Machine Learning-Enabled Gas Sensor Based on MOF-Derived In₂O₃-CuO for Exhaled CO Detection

Noninvasive, precise breath analysis holds significant importance for the screening and monitoring of neonatal jaundice, with its core challenge lying in the specific detection of the key biomarker carbon monoxide (CO) within complex breath matrices. This research employed a controlled process to fabricate five sets of In₂O₃-CuO sensors derived from bimetallic metal-organic frameworks (Bi MOFs). These were assembled into a microsensor array, and by incorporating a linear discriminant analysis algorithm, an electronic nose system was constructed. This approach utilizes thermodynamic feature engineering to enhance data validity and optimize algorithm selection, thereby reducing reliance on large-scale data and computational resources. By integrating thermodynamically feature-driven machine learning, the electronic nose system-comprising merely five In₂O₃-CuO sensors-ultimately achieved accurate discrimination between CO and acetone. Concurrently, the single 3In₂O₃-CuO sensor exhibits excellent reproducibility, moisture resistance, and stability, with a low detection limit of 1 ppm and relatively rapid response/recovery times (5/21 s). By introducing machine learning algorithms to analyze the multidimensional response signals from the sensor array, the study successfully addressed the cross-sensitivity issue between CO and coexisting interfering gas C₃H₆O in clinical environments, achieving qualitative identification of CO ranging from 1 to 50 ppm (cross-validation accuracy reached 82%), providing a highly reliable technical platform for noninvasive screening of neonatal bilirubin metabolic abnormalities.