Computational screening of two-dimensional@three-dimensional metal-organic frameworks composites

Abstract

Metal-organic frameworks (MOFs), composed of organic linkers and inorganic metal nodes, are attracting attention in various fields due to their high surface area and micro-porosity. Although composite materials can lead to improved performance, developing new MOF composites requires a lot of resources, workforce, and time. Therefore, using computational methods to predict undiscovered MOF composites is a very efficient and time-saving way. In this research, an algorithm using geometrical information such as distance and angle was developed to quickly and accurately predict MOF composite candidates within having different dimensionalities. As a result, Three-Dimensional (3D) MOFs were screened to be matched with the Two-Dimensional (2D) MOFs (i.e. 2D MOF@3D MOF composite). Density Functional Theory (DFT) calculation was conducted to verify feasibility of the bonding through the interface. Finally, the computationally predicted 2D MOF@3D MOF composite materials were successfully synthesized and performed very well as chemiresistive sensor. This is the first case of predicting and synthesizing 2D MOF@3D MOF composites. Our research has an important implication on expanding the field of MOF composites by providing potentially synthesizable 2D MOF@3D MOF candidates.