Abstract

Carbon nanotubes are considered as one of the leading candidate materials for the high-performance, internet of thing (IoT)-based gas sensor operating at room temperature. Multi-walled carbon nanotubes (MWCNTs) as a sensing platform were noncovalently functionalized by six kinds of functional modifiers to discriminatively detect ozone (O3). Compared with the MWCNTs, the responses of functionalized MWCNTs to 5 ppm of O3 were improved by about 68.8–258.3% and the highest response reached 34.4%. The maximum response of functionalized MWCNTs to 100% relative humidity was just 5.6%, displaying the excellent endurance to humidity. After two months, the decrease in the response value of the most sensitive sensor to O3 was no more than 15%, showing the good long-term stability. Furthermore, the sensor array was optimized according to principal component analysis and achieved the discriminative detection of six analytes in 30 s at room temperature. For the most sensitive sensor of the sensor array, the theoretical limit of detection for O3 is determined to be 24.2 ppb. Importantly, it could be coupled with the image recognition and cloud computing for a rapid diagnostic detection of gaseous pollutants under the background of the rapid development of IoT.

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