Prelithiation emerges as a promising strategy to compensate the irreversible initial capacity loss of the SiO/ Graphite (SiO/Gr) composite anode. However, many scalable prelithiation methods encounter challenges of low efficiency and inhomogeneity, primarily stemming from a limited understanding of the spatial and temporal variations in prelithiation kinetics within large-scale electrodes. Utilizing an electrochemical prelithiation model, we integrated optimal electrode structure design with an electrochemically controllable method to concurrently enhance prelithiation efficiency and homogeneity. We demonstrated that prelithiation efficiency can be controlled by specifying the prelithiation current, while the design of the channel arrangement in the perforated electrode can enhance prelithiation homogeneity. Furthermore, we elucidated the interaction mechanism among channels under varying prelithiation currents and explored the impacts of channel size, channel spacing, and channel arrangement on prelithiation homogeneity under high prelithiation efficiency. With the insight gained from our simulation, the SiO/Gr composite anode featuring a channel diameter of 500 mu m and a square channel arrangement with a spacing of 8.7 mm was designed. This design achieved a high prelithiation efficiency within 10 h, and maintained the prelithiation nonuniformity parameter below 0.1, accelerating the practical implementation of high-energy-density SiO/Gr composite anodes.

Investigations of one-dimensional segmented heteronanostructures (1D-SHs) have recently attracted much attention due to their potentials for applications resulting from their structure and synergistic effects between compositions and interfaces. Unfortunately, developing a simple, versatile and controlled synthetic method to fabricate 1D-SHs is still a challenge. Here we demonstrate a stress-induced axial ordering mechanism to describe the synthesis of 1D-SHs by a general under-stoichiometric reaction strategy. Using the continuum phase-field simulations, we elaborate a three-stage evolution process of the regular segment alternations. This strategy, accompanied by easy chemical post-transformations, enables to synthesize 25 1D-SHs, including 17 nanowire-nanowire and 8 nanowire-nanotube nanostructures with 13 elements (Ag, Te, Cu, Pt, Pb, Cd, Sb, Se, Bi, Rh, Ir, Ru, Zn) involved. This ordering evolution-driven synthesis will help to investigate the ordering reconstruction and potential applications of 1D-SHs. The formation mechanisms for periodic heterostructures are still poorly understood. Here, the authors propose a versatile approach to synthesize one-dimensional segmented heterostructures and reveal a stress-induced ordering mechanism through phase-field simulations.

Metamaterials with structure-dominated properties provide a new way to design structures to obtain desired performance. To achieve a wide range of applications, on-demand tunable metamaterials would fulfill various and changing needs. The design of on-demand tunable metamaterials requires a higher-level understanding of the relationship between the properties of the metamaterials and the geometrical parameters, which in many cases are complicated and implicit. With the advancement of machine learning and evolutionary methods, it becomes possible to design on-demand tunable metamaterials. This paper designs on-demand tunable acoustic metamaterials for noise attenuation at varying frequencies by employing a genetic algorithm based neural network method. The C-shaped acoustic metamaterials with slidable shells are combined with the specifically designed tri-stable origami-inspired metamaterials to realize the on-demand tunable structure. Experiments were conducted and showed that the designed tunable metamaterials exhibited desired characteristics in different targeting frequency ranges. The present general methodology is expected to provide a route for on-demand tunable design while exploring more possibilities for the application of metamaterials.

Identifying efficient and accurate optimization algorithms is a long-desired goal for the scientific community. At present, a combination of evolutionary and deep-learning methods is widely used for optimization. In this paper, we demonstrate three cases involving different physics and conclude that no matter how accurate a deep-learning model is for a single, specific problem, a simple combination of evolutionary and deep-learning methods cannot achieve the desired optimization because of the intrinsic nature of the evolutionary method. We begin by using a physics-supervised deep-learning optimization algorithm (PSDLO) to supervise the results from the deep-learning model. We then intervene in the evolutionary process to eventually achieve simultaneous accuracy and efficiency. PSDLO is successfully demonstrated using both sufficient and insufficient datasets. PSDLO offers a perspective for solving optimization problems and can tackle complex science and engineering problems having many features. This approach to optimization algorithms holds tremendous potential for application in real-world engineering domains.

Cycling-induced cathode interfacial degradations are usually attributed to chemical process, while the physical effect is overlooked to a large extent. Herein, we investigate the failure mechanism of LiCoO2 cathode and reveal that misfit strain plays a dominant role in the surface layer exfoliation process. We illustrate that highly strained LiCoO2 surface can initiate massive surface cracks, leading to the LiCoO2 surface layer broken and exfoliation. Mechanical cracking coupled with chemical etching aggravates the surface layer degradation, leading to a weathering-like degradation on LiCoO2 surface. Our work reveals that interfacial degradation of electrode materials is a complex physicochemical process. [GRAPHICS] .

Composite particle with high speed channels for delivering lithium is a promising candidate for the high performance electrode in electric vehicles. However, it still suffers from poor rate capability due to the long diffusion pathway caused by large particle size. Inspired by the efficient transporting ability of the hierarchical structure in tree roots, we design the channel structure to improve the rate capability using a diffuse interface model. This model enables us to simulate the fast lithium diffusion along the channel network and the electrochemical reaction on the particle surface. Our results demonstrate that two microstructure features of the channel network can efficiently enhance the rate capacity: One is the gradient distribution of the channel density with a higher channel density near the surface than that in the center; the other is the orientation of the channel structure parallel to lithium flux from the particle surface. The root-inspired hierarchical channel network displaying these two features can improve the capacity retention at large C-rates. Furthermore, our study can offer an efficient tool that can rationalize the topology design of channel structure in the purpose of achieving a high rate capability.

Many promising candidate electrode materials suffer from severe voltage decay, which is detrimental to lithium ion battery performance. Recently, the relationship between stress-mediated chemical potential and voltage variation has received much attention. While, how the plastic deformation affects the voltage variation remains unclear. A stress coupled phase field reaction model is employed to reveal the mechanism for the stress-related voltage decay accompanied by plastic deformation, wherein different lithiation behaviors under electrochemical reactions are considered. It is demonstrated that hydrostatic compression on the surface is responsible for the stress-induced voltage decay, and the plastic deformation can mitigate the voltage decay via not only relaxing the hydrostatic compression, but also changing hydrostatic compression on the surface to be hydrostatic tension under the small yield stress. In addition, under the same yield stress, two-phase lithiation process is prone to trigger the plastic deformation compared to the single phase lithiation process, facilitating to elevate the output voltage. Further, compared to the circular particle, the interconnected particle can improve the output voltage through the favorable transition from hydrostatic compression to hydrostatic tension at the interconnected region and the surface with large curvature.

Ion migration has been recognized as a critical step in determining the performance of numerous devices in chemistry, biology, and material science. However, direct visualization and quantitative investigation of solid-phase ion migration among anisotropic nanostructures have been a challenging task. Here, we report an in-situ ChemTEM method to quantitatively investigate the solid-phase ion migration process among coassembled nanowires (NWs). This complicated process was tracked within a NW and between NWs with an obvious nanogap, which was revealed by both phase field simulation and ab initio modeling theoretical evaluation. A migration "bridge" between neighboring NWs was observed. Furthermore, these new observations could be applied to migration of other metal ions on semiconductor NWs. These findings provide critical insights into the solid-phase ion migration kinetics occurring in nanoscale systems with generality and offer an efficient tool to explore other ion migration processes, which will facilitate fabrication of customized and new heteronanostructures in the future.

A phase field model is developed to study the effect of charging methods on the stress evolution in an arbitrarily-shaped elastoplastic electrode particle. The model integrates Cahn-Hilliard equation with smoothed boundary method for two-phase lithiation under galvanostatic and potentiostatic operations and phase field microelasticity theory for inhomogeneous lithiation-mediated elasticity and plasticity. During two-phase lithiation, we show that the lithiation rate approximately remains constant under galvanostatic operation but slows down as lithiation proceeds under potentiostatic operation. While, the evolution of surface tangential stress during two-phase lithiation is similar under both galvanostatic and potentiostatic operations. Our results show that the surface tangential stress monotonously varies with the Li-rich phase volume, changing from compression to tension. The larger current density, as well as the larger chemical potential on the electrode particle surface, leads to the larger surface tangential compression in the early stage of lithiation but the larger surface tangential tension in the late stage. Our model is capable of probing the connection among the operating conditions, the complex particle geometry and the lithiation-induced stress.

In phase transformation electrodes under lithiation, growth of the Li-rich phase from the Li-poor phase usually proceeds with a moving phase boundary. Their high-rate capability is determined by how fast the phase interface can move. Unfortunately recent studies show that the stress-mediated migration velocity of the phase interface is significantly slowed down. We employ a non-linear stress-kinetics coupled model to address the effect of inner surface in hollow structures on the phase interface movement. We demonstrate that the inner surface can trigger a transition from deceleration to acceleration of the phase interface movement. The plastic deformation at the phase interface dissipates extra energy required to drive the inward migration of the phase interface and leads to deceleration. Another plastic zone arises near the inner surface and expands outward as two-phase lithiation proceeds. The coalescence of these two plastic zones significantly decreases the resistance of the phase interface movement and leads to acceleration. Furthermore, we show that tuning the size of hollow spheres regulates the phase interface migration toward high packing density and superior rate capability. (c) 2017 The Electrochemical Society. All rights reserved.