Benzene, toluene, and xylene, commonly known as BTX, are hazardous aromatic organic vapors with high toxicity towards living organisms. Many techniques are being developed to provide the community with portable, cost effective, and high performance BTX sensing devices in order to effectively monitor the quality of air. In this paper, we study the effect of decorating graphene with tin oxide (SnO2) or tungsten oxide (WO3) nanoparticles on its performance as a chemoresistive material for detecting BTX vapors. Transmission electron microscopy and environmental scanning electron microscopy are used as morphological characterization techniques. SnO2-decorated graphene displayed high sensitivity towards benzene, toluene, and xylene with the lowest tested concentrations of 2 ppm, 1.5 ppm, and 0.2 ppm, respectively. In addition, we found that, by employing these nanomaterials, the observed response could provide a unique double signal confirmation to identify the presence of benzene vapors for monitoring occupational exposure in the textiles, painting, and adhesives industries or in fuel stations.

Many research works report a sensitive detection of a wide variety of gas species. However, their in-lab detection is usually performed by using single gases and, therefore, selectivity often remains an unsolved issue. This paper reports a four-sensor array employing different nano-carbon sensitive layers (bare graphene, SnO2@Graphene, WO3@Graphene, and Au@CNTs). The different gas-sensitive films were characterised via several techniques such as FESEM, TEM, and Raman. First, an extensive study was performed to detect isolated NO2, CO2, and NH3 molecules, unravelling the sensing mechanism at the operating temperatures applied. Besides, the effect of the ambient moisture was also evaluated. Afterwards, a model for target gas identification and concentration prediction was developed. Indeed, the sensor array was used in mixtures of NO2 and CO2 for studying the cross-sensitivity and developing a calibration model. As a result, the NO2 detection with different background levels of CO2 was achieved with an R2 of 0.987 and an RMSE of about 22 ppb.