Grand canonical Monte Carlo simulation on the metal-doped zeolite for enhancing separation of organic sulfur

IF 2.5 4区化学 Q4 BIOCHEMISTRY & MOLECULAR BIOLOGY

Journal of Molecular Modeling Pub Date : 2025-08-13 DOI:10.1007/s00894-025-06472-y

Bin Sun, Dalong Zheng, Xin Song

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引用次数: 0

Abstract

Content

In this study, the effects of metal doping (Al, Cu, Fe) on the performance of MFI for adsorption and removal of organic sulfur (carbonyl sulfide (COS), methyl sulfide (CH₃SH), carbon disulfide (CS₂), and ethyl mercaptan (C₂H₅SH)) were systematically investigated. The mechanisms of metal doping on the adsorption sites and the competing adsorption behaviors were revealed. Under the independent adsorption conditions, Al doping resulted in an enhancement in the adsorption of COS and CS₂. Cu doping led to a preferential enhancement in the adsorption of COS, while concurrently inhibiting the adsorption of other molecules. Fe doping results in a slight reduction in the amount of adsorption. However, it concomitantly leads to a decrease in the stable adsorption escape from 607.43 to 470.32 kPa. Under the simultaneous adsorption conditions, Fe-MFI demonstrated optimal industrial adaptability, characterized by a low adsorption fugacity demand (130.87 kPa) and effective separation of the four molecules through a variable pressure process. Furthermore, the variation of organic sulfur concentration exerts a significant effect on the migration of the respective adsorption sites and the alteration of the adsorption configurations. The present study provides a theoretical basis for the application of metal-modified MFI zeolites in the field of organic sulfur removal and variable pressure adsorption separation.

Methods

The theoretical study is based on the density functional theory (DFT) method for geometric structure optimization and the grand canonical Monte Carlo (GCMC) method for simulation of adsorption properties. This geometric structure (MFI, metal-doped MFI, COS, CS₂, CH₃SH, and C₂H₅SH) optimization is carried out using the Dmol³ module in the Material Studio 2017. In this study, the GGA/PBE method was employed in conjunction with the DNP basis set, a spin-polarized set, and a DFT-D correction. The translational and rotational partition functions of the gas-phase molecules have been taken into account. This sorption behaviors of COS, CS₂, CH₃SH, and C₂H₅SH on MFI and metal-doped MFI are carried out using the Sorption module in the Material Studio 2017. The fugacity range is from 101.33 to 1013.25 kPa, and the temperature is 298 K.

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金属掺杂沸石促进有机硫分离的大正则蒙特卡罗模拟

本研究系统研究了金属掺杂（Al、Cu、Fe）对MFI吸附和去除有机硫（羰基硫（COS）、甲基硫（CH3SH）、二硫化碳（CS2）和乙基硫醇（C2H5SH））性能的影响。揭示了金属在吸附位点上的掺杂机理和竞争吸附行为。在独立吸附条件下，Al掺杂对COS和CS2的吸附增强。Cu掺杂导致对COS的优先吸附增强，同时抑制对其他分子的吸附。铁掺杂导致吸附量略有减少。稳定吸附逸出量从607.43 kPa下降到470.32 kPa。在同步吸附条件下，Fe-MFI表现出最佳的工业适应性，吸附逸度需求低（130.87 kPa），通过变压过程可有效分离四种分子。此外，有机硫浓度的变化对各吸附位点的迁移和吸附构型的改变有显著影响。本研究为金属改性MFI沸石在有机硫脱除和变压吸附分离领域的应用提供了理论基础。方法采用密度泛函理论（DFT）方法进行几何结构优化，采用大正则蒙特卡罗（GCMC）方法模拟吸附性能。这种几何结构（MFI、金属掺杂MFI、COS、CS2、CH3SH和C2H5SH）的优化是使用Material Studio 2017中的Dmol3模块进行的。在本研究中，GGA/PBE方法与DNP基集、自旋极化集和DFT-D校正相结合。考虑了气相分子的平动配分函数和旋转配分函数。COS、CS2、CH3SH和C2H5SH对MFI和掺杂金属的MFI的吸附行为是使用Material Studio 2017中的吸附模块进行的。逸度范围为101.33 ~ 1013.25 kPa，温度为298 K。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Molecular Modeling 化学-化学综合

CiteScore

3.50

自引率

4.50%

发文量

362

审稿时长

2.9 months

期刊介绍： The Journal of Molecular Modeling focuses on "hardcore" modeling, publishing high-quality research and reports. Founded in 1995 as a purely electronic journal, it has adapted its format to include a full-color print edition, and adjusted its aims and scope fit the fast-changing field of molecular modeling, with a particular focus on three-dimensional modeling. Today, the journal covers all aspects of molecular modeling including life science modeling; materials modeling; new methods; and computational chemistry. Topics include computer-aided molecular design; rational drug design, de novo ligand design, receptor modeling and docking; cheminformatics, data analysis, visualization and mining; computational medicinal chemistry; homology modeling; simulation of peptides, DNA and other biopolymers; quantitative structure-activity relationships (QSAR) and ADME-modeling; modeling of biological reaction mechanisms; and combined experimental and computational studies in which calculations play a major role.