AI-Powered Oriented Synthesis of Naphthalenediimide-based MOFs for Photochromic Encryption and Ammonia Sensing

Abstract

The oriented synthesis of metal-organic frameworks (MOFs) is always the goal that scientists strive for. Exhaustive method and orthogonal experiment are generally the most labor-intensive but also the most effective synthetic strategies. An artificial intelligence synthetic method that powered by generative large language model DeepSeek is adopted. Based on N, N’-bis(carboxymethyl)-1, 4, 5, 8-naphthalenediimide (H₂CMNDI) and transition metals ions Zn(II) and Cd(II), we synthesized a series of novel MOFs. Conventional microanalysis methods were employed to analyze the structure and properties of the MOFs. The synthesized MOFs meet our expected performance and exhibit stimulus-responsive activity. They possess recoverable photochromic properties, rapidly darkening significantly under ultraviolet light and then regaining their original color upon heating and being kept away from light in a static state, which can be used for encryption applications. Additionally, the Zn-MOF can detect ammonia water sensitively, with a limit of detection as low as 3.28 μM. The synthesis and application of this series of MOFs are an excellent exploration and a good example of the oriented synthesis of MOFs.