Preparation, Properties, and Applications of 2D Janus Transition Metal Dichalcogenides

Structural symmetry significantly influences the fundamental characteristics of two-dimensional (2D) materials. In conventional transition metal dichalcogenides (TMDs), the absence of in-plane symmetry introduces distinct optoelectronic behaviors. To further enrich the functionality of such materials, recent efforts have focused on disrupting out-of-plane symmetry—often through the application of external electric fields—which leads to the generation of an intrinsic electric field within the lattice. This internal field alters the electronic band configuration, broadening the material’s applicability in fields like optoelectronics and spintronics. Among various engineered 2D systems, Janus transition metal dichalcogenides (JTMDs) have shown as a compelling class. Their intrinsic structural asymmetry, resulting from the replacement of chalcogen atoms on one side, naturally breaks out-of-plane symmetry and surpasses certain limitations of traditional TMDs. This unique arrangement imparts exceptional physical properties, such as vertical piezoelectric responses, pronounced Rashba spin splitting, and notable changes in Raman modes. These distinctive traits position JTMDs as promising candidates for use in sensors, spintronic devices, valleytronic applications, advanced optoelectronics, and catalytic processes. In this Review, we discuss the synthesis methods, structural features, properties, and potential applications of 2D JTMDs. We also highlight key challenges and propose future research directions. Compared with previous reviews, this work focusing on the latest scientific research breakthroughs and discoveries in recent years, not only provides an in-depth discussion of the out-of-plane asymmetry in JTMDs but also emphasizes recent advances in their synthesis techniques and the prospects for scalable industrial production. In addition, it highlights the rapid development of JTMD-based applications in recent years and explores their potential integration with machine learning and artificial intelligence for the development of next-generation intelligent devices.