ABSTRACT
Covalent triazine frameworks (CTFs) with tunable structure, fine molecular design and low cost have been regarded as a class of ideal electrode materials for lithium-ion batteries (LIBs). However, the tightly layered structure possessed by the CTFs leads to partial hiding of the redox active site, resulting in their unsatisfactory electrochemical performance. Herein, two CTFs (BDMI-CTF and TCNQ-CTF) with higher degree of structural distortion, more active sites exposed, and large lattice pores were prepared by dynamic trimerization reaction of cyano. As a result, BDMI-CTF as a cathode material for LIBs exhibits high initial capacity of 186.5 mAh/g at 50 mA g-1 and superior cycling stability without capacity loss after 2000 cycles at 1000 mA g-1 compared with TCNQ-CTF counterparts. Furthermore, based on their bipolar functionality, BDMI-CTF can be used as both cathode and anode materials for symmetric all-organic batteries (SAOBs), and this work will open a new window for the rational design of high performance CTF-based LIBs.
ABSTRACT
An appropriate detection network is required to extract building information in remote sensing images and to relieve the issue of poor detection effects resulting from the deficiency of detailed features. Firstly, we embed a transposed convolution sampling module fusing multiple normalization activation layers in the decoder based on the SegFormer network. This step alleviates the issue of missing feature semantics by adding holes and fillings, cascading multiple normalizations and activation layers to hold back over-fitting regularization expression and guarantee steady feature parameter classification. Secondly, the atrous spatial pyramid pooling decoding module is fused to explore multi-scale contextual information and to overcome issues such as the loss of detailed information on local buildings and the lack of long-distance information. Ablation experiments and comparison experiments are performed on the remote sensing image AISD, MBD, and WHU dataset. The robustness and validity of the improved mechanism are demonstrated by control groups of ablation experiments. In comparative experiments with the HRnet, PSPNet, U-Net, DeepLabv3+ networks, and the original detection algorithm, the mIoU of the AISD, the MBD, and the WHU dataset is enhanced by 17.68%, 30.44%, and 15.26%, respectively. The results of the experiments show that the method of this paper is superior to comparative methods such as U-Net. Furthermore, it is better for integrity detection of building edges and reduces the number of missing and false detections.
