Insights into Heteroatom-Doped Graphene Supercapacitor Data through Manual Data Separation and Statistical Analysis

Abstract

The integration of data science with graphene-based supercapacitors has emerged as a promising approach for optimizing their performance. Graphene-based electrodes exhibit unique pseudo-capacitive properties, but optimizing their supercapacitors requires balancing multiple variables, including electrode features, electrolyte composition, and operating conditions. This is because the pseudo-capacitive effect can create different charge-storage mechanisms via surface redox reactions during charging/discharging. In this paper, we discuss the use of statistical data analysis for gaining insights into the complex interplay between material properties and electrochemical performance. We shed more light on the analysis of large datasets generated from experiments of heteroatom-doped graphene supercapacitors using data preprocessing techniques, such as scattered interpolation–extrapolation and Pearson correlation, instead of traditional machine learning models. We investigate the effects of various electrochemical features on capacitive performance and the influence of heteroatom doping, such as nitrogen, sulfur, and oxygen. Our analysis provides valuable insights into the essential electrochemical features and heteroatom doping that relate to capacitive-boosting performance, serving as a guideline for accelerating the development of energy storage, particularly for graphene-based supercapacitors, in both experimental and machine learning aspects.