High-Throughput Computational Screening of Two-Dimensional Covalent Organic Frameworks (2D COFs) for Capturing Radon in Moist Air

Abstract

Radon (Rn) and its decay products are the primary sources of natural ionizing radiation exposure for the public, posing significant health risks, including being a leading cause of lung cancer. Porous material-based adsorbents offer a feasible and efficient solution for controlling Rn concentrations in various scenes to achieve safe levels. However, due to competitive adsorption between Rn and water, finding candidates with a higher affinity and capacity for capturing Rn in humid air remains a significant challenge. Here, we conducted high-throughput computational screening of 8641 two-dimensional covalent organic frameworks (2D COFs) in moist air using grand canonical Monte Carlo simulations. We identified the top five candidates and revealed the structure–performance relationship. Our findings suggest that a well-defined cavity with an approximate spherical inner space, with a diameter matching that of Rn, is the structural basis for a proper Rn capturing site. This is because the excellent steric match between the cavity and Rn maximizes their van der Waals dispersion interactions. Additionally, the significant polarization electrostatic potential surface of the cavity can regulate the adsorption energy of water and ultimately impact Rn selectivity. Our study offers a potential route for Rn management using 2D COFs in moist air and provides a scientific basis for further experimentation.

Keywords

• radon capture
• two-dimensional covalent organic frameworks
• virtual screening
• structure–performance relationship
• GCMC simulations