A comprehensive review on tungsten oxide nanostructures‐based electrochromic supercapacitors and machine learning models for design and process parameter optimization

Abstract

Electrochromism has received significant research recently due to its unique electrochromic feature for various applications such as smart buildings, smart doors, e‐skins, display devices, and so forth. Transition metal oxides are the major choice as electrode materials for electrochromic devices. Among the various electrochromic oxides available so far, tungsten oxide (WO3) achieves great interest due to its peculiar properties such as high coloration efficiency, low‐cost, high stability, and so forth. WO3 is an example of cathodic electrochromic material in which the material gets color under ion insertion. In this review article, we discuss the recent developments in WO3‐based electrochromic supercapacitors. Initially, the synthesis and characterizations of WO3 are discussed, and later the electrochromic performance evaluation using various electrochemical characterization tools, such as cyclic voltammetry, galvanostatic charge/discharge cycling, and so on, was reviewed. Furthermore, various theoretical models that describe the performance of electrochromic supercapacitors are explained in detail. Finally, the future perspectives of WO3‐based electrochromic supercapacitors are discussed. The most recent advances in machine learning (ML) are being utilized to investigate the mechanism and enhance the strategic performance of WO3 in the field of electrochromic. There is ongoing research in developing ML models for predicting the optimal design of electrochromic supercapacitors in terms of both material selection and performance parameters. Such models are essential in the pursuit of next‐generation smart windows, which are integral to the construction of sustainable buildings and the creation of a more environment‐friendly planet.

Strategies to Enrich Electrochemical Sensing Data with Analytical Relevance for Machine Learning Applications: A Focused Review

“…Since the early 2010s, the integration of machine learning into electrochemical research has been embarked on in a wide range of fields such as energy generation/storage [ 1 , 2 , 3 , 4 ], bio/chemical analysis [ 5 , 6 , 7 ], fundamental electrochemistry [ 8 , 9 ], and environmental sustainability [ 10 ] ( Figure 1 A). The main goals of machine learning applications are, from a material point of view, to improve the functionality of materials utilized in electrochemical research and, from an analysis point of view, to facilitate data processing and interpretation [ 11 ].…”