Integration of Exfoliated WS2/Functionalized MWCNT Nanocomposites for NO2 Sensing Using Machine Learning for Response Prediction

In order to manage the environment and perform noninvasive disease diagnostics, it is necessary to continuously identify harmful and highly toxic gases, such as nitrogen dioxide (NO2). This study demonstrates how to design nanocomposites and build a cost-effective NO2 gas sensor based on exfoliated tungsten disulphide and functionalized multiwalled carbon nanotubes (f-MWCNTs) as a highly efficient sensing material operating at room temperature (RT) in humid conditions. The composite sensor's response under various humidity levels, ranging from 2% to 65%, as well as at different temperatures ( 25~^{\circ } C- 80~^{\circ } C), was studied. Scanning electron microscopy (SEM), Raman spectroscopy, transmission electron microscopy (TEM), and energy-dispersive X-ray spectroscopy (EDX) were used to analyze the sensing material. The composite-based sensor showed an improved response \Delta {R}/{R}_{{0}} of 52% at RT for 50-ppm NO2 with good selectivity to other gases (e.g., ammonia, methane, benzene, isobutene, and hydrogen). The composite sensor exhibited a low detection limit of 1.39 ppm for NO2 at RT. Furthering this advancement, we delve into the integration of machine learning, specifically the CatBoost regression model, with the NO2 sensor. This integration elevates the sensor from a conventional passive detector to an advanced analytical system, significantly boosting its predictive accuracy and adaptability for real-time environmental monitoring and nuanced data interpretation, thereby opening new frontiers in sensor technology and applications in environmental monitoring and health diagnostics.