Novel rod-like ternary nanoscale layered double hydroxides (Ca-Mg-Al-LDH) and their bimetal derivatives (Ca-Mg-Al-LDOx, x: 200, 300, 400, 500, and 600 °C) were fabricated with a simple-green hydrothermal and calicination process. The interaction mechanism and adsorption property of U­(VI) on Ca-Mg-Al-LDH and Ca-Mg-Al-LDOx were investigated by a batch technique and spectroscopy analysis, and the results indicated that U­(VI) could form strong and stable surface complexes on Ca-Mg-Al-LDH and Ca-Mg-Al-LDOx surfaces. The adsorption capacity of U­(VI) on various adsorbents could be controlled and adjusted through changing the calcination temperature, which was attributed to the different contents of various metal–oxide bonds (e.g., Ca–O, Mg–O, and Al–O). The adsorption capacities of U­(VI) on these adsorbents were in the order of Ca-Mg-Al-LDO500 (486.8 mg/g) > Ca-Mg-Al-LDO600 (373.4 mg/g) > Ca-Mg-Al-LDO400 (292.5 mg/g) > Ca-Mg-Al-LDO300 (260.0 mg/g) > Ca-Mg-Al-LDO200 (223.5 mg/g) > Ca-Mg-Al-LDH (132.5 mg/g), which might be attributed to more active surface sites and abundant “Ca–O and Al–O” with the increase of calcination temperature. The results of kinetic and thermodynamic studies demonstrated that the adsorption was a spontaneous and endothermic chemical process, and the better fitted Sips model revealed that the adsorption reaction was multilayer adsorption at low concentration of U­(VI) and monolayer adsorption at high concentration of U­(VI). This study provided highlights on the interaction mechanism of U­(VI) with various metal–oxide bonds, and it could play an important role for the controllable adsorption capacity and effcient application in environmental remediation.

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This work addresses the problem of predicting the Remaining Useful Life (RUL) of components for which a mathematical model describing the component degradation is available, but the values of the model parameters are not known and the observations of degradation trajectories in similar components are unavailable. The proposed approach solves this problem by using a Particle Filtering (PF) technique combined with a Kernel Smoothing (KS) method. This PF-KS method can simultaneously estimate the degradation state and the unknown parameters in the degradation model, while significantly overcoming the problem of particle impoverishment. Based on the updated degradation model (where the unknown parameters are replaced by the estimated ones), the RUL prediction is then performed by simulating future particles evolutions. A numerical application regarding prognostics for Lithium-ion batteries is considered. Various performance indicators measuring precision, accuracy, steadiness and risk of the obtained RUL predictions are computed. The obtained results show that the proposed PF-KS method can provide more satisfactory results than the traditional PF methods.

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