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In this article, the flower-like, urchin-like, and rod-like ZnOs were synthesized by a convenient atmospheric hydrothermal method. The crystalline structures, morphologies, exposed crystal faces, and specific surface areas of the as-prepared ZnO samples were analyzed. Rhodamine B (RhB) was used as the simulated pollutant to evaluate the photocatalytic performance of the ZnO nanostructures. The flower-like ZnO prepared by controlled hydrothermal method at room temperature for 2 h displayed highest specific surface area and exposed more high active {21¯1¯0}\{2\bar{1}\bar{1}0\}facets compared to the other two morphologies of ZnO. In addition, within 2 h of the photocatalytic reaction, the flower-like ZnO results in 99.3% degradation of RhB and produces the most hydroxyl radicals (˙OH) 47.83 μmol/g and superoxide anions (˙O2−{\text{O}}_{2}^{-}) 102.78 μmol/g. Due to the existence of oxygen vacancies on the surface of {21¯1¯0}\{2\bar{1}\bar{1}0\}facets, the flower-like ZnO can efficiently catalyze the production of active oxygen, leading to the improvement in the photocatalytic efficiency.

It is quite necessary to develop a safe and efficient technique for disinfection of drinking water to avoid waterborne pathogens of infectious diseases. Herein, ZnO nanoarray electrodes with different sizes were investigated for low-voltage and high-efficiency electroporation disinfection. The results indicated that the ZnO nano-pyramid with small tip width and proper length exhibited over 99.9% disinfection efficiency against Escherichia coliunder 1 V and a flow rate of 10 mL/min (contact time of 1.2 s). The suitable size of the nanoarray for electroporation disinfection was optimized by establishing the correlation between four kinds of ZnO nanoarrays and their efficiency of electroporation disinfection, which can guide the preparation of next-generation electroporation-disinfecting electrodes.

Ambient noise recorded by a dense seismic array provides an opportunity to resolve detailed 3D shear wave velocity (Vs) structures. We utilize the frequency‐Bessel transform (F‐J) method to compute the dispersion spectrogram for ambient noise recorded by the Long Beach seismic array, extract multimodal dispersion curves with a deep learning approach, and invert multimodal dispersion curves with a gradient algorithm. The addition of higher‐mode dispersion curves reduces nonuniqueness and provides an improved high‐resolution 3D Vs model that shows good correlations with the known stratigraphic sequence and geologic features in the study area. Furthermore, the Richard fault between the Pacific Coast Highway and Newport‐Inglewood faults, which have never been discussed in previous studies, is resolved. Our study demonstrates the potential of using the F‐J method to extract multimodal dispersion curves and consequently retrieve an improved high‐resolution 3D Vs model from a dense seismic array. A seismometer not only records earthquake events but also records weak vibration signals in the environment. Researchers use the seemingly irregular ambient vibration recorded by a dense seismic array to obtain the shear wave velocity (Vs) structure underground. In this study, the ambient vibration recorded by the Long Beach seismic array is analyzed with a recently developed frequency‐Bessel transform method, which can effectively extract higher‐mode dispersion curves. For each subarray, we measure the dispersion curves with this recently developed method and simultaneously invert the fundamental and higher‐mode dispersion curves to obtain a 1D Vs model. Then, we merge all 1D Vs models at each subarray to construct a 3D Vs model. Our model shows high‐resolution features in both lateral and vertical directions, which correlate well with known geological backgrounds. In addition, the Pacific Coast Highway fault, Newport‐Inglewood fault, and Richfield fault never discussed in the previous velocity model, are clearly resolved in this study. Multimodal dispersion curves are readily extracted from the frequency‐Bessel spectrogram by a deep learning approach named DisperNetAn improved high‐resolution 3D shear‐wave velocity model of Long Beach is achieved by the inversion of multimodal dispersion curvesThe stratigraphic sequences and faults, including the Pacific Coast Highway, Newport‐Inglewood and Richfield faults, are well resolved Multimodal dispersion curves are readily extracted from the frequency‐Bessel spectrogram by a deep learning approach named DisperNet An improved high‐resolution 3D shear‐wave velocity model of Long Beach is achieved by the inversion of multimodal dispersion curves The stratigraphic sequences and faults, including the Pacific Coast Highway, Newport‐Inglewood and Richfield faults, are well resolved