Advanced artificial algorithms integrated with p-type NiO nanofibers for health-hazardous gases classification

Cross-response remains a major challenge for semiconducting metal oxide resistive gas sensors, as interference from non-target gases often limits selectivity. In this study, NiO nanofibers (NFs) were synthesized via a simple electrospinning process. Thermogravimetric analysis indicated an optimal calcination temperature of 600 °C. Field-emission scanning electron microscopy imaging showed that the as-spun fibers had diameters of 200–300 nm, which decreased to 80–100 nm after calcination. Energy dispersive X-ray spectroscopy confirmed the presence of Ni and O in the NFs, while the Si signal originated from the Si/SiO2 substrate. X-ray diffraction analysis verified the formation of crystalline cubic-phase NiO. The gas-sensing performance of the NiO NF sensor was evaluated toward NO2 (1–10 ppm), acetone, ethanol (25–200 ppm), and H2 at operating temperatures of 350 °C–450 °C. Furthermore, an intelligent algorithm (principal component analysis) successfully classified the tested gases, demonstrating its potential to enhance gas identification and reduce cross-response in practical sensing applications.

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