Modeling sustainable photocatalytic degradation of acidic dyes using Jordanian nano-Kaolin-TiO and solar energy: Synergetic mechanistic insights.

The abstract highlights the global issue of environmental contamination caused by organic compounds and the exploration of various methods for its resolution. One such approach involves the utilization of titanium dioxide (TiO) as a photocatalyst in conjunction with natural adsorption materials like kaolin. The study employed a modeling-based approach to investigate the sustainable photocatalytic degradation of acidic dyes using a Jordanian nano-kaolin-TiO composite material and solar energy.

Density Functional Theory Unveils the Secrets of SiAuF and SiCuF: Exploring Their Striking Structural, Electronic, Elastic, and Optical Properties.

In the quest for advanced materials with diverse applications in optoelectronics and energy storage, we delve into the fascinating world of halide perovskites, focusing on SiAuF and SiCuF. Employing density functional theory (DFT) as our guiding light, we conduct a comprehensive comparative study of these two compounds, unearthing their unique structural, electronic, elastic, and optical attributes. Structurally, SiAuF and SiCuF reveal their cubic nature, with SiCuF demonstrating superior stability and a higher bulk modulus.

Structural, Electronic, Elastic, and Optical Characteristics of AgZF (Z = Sb and Bi) Fluoro-Perovskites: Using a Computational Approach for Energy Generation.

This research is being conducted to learn more about various compounds and their potential uses in various fields such as renewable energy, electrical conductivity, the study of optoelectronic properties, the use of light-absorbing materials in photovoltaic device thin-film LEDs, and field effect transistors (FETs). AgZF3 (Z = Sb, Bi) compounds, which are simple, cubic, ternary fluoro-perovskites, are studied using the FP-LAPW and low orbital algorithm, both of which are based on DFT. Structure, elasticity and electrical and optical properties are only some of the many features that can be predicted.