MXene in the era of 2D materials: synthesis strategies, applications, and AI/ML-guided DoE

Two-dimensional (2D) materials have gained significant attention from researchers over the past few decades. MXene, known since 2011, is a family of 2D nanomaterials derived from transition metal carbides, nitrides, or carbonitride precursors known as the MAX phase. It exhibits exceptional electrical conductivity, tunable bandgaps, and outstanding electrochemical and mechanical properties. These characteristics make it one of the most rapidly growing families of 2D materials, promising a wide spectrum of applications. This review provides valuable insights into various synthesis methods and strategies, along with recent advancements. Some of the successful synthesis confirmatory advanced characterization techniques are also reported. Its prospective applications in energy storage (supercapacitors and Li-ion batteries) and optoelectronic (solar cells and photodetectors) sectors are thoroughly discussed. On the other hand, machine learning (ML), artificial intelligence (AI) and design of experiment (DoE) have become crucial for accelerating the development of MXene through predictive modeling and optimization. Hence, a valuable insight into the integration of MXene with these powerful approaches to streamline experimental efficiency is also discussed. Additionally, we have suggested some potential future possibilities in terms of synthesis approaches and commercialization. Therefore, this work aims to assist researchers by providing valuable references in the field of MXene in material science.