Rapid detection of volatile organic compounds (VOCs)
is growing
in importance in many sectors. Noninvasive medical diagnoses may be
based upon particular combinations of VOCs in human breath; detecting
VOCs emitted from environmental hazards such as fungal growth could
prevent illness; and waste could be reduced through monitoring of
gases produced during food storage. Electronic noses have been applied
to such problems, however, a common limitation is in improving selectivity.
Graphene is an adaptable material that can be functionalized with
many chemical receptors. Here, we use this versatility to demonstrate
selective and rapid detection of multiple VOCs at varying concentrations
with graphene-based variable capacitor (varactor) arrays. Each array
contains 108 sensors functionalized with 36 chemical receptors for
cross-selectivity. Multiplexer data acquisition from 108 sensors is
accomplished in tens of seconds. While this rapid measurement reduces
the signal magnitude, classification using supervised machine learning
(Bootstrap Aggregated Random Forest) shows excellent results of 98%
accuracy between 5 analytes (ethanol, hexanal, methyl ethyl ketone,
toluene, and octane) at 4 concentrations each. With the addition of
1-octene, an analyte highly similar in structure to octane, an accuracy
of 89% is achieved. These results demonstrate the important role of
the choice of analysis method, particularly in the presence of noisy
data. This is an important step toward fully utilizing graphene-based
sensor arrays for rapid gas sensing applications from environmental
monitoring to disease detection in human breath.