Aromatic Structural Characterization of Different-Rank Vitrinites: Using HRTEM, XRD and AFM

Molecular structure characterization analysis and molecular model construction of anthracite

Coal is the largest non-renewable energy as well as an important basic energy and industrial raw material. Thus, correctly understanding the molecular structure characteristics of coal has important theoretical value for realizing carbon neutralization. In this work, we clarified the molecular structure characteristics of anthracite, where the organic matter in anthracite was characterized and analyzed by industrial/elemental analysis, FTIR, XPS, XRD and solid 13C NMR. The ratio of bridge carbon to the perimeter carbon of anthracite was 0.38, and the degree of condensation in the aromatic structure was high. Nitrogen in coal primarily exists in the form of pyridine and pyrrole. Based on the information on functional group composition, the carbon skeleton structure, and surface element composition, a molecular structure model of Yangquan anthracite could be constructed, where the molecular formula was C208H162O12N4. This study may serve as a reference for researchers in this field to consult and refer to the construction ideas and methods of molecular structure models of different coal samples.

A Novel Method for the Intelligent Recognition of Lattice Fringes in Coal HRTEM Images Based on Semantic Segmentation and Fuzzy Superpixels

High-resolution transmission electron microscopy (HRTEM) can directly obtain the lattice fringes and structure parameters of coal. Aiming at present problems in extracting lattice fringes in HRTEM images, such as unlocated fringe regions, single-threshold segmentation, unclassified fuzzy superpixels, and tedious fringe pruning, an intelligent recognition method based on semantic segmentation, deep neural networks, fuzzy superpixels, and other algorithms is proposed. For unlocated fringe regions, the fringe regions are automatically localized with semantic segmentation. The whole semantic segmentation network adopts DeepLab V3+ based on ResNet to reduce unnecessary operations brought by non-fringe regions. For single-threshold segmentation of the image, the image is chunked before anything else. The genetic-optimized watershed algorithm is applied to divide the fringe base maps and non-fringe ones in order to avoid the distortion caused by different lights and shades of the image. For the fuzzy superpixels between the fringes and non-fringes, a similarity category judgment method based on neighboring pixels is proposed to solve the problem of unclassified fuzzy superpixels and to enrich and perfect the information of the lattice fringe base map. Eventually, as for lattice fringe overlap caused by coals piling together, a similarity judgment method based on the fringes' characteristics is proposed to remove the bur portion of the lattice fringes and improve the pruning rate. Combining the above theories, a visualization tool based on MATLAB App Designer is designed, and the above four steps can be completed by this app to accurately display the results of coal aromatic lattice fringe identification in HRTEM images. Comparison with the lattice fringes drawn by leading experts shows that the fringes interpreted by this method are reliable. This method facilitates the extraction of lattice fringes in HRTEM, which lays the foundation for the labeling of HRTEM images in a variety of deep learning algorithms and facilitates the direct observation of coal structures by researchers.