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.
Abstract. A 1-month field campaign of ozone (O3) flux measurements along a
five-level vertical profile above, inside and below the canopy was run in a
mature broadleaf forest of the Po Valley, northern Italy. The study aimed to
characterize O3 flux dynamics and their interactions with
nitrogen oxides (NOx) fluxes from the forest soil and the
atmosphere above the canopy. Ozone fluxes measured at the levels above the
canopy were in good agreement, thus confirming the validity of the constant
flux hypothesis, while below-canopy O3 fluxes were lower than
above. However, at the upper canopy edge O3 fluxes were
surprisingly higher than above during the morning hours. This was attributed
to a chemical O3 sink due to a reaction with the nitric oxide (NO)
emitted from soil and deposited from the atmosphere, thus converging at the
top of the canopy. Moreover, this mechanism was favored by the morning
coupling between the forest and the atmosphere, while in the afternoon the
fluxes at the upper canopy edge became similar to those of the levels above
as a consequence of the in-canopy stratification. Nearly 80 % of the
O3 deposited to the forest ecosystem was removed by the canopy by
stomatal deposition, dry deposition on physical surfaces and by ambient
chemistry reactions (33.3 % by the upper canopy layer and 46.3 % by the
lower canopy layer). Only a minor part of O3 was removed by the
understorey vegetation and the soil surface (2 %), while the remaining
18.2 % was consumed by chemical reaction with NO emitted from soil. The
collected data could be used to improve the O3 risk assessment for
forests and to test the predicting capability of O3 deposition
models. Moreover, these data could help multilayer canopy models to separate
the influence of ambient chemistry vs. O3 dry deposition on the
observed fluxes.
The present study is focused on the implementation of a novel, low cost, urban grid of nanostructured chemresistor gas sensors for ammonia concentration ([NH(3)]) monitoring, with NH(3) being one of the main precursors of secondary fine particulate. Low-cost chemresistor gas sensors based on carbon nanotubes have been developed, their response to [NH(3)] in the 0.17-5.0 ppm range has been tested, and the devices have been properly calibrated under different relative humidity conditions in the 33-63% range. In order to improve the chemresistor selectivity towards [NH(3)], an Expert System, based on fuzzy logic and genetic algorithms, has been developed to extract the atmospheric [NH(3)] (with a sensitivity of a few ppb) from the output signal of a model chemresistor gas sensor exposed to an NO(2), NO(X) and O(3) gas mixture. The concentration of these pollutants that are known to be the most significant interfering compounds during ammonia detection with carbon nanotube gas sensors has been tracked by the ARPA monitoring network in the city of Milan and the historical dataset collected over one year has been used to train the Expert System.
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