Fluctuation-enhanced sensing of organic solvent vapors mixture by machine learning

The data set consists of exemplary results of the product of voltage noise power spectral density S(f) multiplied by frequency f and normalized to squared DC voltage U^2 recorded in the graphene back-gated Field Effect Transistor under UV light assistance (275 nm) in the selected ambient atmospheres (Figure 3) and the results of gas detection by SVM algorithm: 1) chloroform (Figure 5), 2) acetonitrile (Figure 6), and predicted gas concentrations using various number of frequency bins (Figure 7, Figure 8). The demonstrated data reveals we can determine two gas components in the considered gas mixture (chloroform and acetonitrile) by utilizing flicker noise and the SVM detection algorithm. When we considered noise power spectra in the frequency range 0.5 Hz—2 kHz, the gas detection limit reached 2.9 ppm for chloroform and 49.5 ppm for acetonitrile.