Modeling Sorption and Diffusion of Alkanes, Alkenes, and their Mixtures in Silicalite: From MD and GCMC Molecular Simulations to Artificial Neural Networks,Advanced Theory and Simulations

当前位置： X-MOL 学术 › Adv. Theory Simul. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Modeling Sorption and Diffusion of Alkanes, Alkenes, and their Mixtures in Silicalite: From MD and GCMC Molecular Simulations to Artificial Neural Networks
Advanced Theory and Simulations ( IF 2.9 ) Pub Date : 2021-01-20 , DOI: 10.1002/adts.202000210
Arsenios Gurras ₁ , Leonidas N. Gergidis ₁

Affiliation

Molecular dynamics (MD) in canonical (NVT) statistical ensemble and grand canonical‐Monte Carlo (GCMC) simulations along with artificial neural network (ANN) techniques are used for the study of diffusion and sorption characteristics of small alkanes, alkenes, and their mixtures in silicalite. In particular, sorption isotherms and self‐diffusion coefficients of alkanes (ethane to hexane), alkenes (ethene to hexene), and the respective alkane–alkene mixtures (consisting of the same number of carbon atoms) in silicalite are studied. The findings are directly compared with recent magic‐angle spinning pulsed field‐gradient nuclear magnetic resonance experimental diffusivity measurements and are in close agreement. Furthermore, new results are provided for the alkane–alkene systems. The sorption data from GCMC simulations, the self‐diffusivity calculations from the MD simulations along with available experimental data are used for the development of ANN predictive modeling procedures in order to give generic sorption and diffusion predictions for pure alkanes, alkenes in different input values of fugacity, temperature, and sorbate loadings at the minimum computational resources and time. Finally, structural characteristics for pure alkane, alkenes, and alkane–alkene mixtures when confined in the silicalite framework are computed revealing sorption domains and siting preferences.

中文翻译：

模拟烷烃，烯烃及其混合物在硅质岩中的吸附和扩散：从MD和GCMC分子模拟到人工神经网络

规范（NVT）统计集合中的分子动力学（MD）和大规范-蒙特卡洛（GCMC）模拟以及人工神经网络（ANN）技术用于研究小烷烃，烯烃及其混合物的扩散和吸附特性在硅质岩中。特别是，研究了硅沸石中烷烃（乙烷到己烷），烯烃（乙烯到己烯）以及相应的烷烃-烯烃混合物（由相同碳原子数组成）的吸附等温线和自扩散系数。这些发现直接与最近的魔角旋转脉冲场梯度核磁共振实验扩散率测量结果相吻合。此外，为烷烃-烯烃体系提供了新的结果。来自GCMC模拟的吸附数据，MD模拟的自扩散率计算以及可用的实验数据被用于ANN预测建模程序的开发，以便给出纯链烷烃，不同输入逸度，温度和吸附物负荷值的烯烃的一般吸附和扩散预测以最少的计算资源和时间。最后，计算了纯链烷烃，烯烃和链烷烃混合物的结构特征，将其限制在硅沸石骨架中，从而揭示了吸附域和位置偏好。并以最少的计算资源和最少的时间吸收吸附剂。最后，计算了纯链烷烃，烯烃和链烷烃混合物的结构特征，将其限制在硅沸石骨架中，从而揭示了吸附域和位置偏好。并以最少的计算资源和最少的时间吸收吸附剂。最后，计算了纯链烷烃，烯烃和链烷烃混合物的结构特征，将其限制在硅沸石骨架中，从而揭示了吸附域和位置偏好。

更新日期：2021-03-09

点击分享查看原文

点击收藏

阅读更多本刊最新论文