Comparative Statistical Analysis of AI-Enhanced Sensors Functionalized with Diverse Nanomaterials: Sensitivity, Accuracy, and Environmental Resilience

The integration of artificial intelligence (AI) with nanotechnology has revolutionized sensor technology, driving advancements in environmental monitoring, medical diagnostics, and industrial automation. By leveraging nanomaterials such as graphene, carbon nanotubes (CNTs), metal oxides (e.g., ZnO, SnO₂), molybdenum disulfide (MoS₂), and MXenes, sensor performance has been significantly enhanced in terms of sensitivity, accuracy, and environmental resilience. This study conducts a comprehensive statistical analysis of AI-driven sensors, utilizing a dataset comprising ۳۰۷ experimental trials derived from peer-reviewed literature published between ۲۰۱۸ and ۲۰۲۴. Employing advanced statistical methods, including Analysis of Variance (ANOVA), Principal Component Analysis (PCA), and multiple regression, we evaluate key performance metrics: sensitivity (ppm⁻¹), accuracy (%), response time (ms), and robustness under varying environmental conditions such as temperature and humidity. Our findings underscore the exceptional performance of hybrid nanomaterials, particularly CNT-graphene composites, when coupled with deep learning architectures such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) models. Furthermore, we highlight the critical need for material-specific calibration and adaptive AI frameworks to ensure operational reliability in dynamic environments. This study advocates for standardized testing protocols and the establishment of open-access datasets to promote reproducibility and scalability, providing a robust foundation for the future development of AI-driven nanosensor technologies.

Keywords:

AI sensors , nanomaterials , sensitivity , accuracy , environmental resilience , PCA , ANOVA , deep learning , hybrid nanostructures