Uncertainty‐Aware Deep Ensembles for Robust and Reliable Chemical Sensor Arrays

ABSTRACT Selective detection of sulfur‐containing gases such as hydrogen sulfide (H ₂ S), methyl mercaptan (CH ₃ SH), and dimethyl sulfide ((CH ₃ ) ₂ S) is important for halitosis‐related breath and environmental monitoring. However, conventional metal‐oxide chemiresistive sensors show high cross‐reactivity toward these chemically similar species, which limits their practical selectivity. To address this, we present a deep‐ensemble‐assisted electronic nose based on catalytic metal nanoparticles‐decorated metal oxide nanofiber arrays. A 15‐channel multi‐sensor array was fabricated by anchoring diverse metal catalysts (Pt, Pd, Ir, and Co) onto metal oxide nanofiber scaffolds (SnO ₂ , Co ₃ O ₄ , and WO ₃ ) through an intense pulsed light based photothermal process. Based on comprehensive datasets collected from a 15‐channel multi‐sensor array under varying gas species, concentrations, operating temperatures, and humidity levels, we developed a deep‐ensemble learning framework. This architecture simultaneously performs gas‐species classification and concentration quantification while providing predictive uncertainty, thereby demonstrating a proof‐of‐concept reliability‐aware platform for VSC sensing with potential relevance to future breath‐related and environmental monitoring applications.