Innovative Solutions for High-Performance Silicon Anodes in Lithium-Ion Batteries:Overcoming Challenges and Real-World Applications

Silicon(Si)has emerged as a potent anode material for lithium-ion batteries(LIBs),but faces challenges like low electrical conductivity and significant volume changes during lithiation/delithiation,leading to material pulverization and capacity degradation.Recent research on nanostructured Si aims to mitigate volume expansion and enhance electrochemical performance,yet still grapples with issues like pulverization,unstable solid electrolyte interface(SEI)growth,and interparticle resistance.This review delves into innovative strategies for optimizing Si anodes’electrochemical performance via structural engineering,focusing on the synthesis of Si/C composites,engineering multidimensional nanostructures,and applying non-carbonaceous coatings.Forming a stable SEI is vital to prevent electrolyte decomposition and enhance Li^(+)transport,thereby stabilizing the Si anode interface and boosting cycling Coulombic efficiency.We also examine groundbreaking advancements such as self-healing polymers and advanced prelithiation methods to improve initial Coulombic efficiency and combat capacity loss.Our review uniquely provides a detailed examination of these strategies in real-world applications,moving beyond theoretical discussions.It offers a critical analysis of these approaches in terms of performance enhancement,scalability,and commercial feasibility.In conclusion,this review presents a comprehensive view and a forward-looking perspective on designing robust,high-performance Si-based anodes the next generation of LIBs.

Towards smart optical focusing:deep learningempowered dynamic wavefront shaping through nonstationary scattering media

Optical focusing through scattering media is of great significance yet challenging in lots of scenarios,including biomedical imaging,optical communication,cybersecurity,three-dimensional displays,etc.Wavefront shaping is a promising approach to solve this problem,but most implementations thus far have only dealt with static media,which,however,deviates from realistic applications.Herein,we put forward a deep learning-empowered adaptive framework,which is specifically implemented by a proposed Timely-Focusing-Optical-Transformation-Net(TFOTNet),and it effectively tackles the grand challenge of real-time light focusing and refocusing through time-variant media without complicated computation.The introduction of recursive fine-tuning allows timely focusing recovery,and the adaptive adjustment of hyperparameters of TFOTNet on the basis of medium changing speed efficiently handles the spatiotemporal non-stationarity of the medium.Simulation and experimental results demonstrate that the adaptive recursive algorithm with the proposed network significantly improves light focusing and tracking performance over traditional methods,permitting rapid recovery of an optical focus from degradation.It is believed that the proposed deep learning-empowered framework delivers a promising platform towards smart optical focusing implementations requiring dynamic wavefront control.