Quantitative Analysis of the Influence of Volatile Matter Content in Coal Samples on the Fractal Dimension of Their Nanopore Characteristics - Lin Sun; Shoule Zhao; Jianghao Wei; Yunfeng Li; Dun Wu; Caifang Wu (Applied Sciences)
As a crucial energy source and chemical raw material, coal’s micro-pore structure holds a pivotal influence on the occurrence and development of coalbed methane (CBM). This study systematically analyzed the nano-pore structure, surface roughness, and fractal characteristics of six coal samples with varying volatile matter content (Vdaf) using Atomic Force Microscopy (AFM) combined with Scanning Electron Microscopy (SEM), revealing the correlation between volatile matter and the micro-physical properties of coal. Through AFM three-dimensional topographical observations, it was found that coal samples with higher volatile matter exhibited significant gorge-like undulations on their surfaces, with pores predominantly being irregular macropores, whereas low volatile matter coal samples had smoother surfaces with dense and regular pores. Additionally, the surface roughness parameters (Ra, Rq) of coal positively correlated with volatile matter content. Meanwhile, quantitative analysis of nano-pore parameters using Gwyddion software showed that an increase in volatile matter led to a decline in pore count, shape factor, and area porosity, while the average pore diameter increased. The fractal dimension of samples with different volatile matter contents was calculated, revealing a decrease in fractal dimension with rising volatile matter. Nano-ring analysis indicated that the total number of nano-rings was significantly higher in low volatile matter coal samples compared to high volatile matter ones, but the nano-ring roughness (Rr) increased with volatile matter content. SEM images further validated the AFM results. Through multi-scale characterization and quantitative analysis, this study clarified the extent to which volatile matter affects the nano-pore structure and surface properties of coal, providing critical data support for efficient CBM development and reservoir evaluation.