Machine Learning‐Driven Ultrasensitive WSe2/MWCNT Hybrid‐Based E‐Nose Sensor Array for Volatiles Amines Mixture

Abstract Volatile amines in breath act as biomarkers for kidney and liver diseases. Monitoring these amines, especially when present as a mixture, provides insights into the metabolic state of the body. This study focuses on differentiating volatile amines by systematically modulating the conductivity and sensitivity of WSe 2 /Multiwalled Carbon Nanotubes composite‐based sensors. The fabricated chemiresistive sensor array demonstrates high selectivity, sensitivity (7.67% ppm −1 ), fast response and recovery kinetics (32 s/137 s), and accurate discrimination among target amines, even in the presence of other VOCs (volatile organic compounds). The sensor array operates at room temperature and achieves a theoretical limit of detection (LOD) of 387 ppt, 206, 157, and 202 ppb for triethylamine (TEA), dimethylamine (DMA), methylamine (MA), and ammonia (NH 3 ), respectively, demonstrating its suitability for breath sensing and diagnostics. Machine learning (ML) analysis is employed to differentiate between the volatile amines and mixtures with 94% accuracy. The ability to detect these amines at such low ppt and ppb levels underscores the potential of this e‐nose technology for high‐performance applications in early‐stage disease diagnostics.