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Composite NOx sensors were fabricated by combining partially and fully stabilized yttria-doped zirconia with alumina forming a composite electrolyte, Y2O3-ZrO2-Al2O3, and strontium-doped lanthanum manganese oxide mixed with gold to form the composite sensing electrode, La0.8 Sr0.2MnO3-Au. A surface chemistry analysis of the composite sensor was conducted to interpret defects and the structural phases present at the Y2O3-ZrO2-Al2O3 electrolyte, as well as the charge conduction mechanism at the LaSrMnO3-Au electrode surface. Based on the surface chemistry analysis, ionic and electronic transport properties, and microstructural features of sensor components, the working principle was described for NOx sensing at the composite sensor. The role of the composite materials on the NOx sensing response, cross-sensitivity to O2, H2O, CO, CO2, and CH4, and the response/recovery rates relative to sensor accuracy were characterized by operating the composite NOx sensors via the impedimetric method. The composite sensors were operated at temperatures ranging from 575 to 675 °C in dry and humidified gas environments with NO and NO2 concentrations varying from 0 to 100 ppm, where the balance gas was N2. It was found that the microstructure of the composite NOx sensor electrolyte and sensing electrode had a significant effect on interfacial reactions at the triple phase boundary, as well as the density of active sites for oxygen reactions. Overall, the composite NOx sensor microstructure enabled a high NOx sensing response, along with low cross-sensitivity to O2, CO, CO2, and CH4, and promoted NO detection down to 2 ppm.

Ubiquitous data flow through a directed complex network requires the complete structural controllability of the network. For evaluating the structural controllability of any network, determination of maximum matching in the network is a cardinal task and has always been a problem of immense concern. Its solution is mandatory in structural control theory for controlling real world complex networks. The existing classical approach through the Hopcroft-Karp algorithm and other proposed algorithms require the determination of the bipartite equivalent graph (i.e., network), which belongs to the NP-complete class of problems. In this article, we propose a degree-first greedy search algorithm to determine maximum matching in unipartite graphs without determining its bipartite equivalent. Thus this classical problem of the NP-Complete class can be solved using the heuristic, with ISSN 1943-3581 2014 2 www.macrothink.org/npa reduced complexity. This algorithm can be efficiently used to find maximum matching in most of the real world complex networks that follow Erdős-Rényi model. Simulation results obtained using our heuristic reveal that dense and homogenous networks can be controlled with fewer controller nodes popularly termed as driver nodes, compared to the sparse inhomogeneous networks. Network Protocols and Algorithms