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 β with the scaling coefficent β , 0≤ β ≤1. Based on the model, critical operation conditions accelerating SEI growth are identified.

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 anodes and 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.

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.