Insights into Enhanced Capacitive Behavior of Carbon Cathode for Lithium Ion Capacitors: The Coupling of Pore Size and Graphitization Engineering

The lack of methods to modulate intrinsic textures of carbon cathode has seriously hindered the revelation of in-depth relationship between inherent natures and capacitive behaviors,limiting the advancement of lithium ion capacitors(LICs).Here,an orientateddesigned pore size distribution(range from 0.5 to 200 nm)and graphitization engineering strategy of carbon materials through regulating molar ratios of Zn/Co ions has been proposed,which provides an effective platform to deeply evaluate the capacitive behaviors of carbon cathode.Significantly,after the systematical analysis cooperating with experimental result and density functional theory calculation,it is uncovered that the size of solvated PF6-ion is about 1.5 nm.Moreover,the capacitive behaviors of carbon cathode could be enhanced attributed to the controlled pore size of 1.5-3 nm.Triggered with synergistic effect of graphitization and appropriate pore size distribution,optimized carbon cathode(Zn90Co10-APC)displays excellent capacitive performances with a reversible specific capacity of^50 mAh g-1at a current density of 5 A g-1.Furthermore,the assembly pre-lithiated graphite(PLG)//Zn90Co10-APC LIC could deliver a large energy density of 108 Wh kg-1 and a high power density of 150,000 W kg-1 as well as excellent long-term ability with 10,000 cycles.This elaborate work might shed light on the intensive understanding of the improved capacitive behavior in LiPF<sub>6 electrolyte and provide a feasible principle for elaborate fabrication of carbon cathodes for LIC systems.

Hollow carbon microbox from acetylacetone as anode material for sodium-ion batteries

Carbon-based materials have attracted much interest as one of the promising anodes for sodium-ion batteries. However, low utilization of electrolyte and slow ion-transfer rate during electrochemical process hinder the further application of traditional bulk carbon. In order to enhance the diffusion kinetics and maintain the reversibility, hierarchical hollow carbon microbox was successfully prepared through a tunable bottom-up self-template routine for sodium-ion batteries. During annealing process, the morphology construction and activation happened synchronously. Based on that, a range of cross-linked porous nanosheet and hollow microbox were attained by manipulating reactant condition. The generation of texture and physical property are analyzed and are established linkages related to the electrochemical behavior. As results depicted in kinetic exploration and simulation based on cyclic voltammetry, the surfacecontrolled electrochemical behavior gradually turns to be the diffusion-controlled behavior as the hollow microbox evolves to porous nanosheet. The probable reason is that the rational microstructure/texture design leads to the accelerated diffusion kinetic procedure and the reduced concentration difference polarization. Sodium storage mechanism was deduced as reversible binding of Na-ions with local defects,including vacancies on sp2 graphitic layers, at the edges of flakes and other structural defects instead of intercalation. Bestowed by the morphology design, the broad pore width distribution, abundant defects/active sites and surface functionality, hollow microbox electrode delivers great electrochemical performances. This work is expected to propose a novel and effective strategy to prepare tunable hierarchical hollow carbon microbox and induce the fast kinetic of carbon anode material.

Applications and Challenges of Deep Reinforcement Learning in Multi-robot Path Planning

With the rapid advancement of deep reinforcement learning(DRL)in multi-agent systems,a variety of practical application challenges and solutions in the direction of multi-agent deep reinforcement learning(MADRL)are surfacing.Path planning in a collision-free environment is essential for many robots to do tasks quickly and efficiently,and path planning for multiple robots using deep reinforcement learning is a new research area in the field of robotics and artificial intelligence.In this paper,we sort out the training methods for multi-robot path planning,as well as summarize the practical applications in the field of DRL-based multi-robot path planning based on the methods;finally,we suggest possible research directions for researchers.