Traffic congestion identification by combining PCA with higher-order Boltzmann machine

Modification of coral-like SnO2 nanostructures with dense TiO2 nanoparticles for a self-cleaning gas sensor

A coral-like SnO(2) nanostructure densely-modified with TiO(2) nanoparticles was reported for developing a self-cleaning gas sensor. The density of the TiO(2) nanoparticles in the TiO(2)/SnO(2) nanocomposites can be greatly improved via a plasma-based modification (PM) on SnO(2)/carbonaceous precursors before introducing Ti sources. In gas-sensing measurements, benzene and toluene were employed as target analytes. The results show that the gas sensor based on the TiO(2)/SnO(2) nanostructures with PM exhibits a remarkably improved stability after detecting for many cycles compared with the ones based on TiO(2)/SnO(2) without PM and pure SnO(2) nanostructures. The mechanism for the stable performance has been demonstrated from the self-cleaning degradation of TiO(2) nanoparticles towards the adsorbed organic species. Furthermore, the recognizable ability towards targets was also investigated by using some algorithmic recognition methods including principal component analysis (PCA) and nonnegative matrix factorization (NMF). The fascinating gas-sensing properties including enhanced stability, sensitivity, and recognizable ability enable the presented TiO(2)/SnO(2) nanocomposites to be a promising candidate for fabricating self-cleaning gas sensor which can be applied for detecting environmental gas contaminants.