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.

Liquid Phase Exfoliation of 2D Materials and Its Electrochemical Applications in the Data-Driven Future

“…The work mentioned here is for pristine (ultralow oxygen content) graphene not “graphene oxide” or “reduced graphene oxide”. This is due to graphene oxide and its derivatives, which can provide high pseudocapacitive properties; however, the electrochemical performance depends on the doped species and its concentration . It is obvious that graphene itself cannot be an outstanding electrode material for energy storage if it was standalone.…”

“…However, the underlying effects of its electrochemical features which relate to optimization of capacitance is not fully understood. Jitapunkul et al proposed a simple approach based on Pearson correlation analysis, curve fitting, and scattered interpolation-extrapolation which apply to gathered experimental data to investigate effects of significant features related to capacitive boosting of heteroatom-doped graphene. The positive correlation between specific surface area and specific capacitance can be clearly noted for all single heteroatom-doped graphene, double heteroatom-doped graphene, and triple heteroatom-doped graphene, except for O/S codoped graphene which may be due to a limited number of gathered data.…”

Virtual Special Issue on Machine Learning in Physical Chemistry Volume 2

“…In JPC C , the special issue papers describe ML and other data science techniques utilized in the scope of nanoparticles and nanostructures; surface and interface processes; electron, ion, and thermal transport; optic, electronic, and optoelectronic materials; and catalysts and catalysis; as well as energy conversion and storage materials and processes. As in the Parts A and B, a number of articles directly address either the development of new methods or the use of ML methods in new ways. − Several contributions relate to methods in the use or development of so-called machine learning potentials (MLP) or machine learning interaction potentials (MLIP), including a Perspective on improving these models authored by Maxson et al, as well as other contributions. − Interfaces and related phenomena are addressed in several articles. − Nanomaterials and their properties are also prominently featured. − Another large category in JPC C includes studies that address the calculation of properties of materials for wide-ranging applications. − …”