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        Lithium-sulfur batteries have been regarded as the next-generation rechargeable batteries due to their high theoretical energy density and specific capacity. Nevertheless, the shuttle effect of lithium polysulfides has hindered the development of lithium-sulfur batteries. Herein, a novel zirconium-based metal-organic framework-801 film on carbon cloth was developed as a versatile interlayer for lithium-sulfur batteries. This interlayer has a hierarchical porous structure, suitable for the immobilization of lithium polysulfides and accommodating volume expansion on cycling. Moreover, the MOF-801 material is capable of strongly adsorbing lithium polysulfides and promoting their catalytic conversion, which can be enhanced by the abundant active sites provided by the continuous structure of the MOF-801 films. Based on the above advantages, the lithium-sulfur battery, with the proposed interlayer, delivers an initial discharge capacity of 927 mAh·g–1 at 1 C with an extremely low decay rate of 0.04% over 500 cycles. Additionally, a high area capacity of 4.3 mAh·cm–2 can be achieved under increased S loading.

        ,   et al.
        Na , Cl and K are the most abundant electrolytes present in biological fluids that are essential to the regulation of pH homeostasis, membrane potential and cell volume in living organisms. Herein, we report synthetic crown ether-thiourea conjugates as a cation/anion symporter, which can transport both Na and Cl across lipid bilayers with relatively high transport activity. Surprisingly, the ion transport activities were diminished when high concentrations of K ions were present outside the vesicles. This unusual behavior resulted from the strong affinity of the transporters for K ions, which led to predominant partitioning of the transporters as the K complexes in the aqueous phase preventing the transporter incorporation into the membrane. Synthetic membrane transporters with Na , Cl and K transport capabilities may have potential biological and medicinal applications.

        Zhixing Zhao ,   Bailing Tang   et al.
        The interrelationships and synergistic regulations of bioactive molecules play pivotal roles in physiological and pathological processes involved in the initiation and development of some diseases, such as cancer and neurodegenerative and cardiovascular diseases. Therefore, the simultaneous, accurate and timely detection of two bioactive molecules is crucial to explore their roles and pathological mechanisms in related diseases. Fluorescence imaging associated with small molecular probes has been widely used in the imaging of bioactive molecules in living cells and due to its excellent performances, including high sensitivity and selectivity, noninvasive properties, real-time and high spatial temporal resolution. Single organic molecule fluorescent probes have been successively developed to simultaneously monitor two biomolecules to uncover their synergistic relationships in living systems. Hence, in this review, we focus on summarizing the design strategies, classifications, and bioimaging applications of dual-response fluorescent probes over the past decade. Furthermore, future research directions in this field are proposed.

        Yongqing Zhou ,   Xin Wang   et al.
        Modeling and optimization is crucial to smart chemical process operations. However, a large number of nonlinearities must be considered in a typical chemical process according to complex unit operations, chemical reactions and separations. This leads to a great challenge of implementing mechanistic models into industrial-scale problems due to the resulting computational complexity. Thus, this paper presents an efficient hybrid framework of integrating machine learning and particle swarm optimization to overcome the aforementioned difficulties. An industrial propane dehydrogenation process was carried out to demonstrate the validity and efficiency of our method. Firstly, a data set was generated based on process mechanistic simulation validated by industrial data, which provides sufficient and reasonable samples for model training and testing. Secondly, four well-known machine learning methods, namely, K-nearest neighbors, decision tree, support vector machine, and artificial neural network, were compared and used to obtain the prediction models of the processes operation. All of these methods achieved highly accurate model by adjusting model parameters on the basis of high-coverage data and properly features. Finally, optimal process operations were obtained by using the particle swarm optimization approach.

        Haoqin Fang ,   Jianzhao Zhou   et al.

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