Five Mesoporous Lanthanide Metal-Organic Frameworks: Syntheses, Structures, and Fluorescence Sensing of Fe3+, Cr2O72-, and H2O2 and Electrochemical Sensing of Trinitrophenol.

When a multicarboxylate aromatic ligand, 3,5-di(2',4'-dicarboxylphenyl)benzoic acid (H5L), was employed, five structurally similar lanthanide metal-organic frameworks (Ln-MOFs), {[Pr10L6(OH)3Cl(H2O)6]·4C2H8N}n (1), {[Nd10L6(OH)4 (H2O)9]·4C2H8N}n (2), {[Gd10L6(OH)4(H2O)3]·4C2H8N}n (3), {[Ho10L6(OH)4 (H2O)3]·4C2H8N}n (4) and {[Er10L6(OH)4(H2O)6]·4C2H8N}n (5), were synthesized and characterized. Single-crystal X-ray structural analyses disclosed that all five Ln-MOFs crystallize in the trigonal R3 space group. They have three-dimensional mesoporous structure featuring the coexistence of binuclear and tetranuclear species as inorganic building units. The mesoporous structure of 3 was verified by the gas adsorption experiment of N2. Fluorescence analysis showed that 3 can selectively detect Fe3+, Cr2O72-, and H2O2; furthermore, it can be used for the electrochemical detection of trinitrophenol. With the merit of an excellent highly sensitive detection performance, 3 has unpredictable application prospects in future research fields.

Multi-Strategy Boosted Fick's Law Algorithm for Engineering Optimization Problems and Parameter Estimation.

To address the shortcomings of the recently proposed Fick's Law Algorithm, which is prone to local convergence and poor convergence efficiency, we propose a multi-strategy improved Fick's Law Algorithm (FLAS). The method combines multiple effective strategies, including differential mutation strategy, Gaussian local mutation strategy, interweaving-based comprehensive learning strategy, and seagull update strategy. First, the differential variation strategy is added in the search phase to increase the randomness and expand the search degree of space. Second, by introducing the Gaussian local variation, the search diversity is increased, and the exploration capability and convergence efficiency are further improved. Further, a comprehensive learning strategy that simultaneously updates multiple individual parameters is introduced to improve search diversity and shorten the running time. Finally, the stability of the update is improved by adding a global search mechanism to balance the distribution of molecules on both sides during seagull updates. To test the competitiveness of the algorithms, the exploration and exploitation capability of the proposed FLAS is validated on 23 benchmark functions, and CEC2020 tests. FLAS is compared with other algorithms in seven engineering optimizations such as a reducer, three-bar truss, gear transmission system, piston rod optimization, gas transmission compressor, pressure vessel, and stepped cone pulley. The experimental results verify that FLAS can effectively optimize conventional engineering optimization problems. Finally, the engineering applicability of the FLAS algorithm is further highlighted by analyzing the results of parameter estimation for the solar PV model.