AI-powered oriented synthesis of naphthalenediimide-based MOFs for photochromic encryption and ammonia sensing

Abstract

The oriented synthesis of metal–organic frameworks (MOFs) has been a goal that scientists strive to achieve. Exhaustive methods and orthogonal experiments are generally the most labor-intensive but also the most effective synthetic strategies. Herein, an artificial intelligence synthetic method powered by the generative large language model DeepSeek is adopted. Based on N,N′-bis(carboxymethyl)-1,4,5,8-naphthalenediimide (H₂CMNDI) and the 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 achieve the expected performance level 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, making them suitable 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.