a multifunctional component in vehicles, which serves as both a power source and a structural component. [10][11][12][13] Hence, the total vehicle weight is expected to be reduced due to the mass reduction of structural components, such as car frames and airplane wings.This "structural battery" concept has drawn increasing attention in recent years, and it has been discussed by leading electric vehicle companies lately. [14,15] The key requirement of structural batteries is enhanced mechanical properties, such as strength, modulus, and robustness under different kinds of mechanical deformation (e.g., shearing, flexing, compression, and tension). Strategies to enhance mechanical properties have been explored at both system and material levels. At the system level, cells were integrated with external supporting materials with high strength, such as metals and carbon fiber (CF)-based fabric, to form better mechanical configurations like sandwich structures and strengthen the battery systems. [16][17][18] However, this strategy inevitably results in lower energy densities because of the extra components for reinforcements, and reported reductions are in the range of 40-95%. [16][17][18] At the material level, the underlying principle is to develop new multifunctional materials, which not only function as necessary components in a battery, but also provide enhanced mechanical properties. These materials range over all components in a battery, such as active electrode materials, electrolytes, binders, and substrates. [19][20][21][22] For example, various groups demonstrated that carbon fibers, which have been widely used for load carrying, can serve as the anode itself or the cathode current collector for structural batteries. [23][24][25][26] However, the cycling performance of carbon fibers is not satisfactory, and lithiation dramatically weakens the mechanical properties of carbon fibers. [27,28] Moreover, it is difficult to integrate carbon fibers and cathode materials densely, so the cell energy density is severely compromised. Besides electrode materials, mechanically strong aramid fibers have been explored as the separator, which remarkably enhances both safety and tensile strength of structural batteries, but the cell's capacity and cycling stability are sacrificed considerably. [29] Among different mechanical properties to enhance, flexural properties are especially important, since bending is one of the most common mechanical deformations in cars and aircraft.Structural batteries are attractive for weight reduction in vehicles, such as cars and airplanes, which requires batteries to have both excellent mechanical properties and electrochemical performance. This work develops a scalable and feasible tree-root-like lamination at the electrode/separator interface, which effectively transfers load between different layers of battery components and thus dramatically enhances the flexural modulus of pouch cells from 0.28 to 3.1 GPa. The underlying mechanism is also analyzed by finite element simulations. Meanwhile,...

Lithium metal batteries are attractive for next-generation energy storage because of their high energy density. A major obstacle to their commercialization is the uncontrollable growth of lithium dendrites, which arises from complicated but poorly understood interactions at the electrolyte/electrode interface. In this work, we use a machine learning-based artificial neural network (ANN) model to explore how the lithium growth rate is affected by local material properties, such as surface curvature, ion concentration in the electrolyte, and the lithium growth rates at previous moments. The ion concentration in the electrolyte was acquired by Stimulated Raman Scattering Microscopy, which is often missing in past experimental data-based modeling. The ANN network reached a high correlation coefficient of 0.8 between predicted and experimental values. Further sensitivity analysis based on the ANN model demonstrated that the salt concentration and concentration gradient, as well as the prior lithium growth rate, have the highest impacts on the lithium dendrite growth rate at the next moment. This work shows the potential capability of the ANN model to forecast lithium growth rate, and unveil the inner dependency of the lithium dendrite growth rate on various factors.