The particle size of powder coatings is an important factor affecting flow and fluidization performance, which in turn determines the quality of the coating. Powder coating particles, such as boron nitride, titanium dioxide, barium titanate, niobium oxide, and tungsten oxide, can be seen in SEM images as circular or polygonal shapes, with irregular edges and sizes, as well as aggregation and stacking between them. This paper introduces a novel method of automatic particle identification and size analysis applied in SEM images for the investigation of coating quality. Firstly, a fast gradient segmentation algorithm (Split–Merge) is utilized to automatically generate samples through determining the adhesive particles and particle groups. The samples are then manually checked and corrected to further segment the particles in order to form the dataset. Finally, the YOLOv5, a mature target detection algorithm, is used to train the labeled data in order to produce a multi-target detection recognition model. The model can be applied to identify more pictures of the same substance, and the particles’ long and short axes are estimated using the elliptical coverage of the particles within the identified image boundary box. Experiment results from four kinds of powders suggest that this method can provide automatic particle size analysis for industrial SEM particle images.

Nuclear magnetic resonance (NMR) techniques are widely used to identify pure substances and probe protein dynamics. Edible oil is a complex mixture composed of hydrocarbons, which have a wide range of molecular size distribution. In this research, low-field NMR (LF-NMR) relaxation characteristic data from various sample oils were analyzed. We also suggest a new method for predicting the size of edible oil molecules using LF-NMR relaxation time. According to the relative molecular mass, the carbon chain length and the transverse relaxation time of different sample oils, combined with oil viscosity and other factors, the relationship between carbon chain length and transverse relaxation time rate was analyzed. Various oils and fats in the mixed fluid were displayed, reflecting the composition information of different oils. We further studied the correlation between the rotation correlation time and the molecular information of oil molecules. The molecular composition of the resulting fluid determines its properties, such as viscosity and phase behavior. The results show that low-field NMR can obtain information on the composition, macromolecular aggregation and molecular dynamics of complex fluids. The measurements of grease in the free-fluid state show that the relaxation time can reflect the intrinsic properties of the fluid. It is shown that the composition characteristics and states of complex fluids can be measured using low-field nuclear magnetic resonance.

The detection and quantitative analysis of shale components are of great significance for comprehensively understanding the properties of shale, assessing its resource potential and promoting efficient development and utilization of resources. The low-field NMR T₁-T₂ two-dimensional spectrum can detect shale components non-destructively and effectively. Unfortunately, due to its complexity, the two-dimensional spectral results of low-field NMR are mainly analyzed using manual qualitative analysis, and accurate results of the composition cannot be obtained. Since the information contained in its two-dimensional map is determined by the morphological texture and the position in the map, commonly used image analysis networks cannot adapt. In order to solve these problems, this paper improves a novel Faster Region-based Convolutional Neural Network (Faster-RCNN). Compared with previous models, the improved Faster-RCNN has better image classification and visual key point estimation capabilities. The results show that compared with traditional methods, the deep learning method using this model can directly obtain key information such as kerogen and movable oil and gas content in rocks. The information provided in this study can help complement and improve the development of analytical methods for low-field 2D NMR spectra.

Nuclear magnetic resonance (NMR) techniques are widely used to identify pure substances and probe protein dynamics. Edible oil is a complex mixture composed of hydrocarbons, which have a wide range of molecular size distribution. In this research, low field NMR (LF-NMR) relaxation characteristic data of various sample oils were analyzed. We also suggest a new method for prediction size of edible oil molecules using LF-NMR relaxation time distributions. According to the relative molecular mass, carbon chain length, transverse relaxation time of different sample oils, combined with oil viscosity and other factors, the relationship between carbon chain length and transverse relaxation time rate was analyzed. Various oils and fats in the mixed fluid are displayed, reflecting the composition information of different oils. To study the correlation between the rotation correlation time and molecular information of oil molecules. The molecular composition of the resulting fluid determines its properties, such as viscosity and phase behavior. The results show that low-field NMR can obtain information on the composition, macromolecular aggregation, and molecular dynamics of complex fluids. Measurements of grease in the free fluid state show that the relaxation time can reflect the intrinsic properties of the fluid. It is shown that the composition characteristics and states of complex fluids can be measured by low-field nuclear magnetic resonance.