Li-rich manganese-based cathode materials (LRMs) are considered one of the most promising cathode materials for the next generation lithium-ion batteries (LIBs) because of their high energy density. However, there are problems such as capacity decay, poor rate performance, and continuous voltage drop, which seriously limit their large-scale commercial applications. In this work, Li1.2Mn0.54Co0.13Ni0.13O2 coated with Li2MoO4 with unique spinel structure was prepared by wet chemistry method and the subsequent calcination process. The Li2MoO4 coating layer with spinel structure can provide a 3D Li+ transport channel, which is beneficial for improving rate performance, while protecting LRMs from electrolyte corrosion, suppressing interface side reactions, and improving cycling stability. The capacity retention rate of LRMs coated with 3wt% Li2MoO4 increased from 69.25% to 81.85% after 100 cycles at 1C, and the voltage attenuation decreased from 7.06 to 4.98 mV per cycle. The lower Rct also exhibits improved magnification performance. The results indicate that the Li2MoO4 coating effectively improves the cyclic stability and electrochemical performance of LRMs.

The object detection task is usually affected by complex backgrounds. In this paper, a new image object detection method is proposed, which can perform multi-feature selection on multi-scale feature maps. By this method, a bidirectional multi-scale feature fusion network is designed to fuse semantic features and shallow features to improve the detection effect of small objects in complex backgrounds. When the shallow features are transferred to the top layer, a bottom-up path is added to reduce the number of network layers experienced by the feature fusion network, reducing the loss of shallow features. In addition, a multi-feature selection module based on the attention mechanism is used to minimize the interference of useless information on subsequent classification and regression, allowing the network to adaptively focus on appropriate information for classification or regression to improve detection accuracy. Because the traditional five-parameter regression method has severe boundary problems when predicting objects with large aspect ratios, the proposed network treats angle prediction as a classification task. The experimental results on the DOTA dataset, the self-made DOTA-GF dataset and the HRSC 2016 dataset show that, compared with several popular object detection algorithms, the proposed method has certain advantages in detection accuracy.

Cathode materials play a key role in the development and application of lithium-ion batteries, but the unfavorable factors such as structural phase transformation and low conductivity in the cycling process restrict the further improvement of battery performance, so the development and modification of high-performance cathode materials is a current research hotspot. LiNixCoyMn1-x-yO2(x≥0.6), a high-nickel cathode material, has the advantages of high platform potential, high energy density and low cost, and is an ideal material to achieve the goal of 300 Wh kg-1 and above for batteries. However, its poor thermal stability, poor interface stability, safety and life cannot meet the requirements of current power batteries. In this paper, the research status of high-nickel cathode materials is introduced, including bulk doping and surface coating, and the modification method of doping and coating integration is also introduced. Finally, we put forward the prospect of the future development trend of high-nickel cathode materials.