Modularized Cathode with Neural Network Topology for High Rate and Fault-Tolerant Lithium-Sulfur Batteries

Enhancing the redox kinetics of electrodes, achieving synergistic optimization of local energy conversion and overall charge transfer, and overcoming the technical bottleneck of significant performance degradation due to local unit failure in traditional electrode systems are crucial for developing high-rate lithium-sulfur batteries. Here, a modular cathode system (CoB₁N₃-MR/FNN) with a fully connected cascade neural network topology (FNN) is designed by constructing microreactor modules (CoB₁N₃-MRs) with embedded nanozymes (Co-B₁N₃), ordering and efficiently interconnecting them. This system not only enables efficient energy conversion within individual microreactors but also significantly enhances the long-range charge transport efficiency and energy aggregation capacity of the electrodes. Furthermore, CoB₁N₃-MR/FNN achieves fault tolerance to local damage through its distributed energy storage units and redundant charge transport channels. This synergistically enhanced modular electrode system for energy conversion and charge transport exhibits high specific discharge capacity (0.2 C, 1211 mAh g^-1) and excellent rate capability (5 C, 731.26 mAh g^-1; 10 C, 471.05 mAh g^-1), and shows outstanding electrochemical performances in high sulfur loading, low electrolytes, and flexible pouch batteries (0.2 C, 1165 mAh g^-1), fully demonstrating its practical application value.