Combined density functional theory and non-equilibrium Green’s function method study on graphene-based gas sensors for detection of food quality

Usage of nano-sensors to detect quality of food is an emerging field. A recent experiment on reduced graphene oxide (r-GO) inferred that polymerization of r-GO is necessary to discriminate various volatile organic compounds (VOC), the markers for detecting stage of degradation of food products. Motivated by this, using a combination of density functional theory and non-equilibrium Green’s function, we have investigated in detail the capability of monolayer graphene, r-GO and GO as sensors to detect quality of standard food products like vegetable, fruit, and meat. We assess the sensitivity and selectivity of these 2D materials as chemiresistive as well as work function-based sensors. We find that pristine graphene performs poorly while r-GO is able to differentiate between four, out of six VOCs (acetone, dimethylsulfide, ethanol, methanol, methylacetate, toluene), both as chemiresistive and work function-based sensor. GO, on the other hand, performs at par with r-GO as work function-based sensor but is not useful as chemiresistive one. We show that such behavior can be traced back to the changes in the electronic structures of the 2D materials upon adsorption of the VOCs. We infer that the discrepancy between our results and the experiment in the context of the performance of r-GO sensor can be due to the limitations in the experimental method of reducing Graphene.