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通过尺寸排阻色谱法结合热裂解-气相色谱法测定共聚物序列在摩尔质量分布上的变化。

Co-Polymer sequence determination over the molar mass distribution by size-exclusion chromatography combined with pyrolysis - gas chromatography.

机构信息

Analytical Chemistry Group, van 't Hoff Institute for Molecular Sciences (HIMS), Faculty of Science, University of Amsterdam, Science Park 904, Amsterdam, The Netherlands; Centre for Analytical Sciences Amsterdam, Science Park 904, Amsterdam, The Netherlands.

BASF SE, Carl-Bosch-Strasse 38, Ludwigshafen am Rhein, Germany.

出版信息

J Chromatogr A. 2022 May 10;1670:462973. doi: 10.1016/j.chroma.2022.462973. Epub 2022 Mar 20.

Abstract

The chain sequence of co-polymers strongly affects their physical properties. It is, therefore, of crucial importance for the development and final properties of novel materials. Currently however, few analytical methods are available to monitor the sequence of copolymers. The currently preferred method in copolymer-sequence determination, nuclear-magnetic-resonance spectroscopy (NMR), is insensitive (especially when C-NMR is required) and often offers little spectral resolution between signals indicative of different subunits. These limitations are especially challenging when one is interested in monitoring the sequence across the molar-mass distribution or in quantifying low abundant subunits. Therefore, we set out to investigate pyrolysis - gas chromatography (Py-GC) as an alternative method. Py-GC is more sensitive than NMR and offers better resolution between various subunits, but it does require calibration, since the method is not absolute. We devised a method to fuse data from NMR and Py-GC to obtain quantitative information on chain sequence and composition for a set of random and block poly(methyl methacrylate-co-styrene) copolymer samples, which are challenging to analyse as MMA tends to fully depolymerize. We demonstrated that the method can be successfully used to determine the chain sequence of both random and block copolymers. Furthermore, we managed to apply Py-GC to monitor the sequence of a random and a block copolymer across the molar-mass distribution.

摘要

共聚物的链序列强烈影响其物理性质。因此,对于新型材料的开发和最终性能而言,这是至关重要的。然而,目前可用的分析方法很少能够监测共聚物的序列。目前在共聚物序列确定中首选的方法是核磁共振波谱(NMR),但该方法不灵敏(特别是需要 C-NMR 时),并且在不同亚基的信号之间通常提供很少的光谱分辨率。当人们有兴趣在整个分子量分布范围内监测序列或量化低丰度亚基时,这些限制尤其具有挑战性。因此,我们着手研究热解-气相色谱(Py-GC)作为替代方法。Py-GC 比 NMR 更灵敏,并且在各种亚基之间提供更好的分辨率,但它确实需要校准,因为该方法不是绝对的。我们设计了一种方法,将 NMR 和 Py-GC 的数据融合在一起,以获得一组随机和嵌段聚(甲基丙烯酸甲酯-共-苯乙烯)共聚物样品的链序列和组成的定量信息,这些样品难以分析,因为 MMA 容易完全解聚。我们证明了该方法可成功用于确定随机和嵌段共聚物的链序列。此外,我们设法将 Py-GC 应用于监测随机和嵌段共聚物在整个分子量分布范围内的序列。

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