The electrochemistry of size dependent graphene via liquid phase exfoliation: capacitance and ionic transport

Size-Dependent Graphene Support for Decorating Gold Nanoparticles as a Catalyst for Hydrogen Evolution Reaction with Machine Learning-Assisted Prediction

Size-dependent two-dimensional (2D) materials (e.g., graphene) have been recently used to improve their performance in various applications such as membrane filtration, energy storage, and electrocatalysts. It has also been demonstrated that 2D nanosheets can be one of the promising support materials for decorating nanoparticles (NPs). However, the optimum nanosheet size (lateral length and thickness) for supporting NPs has not yet been explored to enhance their catalytic performance. Herein, we elucidate the mechanism behind size-dependent graphene (GP) as a support due to which gold nanoparticles (AuNPs) are used as an active catalyst for the hydrogen evolution reaction (HER). Surprisingly, the decoration of AuNPs increased with the increasing nanosheet size, counter to what is widely reported in the literature (high surface area for smaller nanosheet size). We found that a large graphene nanosheet (lGP; ∼800 nm) used as the AuNP support (lGP/AuNPs) exhibited superior performance for the HER with long-term stability. The lGP/AuNPs with a suitable content of AuNPs provides a low overpotential and a small Tafel slope, being lower than that of other reported carbon-based HER electrocatalysts. This results from highly exposed active sites of well-dispersed AuNPs on lGP giving high conductivity. The laminar structure of the stacked graphene nanosheets and the high wettability of the lGP/AuNPs electrode surface also play crucial roles in enhancing electrolytes for penetration in the electrode, suggesting a highly electrochemical surface area. Moreover, machine learning (Random Forest) was also used to reveal the essential features of the advanced catalytic material design for catalyst-based applications.

Unlocking the Full Potential of Heteroatom-Doped Graphene-Based Supercapacitors through Stacking Models and SHAP-Guided Optimization

Graphene-based supercapacitors have emerged as a promising candidate for energy storage due to their superior capacitive properties. Heteroatom-doping is a method of improving the capacitive properties of graphene-based electrodes, but the optimal doping conditions and electrochemical properties are not yet fully understood due to the synergistic effects that occur. Many parameters, such as doping content, defects, specific surface area (SA), electrolyte, and more, could affect the capacitance (CAP). In this study, we use machine learning to solve these critical issues. We applied many models, such as Light Gradient Boost Machine, Extreme Gradient Boost, Polynomial Regression, Neural Network, Elastic Net, Lasso Regression, Ridge Regression, Random Forest, Support Vector Machine, K-Nearest Neighbors, Gradient Boost, AdaBoost, and Decision Tree, to find a suitable model for CAP prediction. Moreover, we enhance the prediction result by taking advantage of the top candidate model and creating a stacking concept (called "stacking models"). The SHAP value was used to identify the range of properties that affect CAP, and it was discussed in detail. Our results suggest that high-CAP graphene supercapacitors should have a large SA, with 4-5% nitrogen, 10-15% oxygen, high percentages of sulfur, a defect ratio close to 1, with acid electrolyte, and a low current density. These findings, along with the developed model and code, are expected to serve as a valuable computational tool for future electrochemical research from fundamental to applications.

A Review: Ion Transport of Two-Dimensional Materials in Novel Technologies from Macro to Nanoscopic Perspectives

Ion transport is a significant concept that underlies a variety of technologies including membrane technology, energy storages, optical, chemical, and biological sensors and ion-mobility exploration techniques. These applications are based on the concepts of capacitance and ion transport, so a prior understanding of capacitance and ion transport phenomena is crucial. In this review, the principles of capacitance and ion transport are described from a theoretical and practical point of view. The review covers the concepts of Helmholtz capacitance, diffuse layer capacitance and space charge capacitance, which is also referred to as quantum capacitance in low-dimensional materials. These concepts are attributed to applications in the electrochemical technologies such as energy storage and excitable ion sieving in membranes. This review also focuses on the characteristic role of channel heights (from micrometer to angstrom scales) in ion transport. Ion transport technologies can also be used in newer applications including biological sensors and multifunctional microsupercapacitors. This review improves our understanding of ion transport phenomena and demonstrates various applications that is applicable of the continued development in the technologies described.