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Many disinfection technologies have emerged recently in water treatment industry, which are designed to inactivate water pathogens with extraordinary efficiency and minimum side effects and costs. Current disinfection processes, including chlorination, ozonation, UV irradiation, and so on, have their inherent drawbacks, and have been proven ineffective under certain scenarios. Bacterial inactivation by noble metals has been traditionally used, and copper is an ideal candidate as a bactericidal agent owing to its high abundance and low cost. Building on previous findings, we explored the bactericidal efficiency of Cu(I) and attempted to develop it into a novel water disinfection platform. Nanosized copper ferrite was synthesized, and it was reduced by hydroxylamine to form surface bound Cu(I) species. Our results showed that the generated Cu(I) on copper ferrite surface could inactivate E. coli at a much higher efficiency than Cu(II) species. Elevated reactive oxygen species’ content inside the cell primarily accounted for the strong bactericidal role of Cu(I), which may eventually lead to enhanced oxidative stress towards cell membrane, DNA, and functional proteins. The developed platform in this study is promising to be integrated into current water treatment industry.

According to the chattering problems of traditional sliding mode index exponential reaching law, this paper proposes a fractional order sliding mode index exponential reaching law control strategy, which is applied to the quad-rotor helicopter attitude control. Combined the theory of fractional order calculus and sliding mode variable structure control theory, the fractional order sliding mode controller is designed. The method of  lyapunov analysis proves that this controller can make the system asymptotically stable. Simulation and experiments show that the proposed fractional order sliding mode control system not only undermines the chattering of traditional sliding mode exponential reaching law but also reduces the adjusting time and control margin of the system.

Feed concentration directly affects the recovery of mineral resources in flotation process, which is an important method of separating fine-grained mineral. Due to complicated process and mechanism of thickener, the control effect is poor with the traditional control method under the condition of time-varying process parameters. Fuzzy control and BP neural network are combined with together in this paper, then we propose a optimization method though self-learning fuzzy neural network, and solved the problem of optimal controlling for the system with variable parameters. Applied to the production process of thickener, the result of instance simulation shows that it can elegantly solve the problem of controlling the ore concentration.