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Although advances in industrial products have brought convenience to our lives, severe weather has increased the safety risks to industrial facilities. Considerable efforts have been made to develop high-performance superhydrophobic antiicing coatings. Nevertheless, designing a functional coating with both anti-icing properties and self-deicing remains a major challenge. Here, we propose a design strategy to exploit a photothermal superhydrophobic multifunctional coating with excellent anti-icing and deicing properties based on MXene by high-temperature sintering and layer-by-layer coating. Specifically, poly(tetrafluoroethylene) (PTFE) particles provide low surface energy and binding effects. Room-temperature-vulcanized silicone rubber (RTV) enhances the dispersion of the composite particles and the adhesion of the functional coating to a glass substrate. Furthermore, the functional coatings constructed with MXene exhibit outstanding photothermal effects, imparting excellent superhydrophobicity (CA = 160.18°, SA = 1.8°) and efficient photothermal conversion (equilibrium temperature of 109.3 °C). An anti-icing/deicing test is simulated to confirm their efficient anti-icing/deicing performance in practical applications. Overall, the functional coatings designed in this work can be applied in real industrial facilities.

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For hybrid finite elements based, for example, on complementary energy functional, polynomial trial functions of stress fields are required. Systematic schemes are given for the 2D and 3D elasticity, respectively. For 2D problems, the paper shows that there are maximal four independent polynomials for the nth order homogeneous polynomial Airy stress functions: two for the first order, three for the second order, and four for the nth order (n not less than 3). For 3D problems, there are 3(2n + 1) independent polynomials for the nth order homogeneous polynomial displacement. The corresponding stress field can be given directly. As examples, explicit expressions of the corresponding independent polynomials are listed: up to the tenth order for the 2D case and up to the third order for the 3D case.

This paper systematically studies the effect of Fe3O4 nanoparticle size on the insulation performance of nanofluid impregnated paper. Three kinds of Fe3O4 nanoparticles with different sizes and their nanofluid impregnated papers were prepared. Environmental scanning electron microscopy (ESEM) and infrared spectroscopy were used to analyze the combination of Fe3O4 nanoparticles and nanofluid impregnated paper. The effect of nanoparticle size on breakdown voltage and several dielectric characteristics, e.g., permittivity, dielectric loss, of the nanofluid impregnated paper were comparatively investigated. Studies show that the Fe3O4 nanoparticles were bound to impregnated paper fibers by O–H bonds, while the relative permittivity and dielectric loss of the nanofluid impregnated papers were increased. Meanwhile, the increase of trap depth, caused by the nanoparticles, can trap the electric charge and improve the breakdown strength. The test results show that the direct current (DC) and alternating current (AC) breakdown voltages of nanofluid impregnated paper increased by 9.1% and 10.0% compared to FR3 nanofluid impregnated paper, respectively.

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We performed parameter estimation in the Zebiak–Cane model for the real-world scenario using the approach of ensemble Kalman filter (EnKF) data assimilation and the observational data of sea surface temperature and wind stress analyses. With real-world data assimilation in the coupled model, our study shows that model parameters converge toward stable values. Furthermore, the new parameters improve the real-world ENSO prediction skill, with the skill improved most by the parameter of the highest climate sensitivity (gam2), which controls the strength of anomalous upwelling advection term in the SST equation. The improved prediction skill is found to be contributed mainly by the improvement in the model dynamics, and second by the improvement in the initial field. Finally, geographic-dependent parameter optimization further improves the prediction skill across all the regions. Our study suggests that parameter optimization using ensemble data assimilation may provide an effective strategy to improve climate models and their real-world climate predictions in the future.