Machine Learning Assisted Enhancement in a Two-Dimensional Material’s Sensing Performance

Abstract

Two-dimensional (2D) materials have seen a dramatic increase in use in recent years due to their exceptional characteristics, which make them perfect for a wide range of sensing applications. However, achieving optimal sensing performance in 2D material-based sensors often poses challenges owing to material limitations and environmental factors. The combination of ML algorithms with 2D materials offers a way to maximize selectivity, sensitivity, and overall sensor dependability. The study starts by looking at the basic characteristics of many 2D materials and their uses in sensing, such as graphene and transition metal dichalcogenides (TMDs). It then explores the difficulties encountered by conventional sensing techniques and shows how machine learning (ML) techniques overcome these difficulties. A thorough examination of the various machine learning methods used with 2D materials is provided, along with an explanation of their functions in data processing, pattern identification, and real-time adaptive sensing. The paper also discusses how ML might lead to better performance measures including lower false positive rates and higher accuracy. Comprehensive analysis is done on case studies that demonstrate effective implementations in many sensing domains, such as industrial applications, environmental monitoring, and healthcare. In conclusion, the abstract discusses prospects for the future, highlighting how machine learning-assisted 2D material sensors are developing and how they might transform sensing technologies in a variety of fields.