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High-performance gas sensors based on single-wall carbon nanotube random networks for detection of nitric oxide down to ppb-level

Abstract

We demonstrate highly sensitive and selective gas sensors based on solution-processed single-wall carbon nanotube (SWCNT) random networks for detection of nitric oxide (NO) down to ppb-level operating at room temperature. The proposed gas sensors exhibited a response of 50% in both inert and air atmospheres with a theoretical detection limit of 0.2 ppb and a selectivity toward different target gases of volatile organic compounds, including benzene, toluene, and ammonia. The outstanding sensing performance was realized by functionalizing SWCNT random networks with polyethylenimine (PEI), which possesses the repeating structure of the amine groups. We investigate the functionalization properties of SWCNT random networks by using atomic force microscopy, X-ray photoelectron spectroscopy and Raman spectroscopy and the sensing mechanism in the proposed NO gas sensors. We note that solution-process technologies, from deposition of SWCNT random networks to polymeric functionalization of amine group, were employed at room temperature in an ambient atmosphere to fabricate highly sensitivie and selective NO gas sensors, which is based on low-cost, effective, and scalable merits in industries of sensors. We also investigate the effect of ultraviolet (UV) irradiation on the recovery time underlying the sensing mechanism. Photodesorption energy obtained by UV irradiation reduced the recovery time of the proposed NO gas sensors within a few tens of seconds. We believe that this work is a promising and practical approach to realizing health-care monitoring systems by real-time analyzing NO gas at ppb level in the field of biosensors.

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Publication details

The article was received on 11 Sep 2018, accepted on 22 Nov 2018 and first published on 23 Nov 2018


Article type: Paper
DOI: 10.1039/C8NR07393G
Citation: Nanoscale, 2018, Accepted Manuscript
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    High-performance gas sensors based on single-wall carbon nanotube random networks for detection of nitric oxide down to ppb-level

    J. Jeon, B. Kang, Y. T. Byun and T. Ha, Nanoscale, 2018, Accepted Manuscript , DOI: 10.1039/C8NR07393G

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