Enhancing Selectivity of Two-Dimensional Materials-Based Gas Sensors

Two-dimensional (2D) materials have emerged as promising candidates for gas sensing applications due to their exceptional electrical, structural, and chemical properties, which enable high sensitivity and rapid response to gas molecules. However, despite their potential, 2D material-based gas sensors face a significant challenge in achieving adequate selectivity, as many sensors respond similarly to multiple gases, leading to cross-sensitivity and inaccurate detection. This review provides a comprehensive overview of the recent advancements for improving the selectivity of 2D gas sensors. It explores material modification strategies, such as functionalizing the sensing components and tuning adsorption dynamics, to enhance selective gas interactions. Engineering approaches, including field-effect modulation and sensor array design, are also discussed as effective methods to fine-tune sensor performance. Additionally, the integration of machine learning (ML) algorithms is highlighted for their potential to differentiate among multiple analytes. Prospects for further improving selectivity through material optimization, sensor calibration, and drift compensation are explored, along with the incorporation of smart sensing systems into the Internet of Things (IoT). This review outlines key objectives and strategies that pave the way for next-generation gas sensors with enhanced selectivity, reliability, and versatility, poised to impact a wide range of applications from environmental monitoring to industrial safety.