Computational Screening of Metal-Organic Frameworks (MOFs) for Agrochemicals

In modern agriculture applications, agrochemicals, such as pesticides and herbicides, have been commonly used to maximize crop yield and minimize environmental damage. However, it should be noted that limitless usage can lead to harmful side effects from the environment to the human health. Metal-Organic Frameworks (MOFs), a class of porous materials made from metal ions and organic molecules, offer a novel solution to this area[1] due to their high surface area and tunable properties. In the literature, there are 99 075 MOFs[2]. Once we consider the laboratory conditions, testing all these materials will be not efficient in the laboratory conditions. In this project, we will employ various computational techniques to evaluate the performance of various MOFs to capture agrochemicals. Firstly, high-throughput computational screening will be performed to identify the promising candidates without a need for extensive laboratory testing, saving time and resources. The best promising materials will then be analyzed and investigated detailly to better understand their adsorption kinetics. This work demonstrates the potential of in silico methods in advancing agricultural technologies and offers a pathway to improve a better crop protection with reduces environmental impact and to develop next-generation agrochemicals. By using MOFs for agrochemicals, we aim to contribute to more sustainable farming practices, ultimately leading to increased food security and environmental protection.
 
References:
[1] Rojas, S., Rodríguez-Diéguez, A., and Horcajada, P., 2022. Metal–organic frameworks in agriculture. ACS Appl. Mater. Interfaces, 14(15), 16983-17007.
[2] Moghadam, P.Z., Li, A., Liu, X.W., Bueno-Perez, R., Wang, S.D., Wiggin, S.B., Wood, P.A. and Fairen-Jimenez, D., 2020. Targeted classification of metal–organic frameworks in the Cambridge structural database (CSD). Chem. Sci., 11(32), 8373-8387.