Uncertainty-Aware Deep Ensembles for Robust and Reliable Chemical Sensor Arrays.

Selective detection of sulfur-containing gases such as hydrogen sulfide (H2S), methyl mercaptan (CH3SH), and dimethyl sulfide ((CH3)2S) 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 (SnO2, Co3O4, and WO3) 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.