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The network approach towards the analysis of the dynamics of complex systems has been successfully applied in a multitude of studies in the neurosciences and has yielded fascinating insights. With this approach, a complex system is considered to be composed of different constituents which interact with each other. Interaction structures can be compactly represented in interaction networks. In this contribution, we present a brief overview about how interaction networks are derived from multivariate time series, about basic network characteristics, and about challenges associated with this analysis approach.
In this work, a multi-sensor chip for the investigation of the sensing properties of different types of metal oxides towards hydrogen peroxide in the ppm range is presented. The fabrication process and physical characterization of the multi-sensor chip are described. Pure SnO2 and WO3 as well as Pd- and Pt-doped SnO2 films are characterized in terms of their sensitivity to H2O2. The sensing films have been prepared by drop-coating of water-dispensed nano-powders. A physical characterization, including scanning electron microscopy and X-ray diffraction analysis of the deposited metal-oxide films, was done. From the measurements in hydrogen peroxide atmosphere, it could be shown, that all of the tested metal oxide films are suitable for the detection of H2O2 in the ppm range. The highest sensitivity and reproducibility was achieved using Pt-doped SnO2.
Calibration plot of a SnO2, WO3, Pt-, and Pd-doped SnO2 gas sensor for H2O2 concentrations in the ppm range.