Comparison of (K0.5Na0.5)NbO3 Single Crystals Grown by Seed-Free and Seeded Solid-State Single Crystal Growth.

(K0.5Na0.5)NbO3-based piezoelectric ceramics are of interest as a lead-free replacement for Pb(Zr,Ti)O3. In recent years, single crystals of (K0.5Na0.5)NbO3 with improved properties have been grown by the seed-free solid-state crystal growth method, in which the base composition is doped with a specific amount of donor dopant, inducing a few grains to grow abnormally large and form single crystals. Our laboratory experienced difficulty obtaining repeatable single crystal growth using this method. To try and overcome this problem, single crystals of 0.985(K0.5Na0.5)NbO3-0.015Ba1.05Nb0.77O3 and 0.985(K0.5Na0.5)NbO3-0.015Ba(Cu0.13Nb0.66)O3 were grown both by seed-free solid-state crystal growth and by seeded solid-state crystal growth using [001] and [110]-oriented KTaO3 seed crystals. X-ray diffraction was carried out on the bulk samples to confirm that single-crystal growth had taken place. Scanning electron microscopy was used to study sample microstructure. Chemical analysis was carried out using electron-probe microanalysis. The single crystal growth behaviour is explained using the mixed control mechanism of grain growth. Single crystals of (K0.5Na0.5)NbO3 could be grown by both seed-free and seeded solid-state crystal growth. Use of Ba(Cu0.13Nb0.66)O3 allowed a significant reduction in porosity in the single crystals. For both compositions, single crystal growth on [001]-oriented KTaO3 seed crystals was more extensive than previously reported in the literature. Large (~8 mm) and relatively dense (<8% porosity) single crystals of 0.985(K0.5Na0.5)NbO3-0.015Ba(Cu0.13Nb0.66)O3 can be grown using a [001]-oriented KTaO3 seed crystal. However, the problem of repeatable single crystal growth remains.

Coal identification based on a deep network and reflectance spectroscopy.

The rapid identification of coal types in the field is an important task. This research combines spectroscopy with deep learning algorithms and proposes a method for quickly identifying coal types in the field. First, we collect field spectral data of various coals and preprocess the spectra. Then, a coal identification model that uses a convolutional neural network in combination with an extreme learning machine is proposed. The two-dimensional spectral features of coal are extracted through the convolutional neural network, and the extreme learning machine is used as a classifier to identify the features. To further improve the identification performance of the model, we use the whale optimization algorithm to optimize the parameters of the model. The experimental results show that the proposed method can quickly and accurately identify types of coal. It provides a low-cost, convenient, and effective method for the rapid identification of coal in the field.