEI), which is critical for passivation of the negative electrode. Using this network comprised of nearly 6000 species and 4.5 million reactions, we interrogate the formation of a key SEI component, lithium ethylene dicarbonate. We automatically identify previously proposed mechanisms as well as multiple novel pathways containing counter-intuitive reactions that have not, to our knowledge, been reported in the literature. We envision that our framework and data-driven methodology will facilitate efforts to engineer the composition-related properties of the SEI - or of any complex chemical process - through selective control of reactivity.

Lithium-ion batteries (LIBs) represent the state of the art in high-density energy storage. To further advance LIB technology, a fundamental understanding of the underlying chemical processes is required. In particular, the decomposition of electrolyte species and associated formation of the solid electrolyte interphase (SEI) is critical for LIB performance. However, SEI formation is poorly understood, in part due to insufficient exploration of the vast reactive space. The Lithium-Ion Battery Electrolyte (LIBE) dataset reported here aims to provide accurate first-principles data to improve the understanding of SEI species and associated reactions. The dataset was generated by fragmenting a set of principal molecules, including solvents, salts, and SEI products, and then selectively recombining a subset of the fragments. All candidate molecules were analyzed at the ωB97X-V/def2-TZVPPD/SMD level of theory at various charges and spin multiplicities. In total, LIBE contains structural, thermodynamic, and vibrational information on over 17,000 unique species. In addition to studies of reactivity in LIBs, this dataset may prove useful for machine learning of molecular and reaction properties.

Exercise changes the tumor microenvironment by remodeling blood vessels and increasing infiltration by cytotoxic immune cells. The mechanisms driving these changes remain unclear. Herein, we demonstrate that exercise normalizes tumor vasculature and upregulates endothelial expression of VCAM1 in YUMMER 1.7 and B16F10 murine models of melanoma but differentially regulates tumor growth, hypoxia, and the immune response. We found that exercise suppressed tumor growth and increased CD8+ T-cell infiltration in YUMMER but not in B16F10 tumors. Single-cell RNA sequencing and flow cytometry revealed exercise modulated the number and phenotype of tumor-infiltrating CD8+ T cells and myeloid cells. Specifically, exercise caused a phenotypic shift in the tumor-associated macrophage population and increased the expression of MHC class II transcripts. We further demonstrated that ERK5 S496A knock-in mice, which are phosphorylation deficient at the S496 residue, mimicked the exercise effect when unexercised, yet when exercised, these mice displayed a reversal in the effect of exercise on tumor growth and macrophage polarization compared with wild-type mice. Taken together, our results reveal tumor-specific differences in the immune response to exercise and show that ERK5 signaling via the S496 residue plays a crucial role in exercise-induced tumor microenvironment changes. See related Spotlight by Betof Warner, p. 1158.