A comprehensive review on MoSe(2) nanostructures with an overview of machine learning techniques for supercapacitor applications

对 MoSe(2) 纳米结构进行全面综述,并概述机器学习技术在超级电容器应用中的应用。

阅读:1

Abstract

In the past few decades, supercapacitors (SCs) have emerged as good and reliable energy storage devices due to their impressive power density, better charge-discharge rates, and high cycling stability. The main components of a supercapacitor are its electrode design and composition. Many compositions are tested for electrode preparations, which can provide good performance. Still, research is widely progressing in developing optimum high-performance electrodes. Metal chalcogenides have recently gained a lot of interest for application in supercapacitors due to their intriguing physical and chemical properties, unique crystal structures, tuneable interlayer spacings, broad oxidation states, etc. MoSe(2), belonging to the family of Transition Metal Dichalcogenides (TMDs), has also been well explored recently for application in supercapacitors due to its similar properties to 2D materials. In this review, we briefly discuss supercapacitors and their classification. Various available synthesis routes for MoSe(2) preparation are summarized. A detailed assessment of the electrochemical performances of different MoSe(2) composites, including cyclic voltammetry (CV) analysis and galvanostatic charge-discharge (GCD) analysis, is given for symmetric and asymmetric supercapacitors. The limitations of MoSe(2) and its composites are mentioned briefly. The use of machine learning methods and algorithms for supercapacitor applications is discussed for forecasting valuable details. Finally, a summary is provided, along with conclusions.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。