微型多孔材料
表征(材料科学)
煤
吸附
比例(比率)
分子间力
分子动力学
矿物学
材料科学
化学物理
多孔性
化学
化学工程
分子
纳米技术
计算化学
复合材料
物理
工程类
物理化学
有机化学
量子力学
作者
Junqing Meng,Limin Wang,Jie Wang,Shuo Zhang
出处
期刊:Energy & Fuels
[American Chemical Society]
日期:2023-02-09
卷期号:37 (5): 3634-3653
被引量:4
标识
DOI:10.1021/acs.energyfuels.2c03829
摘要
To explore the micropore structure of coal, this paper proposes a method to reconstruct coal pores at the molecular scale, characterizing and analyzing the micropore structure of coal with different metamorphic degrees. Based on the construction of coal molecules, the molecular-scale pore models of coal were reconstructed by the Monte Carlo method. The pore models were visualized and quantitatively characterized via the principles of the maximum sphere algorithm. The molecular-scale pore structure parameters and morphological characteristics of coal with different metamorphic degrees were analyzed. Combined with CO2 adsorption experiments, the rationality of coal molecular-scale pore reconstruction and characterization was verified. The results are as follows: the pore size of molecular-scale pores is distributed over 0.2–0.7 nm, and the throat diameter is mainly distributed over 0.1–0.5 nm. With the increase of metamorphic degree, the specific surface area and pore volume first decreased and then increased, and the number of pores showed a rapid–slow–rapid rise trend. In addition, the pore shape tends to be simpler and then more complex, the pore connectivity decreases and then increases, while the coal development becomes progressively more uniform. During the evolution of the molecular structure, the aliphatic structure and oxygen-containing functional groups in the coal are shed and the intermolecular pores gradually decrease. As the aromatic structure increases, the internal molecular pores gradually increase. The research provides a new method for exploring the molecular-scale pore structure of coal and has practical significance for understanding the microscopic characteristics of coal with different metamorphic degrees.
科研通智能强力驱动
Strongly Powered by AbleSci AI