Porous ZnO/Co3O4 nanofibers for low-temperature ppb-level acetone sensing and machine learning-assisted VOC discrimination

초록

Low-concentration acetone detection is important for environmental monitoring, industrial safety, and noninvasive breath analysis, but conventional ZnO-based sensors often suffer from limited response and slow kinetics at low operating temperatures. Herein, porous ZnO/Co3O4 composite nanofibers were prepared by electrospinning followed by thermal calcination, with controlled Co/Zn molar ratios to regulate surface oxygen chemistry and heterointerfacial charge modulation. The optimized CZ-2 sensor exhibited the best acetonesensing performance at 150 degrees C, delivering a high response of 66 toward 50 ppm acetone, a low estimated detection limit of 11 ppb, and fast response/recovery times of 10/36 s. The sensor also showed good selectivity, stable operation over 100 days with response fluctuation within 6%, and repeatable response over 50 consecutive cycles with fluctuation within 5%. Structural and electronic analyses indicate that the enhanced performance arises from the combined effects of porous one-dimensional transport channels, defect-related/adsorbed oxygen species, and ZnO/Co3O4 p-n heterointerfaces. These heterointerfaces amplify gas-induced resistance modulation while preserving ZnO-dominated n-type conduction. In addition, machine-learning analysis based on transient sensing features enabled effective discrimination of multiple volatile organic compounds, with the random forest model achieving approximately 96.0% accuracy. This work demonstrates an interface-engineered ZnO/Co3O4 nanofiber platform for sensitive low-temperature acetone detection and data-assisted gas recognition.

키워드