Journal of Applied Crystallography最新文献

The bidisperse structure of a natural Brazilian opal was revealed using transmission electron microscopy (TEM). Since TEM enables observation through the transparency of a few layers of silica spheres, the lamellar observation produced a complex image suggesting a flower-like repeating pattern generated by superimposed discs more or less opaque to the electron beam. Analysis of this disc combination revealed that the stacking structure of this opal was a hexagonal Laves phase of MgZn₂ type. The cutting plane is parallel to (1120). A simulation over the thickness of the lamella matched the TEM image very well. The synthesis of such binary colloids is the aim of many experimental and theoretical studies with a variety of industrial applications. This study aims to prove that determining the structure of ordered natural opals can be performed by analysing TEM images.

Experimental data collected from a triple-axis spectrometer (TAS) are typically analysed by considering the instrument resolution, as the resolution of a TAS instrument is often complex and significantly influences the measured results. Two Python packages, TasVisAn and InsPy, have been developed to visualize and analyse data from TAS instruments – particularly from the cold-neutron TAS Sika and the thermal-neutron TAS Taipan at the Australian Centre for Neutron Scattering. TasVisAn offers a range of functions, including data importing, data reduction, plotting, contour mapping, convolution fitting and more, for data collected on traditional TAS instruments. It also supports data reduction for modern multi-analyser and multiplexing TAS instruments, including the multiplexing mode of Sika. It includes scan simulation and batch file validation tools for both Taipan and Sika, assisting users in designing and planning experiments in advance. InsPy is a general-purpose Python package designed to calculate the 4D instrument resolution in momentum–energy space for any TAS instrument. Combined with InsPy, TasVisAn supports both instrument resolution calculation and resolution-convoluted data fitting. Its flexible external data import feature further allows TasVisAn to be adapted for the visualization and convolution analysis of inelastic neutron scattering data from various TAS instruments.

The travelling heater method (THM) is one of the most promising methods for growing high-quality and large-diameter CdZnTe (CZT) crystals. In the experimental THM growth of CZT crystals, fine-tuning of the core growth parameters such as growth temperature, temperature gradient and growth rate is very important. The THM process is traditionally optimized by conducting multiple crystal growth experiments, which is inefficient and costly. In this study, a machine learning (ML) method is developed to accelerate the geometric optimization process of high-quality CZT crystals grown by THM. Nearly 100 sets of THM growth experimental data were imported into a Gaussian process regression neural network model for training, and the following optimal growth parameters were obtained: growth temperature of 867.43°C, growth rate of 0.74 cm per day and temperature gradient of 32.98°C cm⁻¹. Under these optimal growth parameters, the single-crystal rate (the ratio between the area of the largest single-crystalline region and the area of the whole wafer) was predicted to be 66.4% and the energy spectrum resolution was predicted to be 6.4%. An actual THM growth experiment was carried out using the growth parameters obtained by ML. The experimental results showed that the single-crystal rate of the experimental crystal was 67% and the energy spectrum resolution was about 6.9%, which are close to the results predicted by ML. Compared with the growth results of three sets of artificial improved growth parameters, the crystal growth results of the ML-improved parameters have the best single-crystal rate, resistivity, energy resolution and detector performance. All the results demonstrate that the ML method is effective in guiding the THM growth of CZT crystals.