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Zinc oxide nanostructures such as nanosheets (NS) and nanoflowers (NF) were obtained by a facile hydrothermal synthesis using zinc chloride (ZnCl2) as precursor with low molar concentrations and a short synthesis time (2 h) at 200 °C. By means of X-ray diffraction and Raman spectroscopy measurements, the wurtzite-type ZnO structure was confirmed with high crystalline quality. SEM micrographs showed the influence of ZnCl2 concentration on ZnO morphology; ZnO NF were obtained at low concentrations (0.02 and 0.05 M), while ZnO NS were seen for higher concentrations (0.2–0.6 M) and their thicknesses can be estimated from 15 to 60 nm. In addition, TEM images showed a large number of pores with sizes below 15 nm in both ZnO nanostructures. Raman and photoluminescence displayed the surface defects density for ZnO nanostructures. Raman spectra showed the E1(LO) mode localized at ∼581 cm−1, associated with oxygen vacancies and zinc interstitials, being more intense for ZnO NF, while photoluminescence results showed a strong yellow-orange emission (centered from 587 to 618 nm) relative to UV emission, being more intense for ZnO NF. These properties reveal further potential for high performance luminescent devices based on ZnO NF and NS.

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In this paper, we present a novel algorithm for the automatic detection and measurement of Vickers indentation hardness, using image processing. This algorithm uses image segmentation via binarization, automatically evaluating the mean and extreme gray values by means of standard histogram equalization so as to determine the optimal binarization threshold from each input image. We use a morphological filter and region growing to identify the indentation footprint. Our algorithm determines the four indentation vertices required to calculate diagonal lengths and Vickers hardness number. This algorithm is applied to 230 indentation images of steel-316 and hafnium nitride specimens, obtained using a micro hardness machine. The proposed algorithm can measure the Vickers hardness number of specimens using their indentation images. The algorithm results have a relative error of less than 3% with respect to those obtained through a conventional manual procedure. This algorithm can be used for indentation images with low contrast and irregular indentation edges.