# Reduced Graphene Oxide-Metalloporphyrin Sensors for Human Breath Screening
## 3 Results
### RGO-Metalloporphyrin Sensors Showed Unique Sensing Responses upon Exposure to VOCs  
The VOC-sensing performance of RGO-based sensors was investigated by measuring the electrical resistance response when the sensors were exposed to four different gas analytes. As mentioned in Section 2.4, three different VOCs of acetone, isopropanol, and ammonia, and carbon monoxide were considered. Concentrations of breath biomarkers are summarized in Table 1. Acetone and isopropanol have been reported as VOC biomarkers for diabetes. High levels of acetone exhalation have been reported in type 1 diabetes (2200-21,000 ppb) when compared with healthy individuals (500-2000 ppb) [2; 47]. Isopropanol is a byproduct of acetone metabolism which is induced upon excess ketone production that can occur due to reduced glucose levels in the liver of diabetic patients with insulin deficiency or insulin resistance [48; 49]. Ammonia in the exhaled breath of chronic kidney disease patients is associated with high levels of blood urea nitrogen [50]. In  
Figure 3: Machine learning (ML) models of (**a**) logistic regression (LR), (**b**) support vector machine (SVM), and (**c**) artificial neural networks (ANN) are described.  
a healthy liver, ammonia and ammonium ions are converted into urea. On the other hand, urea is unable to be removed in renal patients, resulting in excessive ammonia buildup. Carbon monoxide has been reported as an indicator of chronic obstructive pulmonary disease [51]. In addition, healthy smokers had higher carbon monoxide concentrations (17.13 \(\pm\) 8.5 ppm) compared with healthy non-smokers (3.61 \(\pm\) 2.15 ppm) [52].  
Figure 5a shows a representative sensing response of four different RGO-metalloporphyrin films subjected to 5 ppm of ammonia. \(R_{norm}\) increased as all sensors were exposed to ammonia. The slope of the transient response curve varied among the four different sensors indicating different ammonia sensitivities. The \(R_{norm}\) values (t\(\sim\)1000 s) of RGO-metalloporphyrin sensors in four different gas analytes are shown in a radar plot (Figure 5b) indicating the unique sensing responses of the sensors to different gaseous compounds.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Exhaled Biomarkers** & **Healthy (ppb)** & **Disease (ppb)** \\ \hline Type 1 diabetes & Acetone & 500–2000 [2] & 2200–21,000 [2] \\  & Isopropanol & 784 (287–8963) [53] & 1223 (481–15,011) [53] \\ Chronic kidney & Ammonia & 356 (290–412) [45] & 3863 (828–11,570) [45] \\ disease & & 3610 \(\pm\) 2150 (healthy & 17,130 \(\pm\) 8500 \\ Smoking status & Carbon monoxide & non-smoker) [52] & (healthy smoker) [52] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Exhaled breath biomarkers for different health conditions.  
Figure 4: The SEM images describe the morphology of (**a**) a GO film and (**b**) an RGO film. The EDS spectra of (**c**) a GO film and (**d**) an RGO film are shown.  
Figure 6 shows the sensing responses of four different RGO-metalloporphyrin sensors under three different concentrations of gas analytes. A monotonic increase in \(R_{norm}\) was observed upon exposure of each sensor to higher gas analyte concentrations. The correlation coefficients (CC) of \(R_{norm}\) and VOC concentration were calculated to measure the strength of their linear relationship. The CCs of all four sensors were > 0.97, showing a linear relationship between \(R_{norm}\) and VOC concentration.