Synergy between Zeolite Framework and Encapsulated Sulfur for Enhanced Ion-Exchange Selectivity to Radioactive Cesium

To eliminate the radioisotope 137Cs+ from contaminated water, various inorganic ion-exchange materials have been developed. Many selective ion-exchange materials are relatively expensive and difficult to prepare, whereas conventional materials such as aluminosilicate zeolites lack ion-exchange selectivity in the presence of competing cations. Here, we report a simple but powerful strategy to significantly increase the Cs+ selectivity of conventional zeolites. We demonstrate that encapsulation of elemental sulfur in the micropores of zeolites (NaA, NaX, chabazite, and mordenite) via vacuum sublimation can remarkably increase the selectivity toward Cs+ in the presence of competing ions. It appears that the elemental sulfur does not provide additional adsorption sites for Cs+ ions but increases the ion-exchange selectivity toward Cs+ by providing additional interaction. Various analyses show that sulfur partially donates its electron to the ion-exchanged Cs+ cations in zeolites, indicating significant Lewis acid–base interaction. According to the hard soft acid base (HSAB) theory, the enhanced Cs+ ion-exchange selectivity can be explained by the fact that sulfur, a soft Lewis base, interacts more strongly with Cs+, which is a softer Lewis acid than other alkali and alkaline earth metal cations. Because of the high intrinsic Cs+ selectivity of bare zeolites and selectivity enhancement resulting from sulfur encapsulation, the sulfur-modified chabazite and mordenite showed highly promising Cs+ capture ability in the presence of various competing ions

Significant Roles of Carbon Pore and Surface Structure in AuPd/C Catalyst for Achieving High Chemoselectivity in Direct Hydrogen Peroxide Synthesis

Direct synthesis of hydrogen peroxide (H₂O₂) from hydrogen (H₂) and oxygen (O₂) has been widely investigated as an attractive way for small-scale/on-site H₂O₂ production. Among various catalysts, carbon-supported AuPd catalysts have been reported to exhibit the most promising H₂O₂ productivity and selectivity. In this work, to better understand the catalytic role of the surface properties and porous structures of the carbon supports, we systematically investigated AuPd catalysts supported on various nanostructured carbons including activated carbon, carbon nanotube, carbon black, and ordered mesoporous carbons. The results showed that a high density of oxygen functional groups on the carbon surface was essential for synthesizing highly dispersed bimetallic catalysts with effective AuPd alloying, which is a prerequisite for achieving high H₂O₂ selectivity. Regarding porous structure, a solely mesoporous carbon support was superior to microporous ones. Microporous carbons such as activated carbon suffered from diffusion limitation, leading to significantly slower H₂ conversion than mesoporous catalysts. Furthermore, H₂O₂ produced from AuPd catalyst in the micropores was more prone to subsequent disproportionation/hydrogenation into H₂O due to retarded diffusion of the H₂O₂ out of the microporous structure, which led to decreased H₂O₂ selectivity. The present study showed that solely mesoporous carbons with high surface oxygen content are most desirable as a support for AuPd catalyst in order to achieve high H₂O₂ productivity and selectivity

Molecular Dynamics Study of Silicon Carbide Using an Ab Initio-Based Neural Network Potential: Effect of Composition and Temperature on Crystallization Behavior

Structure and diffusion dynamics of silicon carbide (Si1–xCx) are investigated via molecular dynamics computer simulations with ab initio-based neural network potentials, exploring the effect of composition and temperature on crystallization behaviors. A neural network potential is developed to describe high-dimensional potential energy surfaces of silicon carbide (SiC) systems, reproducing first-principles results on their potential energies and forces. The phase behavior of amorphous Si1–xCx below its experimental melting point is systematically demonstrated by analyzing the structural and dynamic properties as a function of temperature and carbon concentration x in the composition range 0 ≤ x ≤ 0.5 and the temperature range T = 2000–2600 K, compared to available experiments. The phase of Si1–xCx is characterized by analyzing the pair correlation function, coordination number, tetrahedral order parameter, SiC tetrahedron fraction, Si disordered fraction, and excess entropy. Our results indicate that the system undergoes the crystallization by organizing the short- and medium-range order as the carbon content increases, where the critical carbon fraction for crystallization increases with temperature. The addition of carbon to silicon results in the phase separation into liquid Si and crystal SiC as well as the partial crystallization of Si1–xCx. The self-diffusivity of Si1–xCx is also evaluated to understand how the structural change caused by the crystallization works on diffusion dynamics. The diffusion dynamics of Si1–xCx becomes slower with increasing carbon content and decreasing temperature, which significantly slows down with onset of the crystallization

Molecular Dynamics Study of Silicon Carbide Using an Ab Initio-Based Neural Network Potential: Effect of Composition and Temperature on Crystallization Behavior

Structure and diffusion dynamics of silicon carbide (Si1–xCx) are investigated via molecular dynamics computer simulations with ab initio-based neural network potentials, exploring the effect of composition and temperature on crystallization behaviors. A neural network potential is developed to describe high-dimensional potential energy surfaces of silicon carbide (SiC) systems, reproducing first-principles results on their potential energies and forces. The phase behavior of amorphous Si1–xCx below its experimental melting point is systematically demonstrated by analyzing the structural and dynamic properties as a function of temperature and carbon concentration x in the composition range 0 ≤ x ≤ 0.5 and the temperature range T = 2000–2600 K, compared to available experiments. The phase of Si1–xCx is characterized by analyzing the pair correlation function, coordination number, tetrahedral order parameter, SiC tetrahedron fraction, Si disordered fraction, and excess entropy. Our results indicate that the system undergoes the crystallization by organizing the short- and medium-range order as the carbon content increases, where the critical carbon fraction for crystallization increases with temperature. The addition of carbon to silicon results in the phase separation into liquid Si and crystal SiC as well as the partial crystallization of Si1–xCx. The self-diffusivity of Si1–xCx is also evaluated to understand how the structural change caused by the crystallization works on diffusion dynamics. The diffusion dynamics of Si1–xCx becomes slower with increasing carbon content and decreasing temperature, which significantly slows down with onset of the crystallization