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The formation of passivating films is a common aging phenomenon, for example in weathering of rocks, silicon, and metals. In many cases, a dual-layer structure with a dense inner and a porous outer layer emerges. However, the origin of this dual-layer growth is so far not fully understood. In this work, a continuum model is developed, which describes the morphology evolution of the solid-electrolyte interphase (SEI) in lithium-ion batteries. Transport through the SEI and a growth reaction governed by the SEI surface energies are modelled. In agreement with experiments, this theory predicts that SEI grows initially as a dense film and subsequently as a porous layer. This dynamic phase transition is driven by the slowing down of electron transport as the film thickens. Thereby, the model offers a universal explanation for the emergence of dual-layer structures in passivating films.

Accurately predicting aging of lithium-ion batteries would help to prolong their lifespan, but remains a challenge owing to the complexity and interrelation of different aging mechanisms. As a result, aging prediction often relies on empirical or data-driven approaches, which obtain their performance from analyzing large datasets. However, these datasets are expensive to generate and the models are agnostic of the underlying physics and thus difficult to extrapolate to new conditions. In this article, a physical model is used to predict capacity fade caused by solid-electrolyte interphase (SEI) growth in 62 automotive cells, aged with 28 different protocols. Three protocols parametrize the time, current and temperature dependence of the model, the state of charge dependence results from the anode's open circuit voltage curve. The model validation with the remaining 25 protocols shows a high predictivity with a root-mean squared error of $1.28\%$. A case study with the so-validated model shows that the operating window, i.e. maximum and minimum state of charge, has the largest impact on SEI growth, while the influence of the applied current is almost negligible. Thereby the presented model is a promising approach to better understand, quantify and predict aging of lithium-ion batteries., 15 pages with 8 figures

Silicon anodes promise high energy densities of next-generation lithium-ion batteries, but suffer from shorter cycle life. The accelerated capacity fade stems from the repeated fracture and healing of the solid-electrolyte interphase (SEI) on the silicon surface. This interplay of chemical and mechanical effects in SEI on silicon electrodes causes a complex aging behavior. However, so far, no model mechanistically captures the interrelation between mechanical SEI deterioration and accelerated SEI growth. In this article, we present a thermodynamically consistent continuum model of an electrode particle surrounded by an SEI layer. The silicon particle model consistently couples chemical reactions, physical transport, and elastic deformation. The SEI model comprises elastic and plastic deformation, fracture, and growth. Capacity fade measurements on graphite anodesand in-situ mechanical SEI measurements on lithium thin films provide parametrization for our model. For the first time, we model the influence of cycling rate on the long-term mechanical SEI deterioration and regrowth. Our model predicts the experimentally observed transition in time dependence from square-root-of-time growth during battery storage to linear-in-time growth during continued cycling. Thereby our model unravels the mechanistic dependence of battery aging on operating conditions and supports the efforts to prolong the battery life of next-generation lithium-ion batteries., 15 pages, 13 figures

Quo vadis? The effect of how battery cycling affects solid���electrolyte interphase (SEI) growth rates is analyzed. To this aim, a continuum model of SEI growth is developed and compared to experiments on different time scales. With increasing time, the limiting mechanism changes from electron tunneling via reaction kinetics and charge diffusion to charge migration. The capacity fade of modern lithium ion batteries is mainly caused by the formation and growth of the solid���electrolyte interphase (SEI). Numerous continuum models support its understanding and mitigation by studying SEI growth during battery storage. However, only a few electrochemical models discuss SEI growth during battery operation. In this article, a continuum model is developed that consistently captures the influence of open-circuit potential, current direction, current magnitude, and cycle number on the growth of the SEI. The model is based on the formation and diffusion of neutral lithium atoms, which carry electrons through the SEI. Recent short- and long-term experiments provide validation for our model. SEI growth is limited by either reaction, diffusion, or migration. For the first time, the transition between these mechanisms is modelled. Thereby, an explanation is provided for the fading of capacity with time t of the form t�� with the scaling coefficent ��, 0��������1. Based on the model, critical operation conditions accelerating SEI growth are identified., publishedVersion

Synthetic membranes for desalination and ion separation processes are a prerequisite for the supply of safe and sufficient drinking water as well as smart process water tailored to its application. This requires a versatile membrane fabrication methodology. Starting from an extensive set of new ion separation membranes synthesized with a layer-by-layer methodology, we demonstrate for the first time that an artificial neural network (ANN) can predict ion retention and water flux values based on membrane fabrication conditions. The predictive ANN is used in a local single-objective optimization approach to identify manufacturing conditions that improve permeability of existing membranes. A deterministic global multi-objective optimization is performed in order to identify the upper bound (Pareto front) of the delicate trade-off between ion retention characteristics and permeability. Ultimately, a coupling of the ANN into a hybrid model enables physical insight into the influence of fabrication conditions on apparent membrane properties.

On negative carbon electrodes of lithium-ion batteries, a thin layer of electrolyte reduction products forms. Even though this solid-electrolyte interphase (SEI) suppresses further electrolyte reduction, continued SEI growth is a main contributor to lithium-ion loss, capacity fade, and limited battery lifetime [1]. Our theory-based continuum models and its experimental validation give insights into the relevant transport mechanisms for electrons and lithium ions in the SEI. Standard SEI models study capacity fade as a function of storage time or cycle number. We developed a series of models to predict additional dependencies, i.e., SEI morphology [2,3], potential dependence of SEI growth [4], dependence of SEI growth on cycling conditions [5], and lithium-ion concentration polarization inside SEI. We gain novel insights into transport mechanisms by comparing with dedicated experiments, e.g., battery storage at various state-of-charge [6], differential capacity analysis during cycling [7], and impedance spectroscopy on model electrodes [8]. Going beyond the standard SEI models, we consider electron transport and solvent diffusion in a single model. Therefore, we can discuss the morphology of SEI by taking into account the interplay of structure, reaction kinetics, and transport mechanisms [2,3]. From the potential dependence of capacity fade [6], we predict the mechanism behind its continued growth [4]. We present the first indirect experimental evidence that neutral radicals carry a negative charge and diffuse through the SEI. Bazant et al. have recently measured and simulated the differential capacity loss during cycling [5,7]. They find that SEI grows mainly while lithium is intercalating. In this talk, we will combine these models for calendar-life and cycle-life. Our model naturally predicts the transition from square-root-of-time growth to linear growth of SEI observed during fast cycling [9]. We analyze with 3D micro-structure resolved simulations how the potential dependence of SEI growth results in SEI inhomogeneity throughout the negative electrode. In this talk, we discuss a physics-based model for impedance spectroscopy of lithium batteries with SEI as porous surface film. Our consistent model results in an analytic expression for the cell impedance including bulk and surface processes. Validating our model with experiments of lithium metal electrodes [8], we find large transference numbers for lithium ions. This analysis reveals that lithium-ion transport through the SEI has solid electrolyte character. The figure is reprinted from Ref. 4 with permission from Wiley. This work is supported by the German Research Foundation (DFG) via the research training group SIMET. References [1] B. Horstmann, F. Single, A. Latz, Curr. Opin. Electrochem. 2019, 13, 61–69, doi:10.1016/j.coelec.2018.10.013. [2] F. Single, B. Horstmann, A. Latz, Phys. Chem. Chem. Phys. 2016, 18, 17810–17814, doi:10.1039/C6CP02816K. [3] F. Single, B. Horstmann, A. Latz, J. Electrochem. Soc. 2017, 164, E3132–E3145, doi:10.1149/2.0121711jes. [4] F. Single, A. Latz, B. Horstmann, ChemSusChem 2018, 11, 1950–1955, doi:10.1002/cssc.201800077. [5] S. Das, P. M. Attia, W. C. Chueh, M. Z. Bazant, J. Electrochem. Soc. 2019, 166, E107–E118, doi:10.1149/2.0241904jes. [6] P. Keil, S. F. Schuster, J. Wilhelm, J. Travi, A. Hauser, R. C. Karl, A. Jossen, J. Electrochem. Soc. 2016, 163, A1872–A1880, doi:10.1149/2.0411609jes. [7] P. M. Attia, S. Das, K. Lim, M. Z. Bazant, W. C. Chueh, J. Electrochem. Soc. 2019, 166, E97–E106, doi:10.1149/2.0231904jes. [8] F. Wohde, M. Balabajew, B. Roling, J. Electrochem. Soc. 2016, 163, A714–A721, doi:10.1149/2.0811605jes. [9] D. Li, D. L. Danilov, L. Gao, Y. Yang, P. H. L. Notten, Electrochim. Acta 2016, 210, 445–455, doi:10.1016/j.electacta.2016.05.091. Figure 1