Martineau Charlotte, Vial Sandrine, Barth Dominique, Quessette Franck, Taulelle Francis
Tectospin, Institut Lavoisier de Versailles (UMR CNRS 8180), Université de Versailles, St Quentin en Yvelines, 45 Avenue des Etats Unis, 78035 Versailles Cedex, France.
PRISM (UMR CNRS 8144), Université de Versailles, St Quentin en Yvelines, 45 Avenue des Etats Unis, 78035 Versailles Cedex, France.
Solid State Nucl Magn Reson. 2015 Feb;65:84-8. doi: 10.1016/j.ssnmr.2014.10.002. Epub 2014 Oct 23.
In this contribution, we have explored the potential and strength of one-dimensional (1D) (29)Si and two-dimensional (2D) (29)S-(29)Si and (29)Si-(17)O NMR as invariants of non-oriented graph for fingerprinting zeolite frameworks. 1D and 2D (29)Si NMR can indeed provide indications on the graph vertices, edges and allow the construction of the adjacency matrix, i.e. the set of connections between the graph vertices. From the structural data, hypothetical 1D (29)Si and 2D (29)Si-(29)Si NMR signatures for 193 of the zeolite frameworks reported in the Atlas of Zeolite Structures have been generated. Comparison between all signatures shows that thanks to the 1D (29)Si NMR data only, almost 20% of the known zeolite frameworks could be distinguished. Further NMR signatures were generated by taking into account 2D (29)Si-(29)Si and (29)Si-(17)O correlations. By sorting and comparison of all the NMR data, up to 80% of the listed zeolites could be unambiguously discriminated. This work indicates that (i) solid-state NMR data indeed represent a rather strong graph invariant for zeolite framework, (ii) despite their difficulties and costs (isotopic labeling is often required, the NMR measurements can be long), (29)Si and (17)O NMR measurements are worth being investigated in the frame of zeolites structure resolution. This approach could also be generalized to other zeolite-related materials containing NMR-measurable nuclides.
在本论文中,我们探索了一维(1D) (29)Si、二维(2D) (29)S-(29)Si和(29)Si-(17)O核磁共振作为无定向图不变量用于沸石骨架指纹识别的潜力和优势。一维和二维(29)Si核磁共振确实可以提供有关图顶点、边的信息,并允许构建邻接矩阵,即图顶点之间的连接集。根据结构数据,已经生成了《沸石结构图谱》中报道的193种沸石骨架的假设一维(29)Si和二维(29)Si-(29)Si核磁共振信号。所有信号之间的比较表明,仅借助一维(29)Si核磁共振数据,就可以区分近20%的已知沸石骨架。通过考虑二维(29)Si-(29)Si和(29)Si-(17)O相关性生成了更多的核磁共振信号。通过对所有核磁共振数据进行分类和比较,高达80%的所列沸石可以被明确区分。这项工作表明:(i) 固态核磁共振数据确实是沸石骨架相当强大的图不变量;(ii) 尽管存在困难和成本(通常需要同位素标记,核磁共振测量可能耗时较长),(29)Si和(17)O核磁共振测量在沸石结构解析框架内仍值得研究。这种方法也可以推广到其他含有可进行核磁共振测量核素的沸石相关材料。