We have produced ultrathin lead (Pb) nanowires in the 6 nm pores of SBA-15 mesoporous silica substrates by chemical vapor deposition. The nanowires form regular and dense arrays. We demonstrate that bulk Pb (a type-I superconductor below Tc = 7.2 K with a critical field of 800 Oe) can be tailored by nanostructuring to become a type-II superconductor with an upper critical field (Hc2) exceeding 15 T and signs of Cooper pairing 3-4 K above the bulk Tc. The material undergoes a crossover from a one-dimensional fluctuating superconducting state at high temperatures to three-dimensional long-range-ordered superconductivity in the low-temperature regime. We show with our data in an impressive way that superconductivity in elemental metals can be greatly enhanced by nanostructuring.

The nonlinear acoustic applications for material characterization are reviewed. The general theoretical analysis of the effects of nonlinearity, dissipation, dispersion, and diffraction on intense acoustic-wave propagation is given. Acoustic nonlinear parameters and their determination methods are introduced. The investigations of nonlinear acoustic applications for solid material evaluation are discussed for different levels of disruption, from asymmetry of lattice structure and dislocation in crystals to disbonds and cracks in engineering materials. The experimental methods involved in these investigations are also considered. The techniques used for nonlinear acoustic imaging are divided into two categories, concerned with resolution improvement by using higher harmonics, and nonlinear parametric imaging. The nonlinear acoustic applications in biomedical imaging, acoustic microscopy, and nonlinear nondestructive evaluation are presented. Finally, the issues that need further investigations in this area are discussed.

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Probability-based diagnostic imaging, as one of the damage identification methods using ultrasonic guided waves, has been attracting increasing attention by researchers in the community of structural health monitoring. However, the probability-based diagnostic imaging algorithm’s influencing parameters, including the selection of certain damage index and frequency, the network of sensing paths, and the size of the effective elliptical distribution area, are empirically determined. This experience dependency limits the application of the method to identify damages in real-world practices. Therefore, it is important to clarify the influences of the above-mentioned various factors on the damage identification. However, the complexity of these factors makes it almost impossible to interpret the influencing mechanisms directly. Thus, a fusion image approach of multiple frequencies is employed to eliminate the influence of different frequencies, while a histogram-based method is proposed to evaluate the reliability of the fusion result. Meanwhile, a unit weight distribution function, considering both the network of sensing paths and the size of the effective elliptical distribution area, is presented in the analysis. Then, the influencing mechanisms are studied and discussed in detail, and a methodology is proposed to optimize the network and the scaling parameter which controls the affected zone.