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 NH3. 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 NH3 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 sensor array based on heterojunctions between semiconducting organic layers and single walled carbon nanotube (SWCNT) films was produced to explore applications in breathomics, the molecular analysis of exhaled breath. The array was exposed to gas/volatiles relevant to specific diseases (ammonia, ethanol, acetone, 2-propanol, sodium hypochlorite, benzene, hydrogen sulfide, and nitrogen dioxide). Then, to evaluate its capability to operate with real relevant biological samples the array was exposed to human breath exhaled from healthy subjects. Finally, to provide a proof of concept of its diagnostic potential, the array was exposed to exhaled breath samples collected from subjects with chronic obstructive pulmonary disease (COPD), an airway chronic inflammatory disease not yet investigated with CNT-based sensor arrays, and the results were compared to those from of healthy subjects breathprints. Principal component analysis showed that the sensor array was able to detect various target gas/volatiles with a clear fingerprint on a 2D subspace, was suitable for breath profiling in exhaled human breath, and was able to distinguish subjects with COPD from healthy subjects based on their breathprints. This classification ability was further improved by selecting the most responsive sensors to nitrogen dioxide, which has been proposed as a biomarker of COPD.
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