燃料化学学报2017，Vol.45Issue(7)：769-779,11.
基于分子模拟技术煤焦分子模型构建
Constructions of coal and char molecular models based on the molecular simulation technology
摘要
Abstract
Coal and char are essential energy sources for the process industry. Insightful understanding of those molecules is useful to explore reactivities of coal and char. Therefore, coal and char molecular structures were investigated at atomic level using Materials Studio 7. 0 software. Firstly, coal and char initial structures were constructed based on reported literatures. Secondly, those structures were improved by molecular mechanics, where functional group fragments were added to satisfy the property of coal or char. Then, the subsequent structures were optimized by annealing dynamics simulation to adjust density and elementary composition. Finally, the potential energies of coal and char were calculated using energy minimization method. It was pointed out that the estimated densities and elementary composition were agreements with the published literatures, which indicated that those structures were valid and reasonable. From the simulated results, it was shown that the Coulomb energy and van der Waals energy played a much more important role than other energies during the stabilizing molecular construction process. Thus, it was inferred that the weak bond was predominant in the thermal processing of coal or char. In addition, this work demonstrated that the molecular simulation technology was meaningful to construct the complex macromolecular structure.关键词
煤焦/分子模拟/分子动力学/退火模拟算法Key words
coal char/molecular simulation/molecular dynamics/annealing dynamics simulation分类
化学化工引用本文复制引用
雷昭,杨鼎,张云鹤,崔平..基于分子模拟技术煤焦分子模型构建[J].燃料化学学报,2017,45(7):769-779,11.基金项目
The project was supported by the National Natural Science Foundation of China ( 21476001 ) , Key Project of Anhui Provincial Department of Education (KJ2017A045) and the Open Fund of Shaanxi Key Laboratory of Energy Chemical Process Intensification (SXECPI201601).国家自然科学基金(21476001),安徽省教育厅重点项目(KJ2017A045)和陕西省能源化工过程强化重点实验室开放课题(SXECPI201601)资助 （ 21476001 ）
