A flexible
electronic-nose (
E-nose) was constructed by assembling
graphene oxide (GO) using different types of
metal ions (Mx+) with different ratio of GO to Mx+. Owing to the cross-linked networks, the Mx+-induced assembly of
graphene films resulted in different porous structures. A chemi-resistive sensor array was constructed by coating the GO-M hybrid films on PET substrate patterned with 8 interdigited
electrodes, followed by in situ reduction of GO to rGO with
hydrazine vapor. Each of the sensing elements on the sensor array showed a cross-reactive response toward different types of
gases at room temperature. Compared to bare rGO, incorporation of
metal species into rGO significantly improved sensitivity owing to the additional interaction between
metal species and gas analyte. Principle component analysis (PCA) showed that four types of exhaled breath (EB)
biomarkers including
acetone,
isoprene,
ammonia, and hydrothion in sub-ppm concentrations can be discriminated well. To overcome the interference from humidity in EB, a protocol to collect and analyze EB
gases was established and further validated by simulated EB samples. Finally, clinical EB samples collected from patients with
lung cancer and healthy controls were analyzed. In a 106 case study, the healthy group can be accurately distinguished from
lung cancer patients by linear discrimination analysis. With the assistance of an artificial neural network, a sensitivity of 95.8% and specificity of 96.0% can be achieved in the diagnosis of
lung cancer based on the
E-nose. We also find that patients with
renal failure can be distinguished through comparison of dynamic response curves between patient and healthy samples on some specific sensing elements. These results demonstrate the proposed
E-nose will have great potential in noninvasive disease screening and personalized healthcare management.