RESUMEN
Storage capacity, average open circuit voltage (OCV), diffusion barrier, lattice parameter changes, etc. are key indicators of whether a material would be suitable for use as a Li-ion or non-Li-ion battery (LIB or NLIB) anode. The rapid development of 2D materials over the past few decades has opened up new possibilities for these metrics. Using first-principles calculations, we prove that two 2D materials, TiB4 and SrB8, show excellent performance in terms of the above metrics when used as anodes for LIBs or NLIBs. As detailed, TiB4 has an Li\Na\K\Ca storage capacity of 588 mA h g-1, 588 mA h g-1, 588 mA h g-1, and 1176 mA h g-1, respectively, and SrB8 has an Li\Na\K\Ca storage capacity of 308 mA h g-1, 308 mA h g-1, 462 mA h g-1, and 616 mA h g-1, respectively, and they show good electrical conductivity whether existing Li, Na, K or Ca is adsorbed or not. The diffusion barriers on both surfaces are low, indicating good rate performance. The average OCV is also very low. In particular, the lattice parameters of the two materials change very little during the embedding of Li\Na\K\Ca. For Ti9B36 the corresponding values are about 0.37% (Li), 0.33% (Na), 0.64% (K) and 0.03% (Ca), and for Sr8B64 the corresponding values are about 0.70% (Li), 0.16% (Na), 0.13% (K) and 0.004% (Ca), which imply zero strain-like character and great cycling performance. All the above results show that TiB4 and SrB8 monolayers are very promising Li\Na\K\Ca ion battery anodes.
RESUMEN
Compared to wide-field telescopes, small-field detection systems have higher spatial resolution, resulting in stronger detection capabilities and higher positioning accuracy. When detecting by small fields in synchronous orbit, both space debris and fixed stars are imaged as point targets, making it difficult to distinguish them. In addition, with the improvement in detection capabilities, the number of stars in the background rapidly increases, which puts higher requirements on recognition algorithms. Therefore, star detection is indispensable for identifying and locating space debris in complex backgrounds. To address these difficulties, this paper proposes a real-time star extraction method based on adaptive filtering and multi-frame projection. We use bad point repair and background suppression algorithms to preprocess star images. Afterwards, we analyze and enhance the target signal-to-noise ratio (SNR). Then, we use multi-frame projection to fuse information. Subsequently, adaptive filtering, adaptive morphology, and adaptive median filtering algorithms are proposed to detect trajectories. Finally, the projection is released to locate the target. Our recognition algorithm has been verified by real star images, and the images were captured using small-field telescopes. The experimental results demonstrate the effectiveness of the algorithm proposed in this paper. We successfully extracted hip-27066 star, which has a magnitude of about 12 and an SNR of about 1.5. Compared with existing methods, our algorithm has advantages in both recognition rate and false-alarm rate, and can be used as a real-time target recognition algorithm for space-based synchronous orbit detection payloads.
