Silver nanoclusters (AgNCs) were first coated with bovine serum albumin (BSA) and then encapsulated into porous metal-organic frameworks of ZIF-8 by the protein-mediated biomineralization process. Unexpectedly, the fluorescence intensities of the yielded AgNCs-BSA@ZIF-8 nanocomposites were discovered to be continuously enhanced during each of the BSA coating and ZIF-8 encapsulation steps. Compared to common AgNCs, greatly improved photostability and storage stability of AgNCs could also be expected. More importantly, having benefited from the ZIF-8 shells, the prepared nanocomposites could possess the specific accumulation and sensitive response to Cu ions, resulting in the rational quenching of their fluorescence intensities. Moreover, AgNCs-BSA@ZIF-8 nanocomposites were coated onto the hydrophobic arraying slides toward a microdots array-based fluorimetric method for the fast and sensitive evaluation of Cu ions. It was discovered that the developed fluorimetric strategy could ensure the high-throughput analysis of Cu ions in wide pH range, and especially some harsh and high-salt media. It can allow for the detection of Cu ions in blood with the concentrations ranging from 4.0 × 10 to 160 μM, thus serving as a new copper detection candidate to be widely applied in clinical test, food safety, and environmental monitoring fields.
The control of networked multivehicle systems designed to perform complex coordinated tasks is currently an important and challenging field of research. This paper addresses a cooperative search problem where a team of uninhabited aerial vehicles (UAVs) seeks to find targets of interest in an uncertain environment. We present a practical framework for online planning and control of a group of UAVs for cooperative search based on two interdependent tasks: (i) incrementally updating “cognitive maps” used as the representation of the environment through new sensor readings; (ii) continuously planning the path for each vehicle based on the information obtained through the search. We formulate the cooperative search problem and develop a decentralized strategy based on an opportunistic cooperative learning method, where the emergent coordination among vehicles is enabled by letting each vehicle consider other vehicles’ actions in its path planning procedure. By using the developed strategy, physically feasible paths for the vehicles to follow are generated, where constraints on aerial vehicles, including physical maneuverabilities, are considered and the dynamic nature of the environment is taken into account. We also present some mathematical analysis of the developed search strategy. Our analysis shows that this strategy guarantees a complete search of the environment and is robust to a partial loss of UAVs. A lower bound on the search time for any strategy and a relaxed upper bound for the proposed strategy are given. Simulation results are used to illustrate the effectiveness of the proposed strategy.
The main objective of this research is to develop and evaluate the performance of strategies for cooperative control of autonomous air vehicles that seek to gather information about a dynamic target environment, evade threats, and coordinate strikes against targets. The air vehicles are equipped with sensors to view a limited region of the environment they are visiting, and are able to communicate with one another to enable cooperation. They are assumed to have some "physical" limitations including possibly maneuverability limitations, fuel/time constraints and sensor range and accuracy. The developed cooperative search framework is based on two inter-dependent tasks: (i) on-line learning of the environment and storing of the information in the form of a "target search map"; and (ii) utilization of the target search map and other information to compute on-line a guidance trajectory for the vehicle to follow. We study the stability of vehicular swarms to try to understand what types of communications are needed to achieve cooperative search and engagement, and characteristics that affect swarm aggregation and disintegration. Finally, we explore the utility of using
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