Our paper titled “Identifying optimal amorphous materials for fluoride removal through Monte Carlo and neural network modeling” has been published online in the journal “Adsorption”, accessible at https://doi.org/10.1007/s10450-024-00496-1
To mitigate CF4’s greenhouse impact in microelectronics, we used Monte Carlo simulations and neural networks to screen over 100 amorphous materials for N2/CF4 gas adsorption. Materials with densities of 0.7-1.0 g/cm³ and pore sizes of 1.4-1.6 Å showed high CF4 adsorption. HCP-Colina-id0016 and aCarbon-Bhatia-id001 were top performers. For N2/CF4 separation, HCP-Colina-id0016 is recommended at 4500 kPa, and aCarbon-Bhatia-id001 at below 500 kPa. The GA-BP neural network model outperformed standalone BP in predicting CF4 adsorption.
“Adsorption” is a comprehensive resource tailored for scientists, engineers, and technologists, offering peer-reviewed content on both fundamental and applied aspects of adsorption and related fields. The journal covers a wide range of topics including the mathematics, thermodynamics, chemistry, and physics of adsorption, as well as practical applications such as processes, models, engineering, and equipment design.
“Adsorption” is indexed in the Science Citation Index (SCI), boasting a 2023 impact factor of 3.3 and a CiteScore of 6.4. The journal is ranked in Zone 1 of the CiteScore Journal Ranking under Chemical Engineering, underscoring its high quality and strong reputation in the field.