We demonstrate high performance gas sensors based on functionalized single-wall carbon nanotube random networks for the detection of nitric oxide down to the ppb-level.

This paper presents a new pattern recognition approach for enhancing the selectivity of gas sensor arrays for clustering intelligent odor detection. The aim of this approach was to accurately classify an odor using pattern recognition in order to enhance the selectivity of gas sensor arrays. This was achieved using an odor monitoring system with a newly developed neural-genetic classification algorithm (NGCA). The system shows the enhancement in the sensitivity of the detected gas. Experiments showed that the proposed NGCA delivered better performance than the previous genetic algorithm (GA) and artificial neural networks (ANN) methods. We also used PCA for data visualization. Our proposed system can enhance the reproducibility, reliability, and selectivity of odor sensor output, so it is expected to be applicable to diverse environmental problems including air pollution, and monitor the air quality of clean-air required buildings such as a kindergartens and hospitals.

In this study, we demonstrated a highly selective chemiresistive-type NO₂ gas sensor using facilely prepared carbon dot (CD)-decorated single-walled carbon nanotubes (SWCNTs). The CD-decorated SWCNT suspension was characterized using transmission electron microscopy (TEM), X-ray diffraction (XRD), and UV-visible spectroscopy, and then spread onto an SiO₂/Si substrate by a simple and cost-effective spray-printing method. Interestingly, the resistance of our sensor increased upon exposure to NO₂ gas, which was contrary to findings previously reported for SWCNT-based NO₂ gas sensors. This is because SWCNTs are strongly doped by the electron-rich CDs to change the polarity from p-type to n-type. In addition, the CDs to SWCNTs ratio in the active suspension was critical in determining the response values of gas sensors; here, the 2:1 device showed the highest value of 42.0% in a sensing test using 4.5 ppm NO₂ gas. Furthermore, the sensor selectively responded to NO₂ gas (response ~15%), and to other gases very faintly (NO, response ~1%) or not at all (CO, C₆H₆, and C₇H₈). We propose a reasonable mechanism of the CD-decorated SWCNT-based sensor for NO₂ sensing, and expect that our results can be combined with those of other researches to improve various device performances, as well as for NO₂ sensor applications.

We demonstrate highly sensitive and selective chemiresistive-type NO gas detection using defected single-walled carbon nanotubes (SWCNTs) decorated with N-[3-(trimethoxysilyl)propyl]ethylene diamine (en-APTAS) molecules. The defected SWCNTs were prepared via furnace annealing at 700 °C and confirmed by transmission electron microscopy. A single en-APTAS molecule has two amine groups acting as adsorption sites for NO gas, which can improve the NO response. The NO response was further enhanced when the defected SWCNTs were utilized because NO sensing reactions could occur on both the inner and outer walls of the defected SWCNTs. The en-APTAS decoration improved the NO response of the SWCNT-based gas sensing devices by 2.5 times; when the defected SWCNTs were used, the NO response was further improved by 3 times. Meanwhile, the recovery performance in a time-resolved response curve was significantly improved (45 times) via a simple rinsing process with ethanol. Specifically, the fabricated device did not respond to carbon monoxide (CO) or BTEX gas (i.e., a mixture of benzene, toluene, ethyl benzene, and xylene), indicating its high selectivity to NO gas. The results show the possibility of a high-performance SWCNT-based NO gas sensor applicable to healthcare fields requiring ppb-level detection, such as in vitro diagnostics (IVDs) of respiratory diseases.

This paper demonstrates the enhanced NO₂ sensing performance of graphene with defects generated by rapid thermal annealing (RTA). A high temperature of RTA (300–700 °C) was applied to graphene under an argon atmosphere to form defects on sp² carbon lattices. The density of defects proportionally increased with increasing the RTA temperature. Raman scattering results confirmed significant changes in sp² bonding. After 700 °C RTA, I_D/I_G, I_2D/I_G, and FWHM (full width at half maximum)(G) values, which are used to indirectly investigate carbon-carbon bonds’ chemical and physical properties, were markedly changed compared to the pristine graphene. Further evidence of the thermally-induced defects on graphene was found via electrical resistance measurements. The electrical resistance of the RTA-treated graphene linearly increased with increasing RTA temperature. Meanwhile, the NO₂ response of graphene sensors increased from 0 to 500 °C and reached maximum (R = ~24%) at 500 °C. Then, the response rather decreased at 700 °C (R = ~14%). The results imply that rich defects formed at above a critical temperature (~500 °C) may damage electrical paths of sp² chains and thus deteriorate NO₂ response. Compared to the existing functionalization process, the RTA treatment is very facile and allows precise control of the NO₂ sensing characteristics, contributing to manufacturing commercial low-cost, high-performance, integrated sensors.