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Uncertainty-Aware Deep Ensembles for Robust and Reliable Chemical Sensor Arrays.
Sungwoo Eo1, Ji-Hwan Eum1, Suk-Jeong Kwon1
1Department of Materials Science and Chemical Engineering, Hanyang University, Ansan, Republic of Korea.
Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|June 15, 2026
Summary
This study introduces a novel electronic nose for precise detection of sulfur gases like hydrogen sulfide (H₂S). The system utilizes a deep-ensemble learning framework for reliable breath and environmental monitoring.
Area of Science:
- Materials Science
- Chemical Sensing
- Artificial Intelligence
Background:
- Selective detection of volatile sulfur compounds (VSCs) is crucial for breath analysis and environmental monitoring.
- Conventional sensors struggle with cross-reactivity to similar sulfur-containing gases, limiting practical selectivity.
- Developing advanced sensing platforms is necessary to overcome these limitations.
Purpose of the Study:
- To develop a highly selective electronic nose for detecting and quantifying specific sulfur-containing gases.
- To address the challenge of cross-reactivity in conventional chemiresistive sensors.
- To create a reliability-aware VSC sensing platform for real-world applications.
Main Methods:
- Fabrication of a 15-channel multi-sensor array using metal oxide nanofibers decorated with diverse metal catalysts (Pt, Pd, Ir, Co).
- Utilized an intense pulsed light photothermal process for catalyst anchoring.
- Developed a deep-ensemble learning framework trained on comprehensive datasets under varying environmental conditions.
Main Results:
- Achieved selective detection of hydrogen sulfide (H₂S), methyl mercaptan (CH₃SH), and dimethyl sulfide ((CH₃)₂S).
- The deep-ensemble model accurately classified gas species and quantified concentrations.
- The system demonstrated predictive uncertainty, enabling reliability-aware sensing.
Conclusions:
- The developed deep-ensemble-assisted electronic nose offers a promising solution for selective VSC detection.
- This technology has significant potential for future applications in halitosis-related breath monitoring and environmental sensing.
- The reliability-aware approach enhances the trustworthiness of the sensing platform.
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