Chemiresistive sensor arrays based on noncovalently functionalized multi-walled carbon nanotubes for ozone detection.

The table of contents entry A sensing scheme based on internet of thing to detect O 3 at room temperature is achieved by tailoring the sensing performances of MWCNTs with non-covalent functionalization. The sensor array based on MWCNTs makes the discriminative and sensitive detection of O 3 possible within 30 s at room temperature. • Multi-walled carbon nanotubes were noncovalently functionalized to develop the chemiresistive sensor array with the tailoring responses. • The sensor array displayed the excellent endurance to humidity and the good long-term stability. • The sensor array can identify O 3 , NO 2 and other analytes within 30 s in the real-world detection. • This method may lead to a valuable application based on internet of thing for the low-cost monitoring of gaseous pollutants. 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 (O 3). Compared with the MWCNTs, the responses of functionalized MWCNTs to 5 ppm of O 3 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 O 3 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 O 3 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. [ABSTRACT FROM AUTHOR]