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With the rapid development of electric vehicles, power electronics, and medical devices, high-energy-density capacitors have attracted considerable research and engineering attention. In this investigation, we added surface-coated BNT-NN ((Bi0.5Na0.5)­TiO3–NaNbO3) nanoparticles into the poly­(vinylidene fluoride-hexafluoropropylene) (PVDF-HFP) matrix to fabricate BNT-NN/PVDF-HFP nanocomposites. We found that the composite with ultralow contents of BNT-NN (0.5 wt %) exhibited an ultrahigh energy density of 36.94 J cm–3 at 800 mV m–1, which is 17.5% higher than that of the pristine PVDF-HFP film. However, as the content of BNT-NN nanoparticles increased to 1%, the energy density decreased by 10.9% to 32.9 J cm–3 at 750 mV m–1. Compared with other PVDF-based nanocomposite films reported previously, this work shows a much higher energy density. This may be attributed to the largely increasing dielectric constant at the interface between the nanoparticles and their surrounding substrate, especially for the nanocomposite with a low volume content. The calculated dielectric constant from the theoretical model considering interface parameters fits well with the experimental data, indicating that the composites possess K max at ultralow nanoparticle contents. Therefore, the ultralow nanoparticle-added nanocomposites show great promise for ultrahigh-energy-density capacitor applications.

Iron tailings ponds are engineered dam and dyke systems used to capture iron tailings. They are high-risk hazards with high potential energy. If the tailings dam broke, it would pose a serious threat to the surrounding ecological environment, residents’ lives, and property. Rainfall is one of the most important influencing factors causing the tailings dam break. This paper took Chengde Area, a typical iron-producing area, as the study area, and proposed a remote sensing method to evaluate the safety risk of tailings ponds under rainfall condition by using runoff coefficient and catchment area. Firstly, the vegetation coverage in the study area was estimated using the pixel dichotomy model, and the vegetation type was classified by the support vector machine (SVM) method from Landsat 8 OLI image. Based on DEM, the slope of the study area was extracted, and the catchment area of the tailings pond was plotted. Then, taking slope, vegetation coverage, and vegetation type as three influencing factors, the runoff coefficient was constructed by weight assignment of each factor using analytic hierarchy process (AHP) model in both quantitative and qualitative way. Finally, the safety risk of tailings ponds was assessed according to average runoff coefficient and catchment area in the study area. The results showed that there were 124 low-risk tailings ponds, 16 moderate-risk tailings ponds, and 4 high-risk tailings ponds in the study area. This method could be useful for selecting targeted tailings ponds for focused safety monitoring. Necessary monitoring measurements should be carried out for the high-risk and moderate-risk tailings ponds in rainy season.

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