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通过分子筛实现手性分离:对纳米多孔石墨烯合适功能化的计算筛选

Chiral Separation via Molecular Sieving: A Computational Screening of Suitable Functionalizations for Nanoporous Graphene.

作者信息

Fruehwirth Samuel M, Meyer Ralf, Hauser Andreas W

机构信息

Institute of Experimental Physics, Graz University of Technology, Petersgasse 16, A-8010, Graz, Austria.

出版信息

Chemphyschem. 2018 Sep 18;19(18):2331-2339. doi: 10.1002/cphc.201800413. Epub 2018 Jun 19.

Abstract

In a recent study [Angew. Chem. Int. Ed., 2014, 53, 9957-9960] a new concept of chiral separation has been suggested, which is based on functionalized, nanoporous sheets of graphene. In this follow-up article we discuss the underlying principle in greater detail and make suggestions for suitable pore functionalizations with respect to a selection of chiral prototype molecules. Considering drug molecules as future targets for a chiral separation via membranes, the necessary pore sizes represent a big challenge for standard methods of computational chemistry. Therefore, we test two common force fields (GAFF, CGenFF) as well as a semiempirical tight-binding approach recently developed by the Grimme group (GFN-xTB) against the computationally much more expensive density functional theory. We identify the GFN-xTB method as the most suitable approach for future simulations of functionalized pores for the given purpose, as it is able to produce reaction pathways in very good agreement with density functional theory, even in cases where force fields tend to an extreme overestimation of barrier heights.

摘要

在最近的一项研究中[《德国应用化学》国际版,2014年,第53卷,9957 - 9960页],提出了一种基于功能化纳米多孔石墨烯片的手性分离新概念。在这篇后续文章中,我们将更详细地讨论其基本原理,并针对一系列手性原型分子提出合适的孔功能化建议。考虑到药物分子是未来通过膜进行手性分离的目标,所需的孔径对标准计算化学方法来说是一个巨大挑战。因此,我们将两种常用的力场(GAFF、CGenFF)以及格林姆小组最近开发的一种半经验紧束缚方法(GFN - xTB)与计算成本高得多的密度泛函理论进行了对比测试。我们确定GFN - xTB方法是未来针对给定目的对功能化孔进行模拟的最合适方法,因为即使在力场往往会极大高估势垒高度的情况下,它也能产生与密度泛函理论非常吻合的反应路径。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6fc3/6175349/9aa837dd6d1b/CPHC-19-2331-g001.jpg

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