Binder-free nanorice-like featured CuS@WS 2 structures have been synthesized using a simple and cost-effective chemical bath deposition approach and their application as electroactive material for high-performance supercapacitors. The surface properties of morphology, structure and composition of the as-prepared electrodes are examined using the scanning electron microscopy, transmission electron microscopy, X-ray diffraction and X-ray photoelectron spectroscopy, respectively. The nanorice-like featured CuS@WS 2 electrode exhibits nanorice-like structures, which provides the abundant active sites for redox reactions and facilitates the electrolyte diffusion. The electrochemical performance of the supercapacitor electrodes was examined by cyclic voltammetry and galvanostatic charge-discharge studies. From the electrochemical tests, the CuS@WS 2 electrode exhibits a higher specific capacitance (C s) of 887.15 F g −1 at a current density of 3.75 A g −1 with greater energy density and excellent rate capability compared to bare CuS (588.0 F g −1) and WS 2 (19.40 F g −1) electrodes. Overall, these results demonstrate that the as-synthesized CuS@WS 2 could be a promising material for next-generation high-performance electrochemical energy storage applications.
Data mining has become one of the most popular and new technology that it has gained a lot of attention in the recent times and with the increase in the popularity and the usage there comes a lot of issues/problems with the usage one of it Outlier detection and maintaining the datasets without the expected patterns. To identify the difference between Outlier and normal behavior we use key assumption techniques. We Provide the reverse nearest neighbor technique. There is a connection between the hubs and antihubs, outliers and the present unsupervised detection methods. With the KNN method it will be possible to identify and influence the outlier and antihub methods on real life datasets and synthetic datasets. So, From this we provide the insight of the Reverse neighbor count on unsupervised outlier detection.
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