# Reduced Graphene Oxide-Metalloporphyrin Sensors for Human Breath Screening
## 4 Discussion  
In this study, room-temperature-operating RGO-metalloporphyrin VOC sensors were assembled using a simple and low-cost fabrication method. The RGO thin films were noncovalently functionalized by metalloporphyrin molecules via \(\pi\)-\(\pi\) stacking (Figure 9a). We showed the cross-sensing behavior of RGO-metalloporphyrin sensors when exposed to the disease-related VOCs in human breath. Different sensing properties of the RGO-metalloporphyrin sensors were enabled by different metalloporphyrin molecules (Figure 9b). To be specific, metalloporphyrin with different transition metals at the center of the porphyrin ring provided different sensitivity and selectivity to different gas molecules through various interaction mechanisms of hydrogen bonds, polarization, and polarity interaction [55]. The sensors' higher sensitivity to ammonia can be explained by the synergistic sensing behavior of RGO and metalloporphyrin. The RGO contains oxygen functional groups, including carboxyl, epoxides, and hydroxyl groups on the graphene sheets (Figure 9a). It was demonstrated that the oxygen functional groups effectively improve ammonia absorption on RGO films [56]. It should be mentioned that recovery response measurements were difficult with our static-sensing test setup because mechanical openings of the chamber door affected the electrical measurements. Future work will use a dynamic gas-sensing system, with mass flow controllers, to precisely control the gas flow for sensor recovery tests. The thickness of RGO thin films was \(\sim\)10 \(\upmu\)m. The thickness variation in \(\upmu\)m-thick films is not expected to affect the sensing response because only the outer surface of the RGO films becomes active for gas detection reactions [57]. Figure 8: ML-based identification of kidney health using the signal intensity of the developed sensors. (**a,b**) illustrate the Precision-Recall and ROC curves with corresponding areas under the curves (AUC) of 0.97 and 0.96 when a SVM with linear kernel is used as the ML method under 5-fold cross-validation settings. Unlike a selective VOC sensor, a cross-sensitive-type VOC sensor responds to multiple gaseous compounds, and selectivity is achieved through pattern recognition methods [21, 58, 59]. Therefore, finding an appropriate pattern recognition method is a crucial process to detect and discriminate gas analytes of interest [60]. Despite the importance of pattern recognition and classification analysis, many studies on nanomaterial-based cross-sensitive VOC sensors focused on designing the VOC sensors and visualizing the sensing data (mainly using PCA) [17, 37, 61, 62, 63], and integration with classification algorithms was limited. This study provided a set of processes that can be applied to develop VOC sensors and improve their VOC discrimination performance through a comparative study on different ML methods. For pattern recognition analysis, we applied two different features (\(R_{norm}\) and \(Area\)) from the sensing response curves of each sensor (eight features in total). \(R_{norm}\) reflects the reaction degree change in sensors to gas, and \(Area\) describes a transient response feature that might represent the cumulative total of the reaction degree [64, 65]. By considering different features (\(R_{norm}\) and \(Area\)) from the response curve, information about different aspects of the reactions can be obtained [64]. PCA analysis showed that all eight features have comparable importance for explaining variability (see PC1 weights in Table S1), while \(R_{norm}\) and \(Area\) of MnTPP showed slightly higher importance with PC1 weights of \(-0.36\). This implies that all four sensors and their features played an important role in explaining variance, and a single feature cannot be used solely for VOC discrimination [66]. ML algorithms of LR, SVM, and ANN were applied to detect three different VOCs and carbon monoxides, and their performance was compared.