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We report on the synthesis and CO gas-sensing properties of mesoporous tin(IV) oxides (SnO2). For the synthesis cetyltrimethylammonium bromide (CTABr) was used as a structure-directing agent; the resulting SnO2 powders were applied as films to commercially available sensor substrates by drop coating. Nitrogen physisorption shows specific surface areas up to 160 m2·g-1 and mean pore diameters of about 4 nm, as verified by TEM. The film conductance was measured in dependence on the CO concentration in humid synthetic air at a constant temperature of 300 °C. The sensors show a high sensitivity at low CO concentrations and turn out to be largely insensitive towards changes in the relative humidity. We compare the materials with commercially available SnO2-based sensors.
The RoboCup Logistics League (RCLL) is a robotics competition in a production logistics scenario in the context of a Smart Factory. In the competition, a team of three robots needs to assemble products to fulfill various orders that are requested online during the game. This year, the Carologistics team was able to win the competition with a new approach to multi-agent coordination as well as significant changes to the robot’s perception unit and a pragmatic network setup using the cellular network instead of WiFi. In this paper, we describe the major components of our approach with a focus on the changes compared to the last physical competition in 2019.
Having well-defined control strategies for fuel cells, that can efficiently detect errors and take corrective action is critically important for safety in all applications, and especially so in aviation. The algorithms not only ensure operator safety by monitoring the fuel cell and connected components, but also contribute to extending the health of the fuel cell, its durability and safe operation over its lifetime. While sensors are used to provide peripheral data surrounding the fuel cell, the internal states of the fuel cell cannot be directly measured. To overcome this restriction, Kalman Filter has been implemented as an internal state observer.
Other safety conditions are evaluated using real-time data from every connected sensor and corrective actions automatically take place to ensure safety. The algorithms discussed in this paper have been validated thorough Model-in-the-Loop (MiL) tests as well as practical validation at a dedicated test bench.