ABSTRACT
As a new-generation photoelectric material, perovskites have attracted researchers' attention due to their excellent optoelectronic properties. However, the existence of defects inevitably causes structural degradation and restricts their performance, which need to be further improved by post-treatment. At present, post-treatments mostly focus on non-contact treatments, which may constrain the effect since the influence on the perovskites caused by the direct contact is much more straightly. Therefore, we proposed an annealing strategy of straight manipulation in a solvent atmosphere with the assistance of polyimide (PI) tape for the perovskite post-treatment, due to the high heat resistance and less glue residual of this tape. It casts an influence on the perovskite directly, proving the possibility of the straight manipulation by operators, promoting the recrystallization of the perovskite grains and removing the impurity substance. The optimized Pb-free perovskite film exhibits a better X-ray sensitivity of 7.5 × 104 µC Gyair-1 cm-2 and a great detection limit of 47 nGyair s-1, which is comparable to advanced Pb-based perovskite X-ray detectors and all commercial ones. The new annealing strategy provides a facile, effective, and simple method to improve the perovskite quality, exhibiting the potential and harmlessness of the direct contact post-treatment, which paves the way for a broader application of perovskites.
ABSTRACT
Tin-based anode materials with high theoretical specific capacity are subject to huge volume expansion and poor reaction reversibility, leading to degradation of battery performance. Herein, the steric-hindrance effect and self-sacrificing template behavior of polydopamine were firstly developed to induce the formation of hollow nanospheres assembled by ultrafine SnO2 quantum dots (SnO2-QDs) and nitrogen-doped carbon (NC), containing residual polydopamine (PDA) cores. The PDA@SnO2-QDs/NC hollow nanospheres could effectively accommodate the volume expansion and maintain structural stability. More importantly, the PDA core could capture oxygen free radicals produced by the charge/discharge process and be involved in the evolution of the SEI layer, achieving enhanced electrochemical reaction kinetics. The optimized PDA@SnO2-QDs/NC anode shows a specific capacity of 898 mAh g-1 after 300 cycles at 0.3 A g-1, and scarcely capacity attenuation after 1500 cycles at 1 A g-1. The long-cyclic life is up to 3000 cycles at 3 A g-1. Even after 200 cycles, the anode in the PDA@SnO2-QDs/NC||LFP full battery gives a reversible capacity of 489 mAh g-1 at 0.3 A g-1, with a capacity retention of 77 %. This work casts new light on tin-based anode materials and interface optimization.
ABSTRACT
The detection and classification of white blood cells (WBCs, also known as Leukocytes) is a hot issue because of its important applications in disease diagnosis. Nowadays the morphological analysis of blood cells is operated manually by skilled operators, which results in some drawbacks such as slowness of the analysis, a non-standard accuracy, and the dependence on the operator's skills. Although there have been many papers studying the detection of WBCs or classification of WBCs independently, few papers consider them together. This paper proposes an automatic detection and classification system for WBCs from peripheral blood images. It firstly proposes an algorithm to detect WBCs from the microscope images based on the simple relation of colors R, B and morphological operation. Then a granularity feature (pairwise rotation invariant co-occurrence local binary pattern, PRICoLBP feature) and SVM are applied to classify eosinophil and basophil from other WBCs firstly. Lastly, convolution neural networks are used to extract features in high level from WBCs automatically, and a random forest is applied to these features to recognize the other three kinds of WBCs: neutrophil, monocyte and lymphocyte. Some detection experiments on Cellavison database and ALL-IDB database show that our proposed detection method has better effect almost than iterative threshold method with less cost time, and some classification experiments show that our proposed classification method has better accuracy almost than some other methods.
