Two-dimensional materials for gas sensing: bridging computational design and experimental synthesis.

Gas sensors hold critical application value in a wide range of scenarios, including indoor formaldehyde monitoring, volatile gas detection in laboratory environments, and explosive gas surveillance in industrial mining. Two-dimensional (2D) materials have attracted extensive attention for gas sensor construction owing to their ultrahigh surface-to-volume ratio and abundant surface-active sites. In this review, we aim to establish a robust linkage between experimental and computational methodologies. We firstly elaborate on the gas sensing mechanisms, and introduce the core metrics for evaluating gas-sensing performance from both experimental and computational perspectives. We then summarize representative target gas analytes and typical 2D gas-sensing materials investigated via the combination of experiments and computations, including graphene, phosphorene, MXenes, 2D transition metal dichalcogenides, and 2D metal oxides. We finally discuss the experimental and computational challenges facing 2D gas sensors, and present emerging data-driven strategies for gas sensor development. Furthermore, we outline the inherent limitations of state-of-the-art 2D materials in gas-sensing applications, and propose integrating machine learning methods to overcome the bottlenecks in the design and fabrication of high-performance gas sensors. Emphasis is placed on computational approaches that mimic realistic experimental conditions, with the goal of promoting the coordinated development of 2D gas sensors in both computational simulation and experimental measurement.