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An isomorphous series of 10 microporous copper-based metal–organic frameworks (MOFs) with the general formulas ∞3[{Cu3(μ3-OH)(X)}4{Cu2(H2O)2}3(H-R-trz-ia)12] (R = H, CH3, Ph; X2– = SO42–, SeO42–, 2 NO32– (1–8)) and ∞3[{Cu3(μ3-OH)(X)}8{Cu2(H2O)2}6(H-3py-trz-ia)24Cu6]X3 (R = 3py; X2– = SO42–, SeO42– (9, 10)) is presented together with the closely related compounds ∞3[Cu6(μ4-O)(μ3-OH)2(H-Metrz-ia)4][Cu(H2O)6](NO3)2·10H2O (11) and ∞3[Cu2(H-3py-trz-ia)2(H2O)3] (12Cu), which are obtained under similar reaction conditions. The porosity of the series of cubic MOFs with twf-d topology reaches up to 66%. While the diameters of the spherical pores remain unaffected, adsorption measurements show that the pore volume can be fine-tuned by the substituents of the triazolyl isophthalate ligand and choice of the respective copper salt, that is, copper sulfate, selenate, or nitrate.
This Master's Thesis discusses intelligent sensor networks considering autonomous sensor placement strategies and system health management. Sensor networks for an intelligent system design process have been researched recently. These networks consist of a distributed collective of sensing units, each with the abilities of individual sensing and computation. Such systems can be capable of self-deployment and must be scalable, long-lived and robust. With distributed sensor networks, intelligent sensor placement for system design and online system health management are attractive areas of research. Distributed sensor networks also cause optimization problems, such as decentralized control, system robustness and maximization of coverage in a distributed system. This also includes the discovery and analysis of points of interest within an environment. The purpose of this study was to investigate a method to control sensor placement in a world with several sources and multiple types of information autonomously. This includes both controlling the movement of sensor units and filtering of the gathered information depending on individual properties to increase system performance, defined as a good coverage. Additionally, online system health management was examined in this study regarding the case of agent failures and autonomous policy reconfiguration if sensors are added to or removed from the system. Two different solution strategies were devised, one where the environment was fully observable, and one with only partial observability. Both strategies use evolutionary algorithms based on artificial neural networks for developing control policies. For performance measurement and policy evaluation, different multiagent objective functions were investigated. The results of the study show that in the case of a world with multiple types of information, individual control strategies performed best because of their abilities to control the movement of a sensor entity and to filter the sensed information. This also includes system robustness in case of sensor failures where other sensing units must recover system performance. Additionally, autonomous policy reconfiguration after adding or removing of sensor agents was successful. This highlights that intelligent sensor agents are able to adapt their individual control policies considering new circumstances.