Enhancing the sensitivity of chemiresistor gas sensors based on pristine carbon nanotubes to detect low-ppb ammonia concentrations in the environment
The possibility of using novel architectures based on carbon nanotubes (CNTs) for a realistic monitoring of the air quality in an urban environment requires the capability to monitor concentrations of polluting gases in the low-ppb range. This limit has been so far virtually neglected, as most of th...
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Published in | Analyst (London) Vol. 138; no. 24; pp. 7392 - 7399 |
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Main Authors | , , , , , , |
Format | Journal Article |
Language | English |
Published |
England
12.11.2013
|
Subjects | |
Online Access | Get full text |
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Summary: | The possibility of using novel architectures based on carbon nanotubes (CNTs) for a realistic monitoring of the air quality in an urban environment requires the capability to monitor concentrations of polluting gases in the low-ppb range. This limit has been so far virtually neglected, as most of the testing of new ammonia gas sensor devices based on CNTs is carried out above the ppm limit. In this paper, we present single-wall carbon nanotube (SWCNT) chemiresistor gas sensors operating at room temperature, displaying an enhanced sensitivity to NH
3
. Ammonia concentrations in air as low as 20 ppb have been measured, and a detection limit of 3 ppb is demonstrated, which is in the full range of the average NH
3
concentration in an urban environment and well below the sensitivities so far reported for pristine, non-functionalized SWCNTs operating at room temperature. In addition to careful preparation of the SWCNT layers, through sonication and dielectrophoresis that improved the quality of the CNT bundle layers, the low-ppb limit is also attained by revealing and properly tracking a fast dynamics channel in the desorption process of the polluting gas molecules.
A detection limit of 3 ppb for [NH
3
] in the environment is achieved by chemiresistor sensors based on pristine SWCNT. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0003-2654 1364-5528 |
DOI: | 10.1039/c3an01209c |