Insights into binding mechanisms of size-selected graphene binders for flexible and conductive porous carbon electrodes

Role of Transition Metal Dichalcogenides as a Catalyst Support for Decorating Gold Nanoparticles for Enhanced Hydrogen Evolution Reaction

The two-dimensional (2D) transition metal dichalcogenides (TMDs) have been widely used in various electrochemical applications, such as electrocatalysts, sensors, and energy storage. They have been potentially demonstrated not only as catalysts but also as supporting materials for boosting catalytic performance and durability. However, the different types of TMD nanosheets (transition metals and chalcogenide atoms) for supporting nanoparticles have not yet been investigated for electrocatalytic performance. Herein, we provide mechanistic insights into the hydrogen evolution reaction (HER) of various TMDs (i.e., MoS2, MoSe2, and WSe2) as catalyst supports for the decoration of gold nanoparticles (AuNPs), which represent an active catalyst. Among various TMD supports, it was found that the MoS2 supports with an optimal amount of AuNPs loading (MoS2/AuNPs) exhibited excellent catalytic activity (low overpotential and Tafel slope), which is better than that of other TMD supports and the previously reported TMD-based support. This is due to well-dispersed AuNPs with the charge transfer of Au-MoS2 interaction (increasing n-type), leading to highly active sites for HER performance. Moreover, the perfect laminar stacking of the MoS2/AuNPs electrode, providing high porosity and good wettability, plays an important role in enhancing the ability of ionic electrolytes to infiltrate through the electrode area (up to ∼50 F g-1). The MoS2/AuNPs exhibit long-term stability with no disintegration of the electrode when performing the HER at ultrahigh current density (>200 mA cm-2) for over 24 h. This work aims to deepen the understanding of TMD materials as catalyst supports, and is advantageous for the development of catalyst-based applications.

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.

Tuning Surface Energy to Enhance MoS2 Nanosheet Production via Liquid-Phase Exfoliation: Understanding the Electrochemical Adsorption of Cesium Chloride

Environmental pollution caused by radionuclides like Cs-137 and Cs-134 has increased global attention toward public health. Electrochemical adsorption has emerged as a feasible, rapid, and scalable method to treat contaminated water sources. However, graphene and its derivatives have limitations in ion adsorption via physisorption, forming a double layer that restricts the electrode's adsorption capacity. To address this, we propose the use of molybdenum disulfide (MoS2) with its extensive intercalation galleries of MoS2 nanosheets for cesium removal via an electrochemical route. Liquid-phase exfoliation with water and N-methyl-2-pyrrolidone (NMP) was then used to produce MoS2 nanosheets in a scalable quantity (high-yield production). The formation of a mixed solvent possessing relatively equivalent surface energy for exfoliation enabled us to achieve a remarkable exfoliation yield of up to ca. 1.26 mg mL-1, which is one of the highest yields reported to date (without a surfactant being added) and to the best of our knowledge. The 35% v/v of water in NMP displayed a maximum yield while maintaining the structure of the as-exfoliated one. Water exceeding over 66.7% v/v led to the formation of MoO3. Moreover, an insight into the cesium ion removal mechanism through the electrochemical route was demonstrated. It is found that the Cs+ removal follows electrochemical intercalation rather than adsorption. This work aids the understanding of cesium intercalation coupled with a mass-scale production method, which should lead to more efficient and cost-effective removal of radionuclides from contaminated water sources, opening new research avenues in materials and environmental science.

Oil palm leaf-derived hierarchical porous carbon for "water-in-salt" based supercapacitors: the effect of anions (Cl- and TFSI-) in superconcentrated conditions

This study investigates the use of a hierarchical porous carbon electrode derived from oil palm leaves in a "water-in-salt" supercapacitor. The impact of anion identity on the electrical performance of the carbon electrode was also explored. The results show that the prepared carbon had a hierarchical porous structure with a high surface area of up to 1840 m2 g-1. When a 20 m LiTFSI electrolyte was used, the carbon electrode had a specific capacitance of 176 F g-1 with a wider potential window of about 2.6 V, whereas the use of a cheaper 20 m LiCl electrolyte showed a higher specific capacitance of 331 F g-1 due to the smaller size of the Cl- anion, which enabled inner capacitance. Therefore, the anion identity has an effect on the electrochemical performance of porous carbon, and this research contributes to the understanding of using "water-in-salt" electrolytes in carbon-based supercapacitors. The study's findings provide insights into developing low-cost, high-performance supercapacitors that can operate in a wider voltage range.

Ultrahigh stable laminar graphene membranes for effective ionic and molecular nanofiltration with a machine learning-assisted study

Graphene oxide (GO) membranes have gained great attention for water purification due to the formation of stacked nanosheets giving nanocapillary channels. Unlike graphene, the interlayer spacing of GO membranes gets readily expanded in aqueous solution due to their high oxygen content, resulting in poor ion rejection. Herein, we prepared ultralow oxygen-containing graphene (∼1 at%) via facile liquid-phase exfoliation which was formed as membrane laminates. The graphene membranes exhibited ultrahigh stability with no observed swelling or deformation of the laminar structure when kept in water, aqueous salt solutions, and various pH solutions for over one week. The membranes with a high degree of tortuous nanocapillary channels can efficiently reject the ions found in seawater as well as various charged dye molecules. This indicates that the graphene membranes exhibited ionic and molecular sieving properties due to the effect of size exclusion obtained from the narrow nanocapillary channel and electrostatic repulsion from negatively charged graphene nanosheets. Moreover, we also demonstrated machine learning to gain insights into the membrane performance, which allowed us to obtain membrane optimization as a model for water purification technology.