高分子材料拥有众多优良的特性,使其广泛应用于现代社会的生产、生活及高新科技等领域。对于高分子特性的研究离不开微观物理、化学结构的定量表征。在众多表征高分子结构定量信息的实验技术中,固体核磁共振(SSNMR)发挥着重要的作用。它可以定量表征高分子的化学结构、材料体系的相成分及共混组分等,从而实现了难溶高分子产物的结构鉴定,以及高分子微观物理、化学结构关联材料性能的研究。然而,传统的SSNMR定量方法(直接激发法,DD)往往需要耗费十几个小时甚至数天的实验时间。交叉极化方法(CP)虽然大大缩短了实验时间,却往往无法提供定量的信息。基于此,研究者们提出了多个定量CP的实验方法,同时满足了方法定量性和实验耗时短的需求。本文综述了传统SSNMR定量方法及几种定量的CP方法,简单介绍了每个方法的基本原理,并平行阐述了实验方法的应用范围及特点,通过部分实例展示了SSNMR定量方法在高分子材料领域的应用价值,为研究者研发新型的高分子材料提供可靠并有效的结构表征手段。
Polymers have been widely used nowadays owing to their versatile properties, which firmly relate to their molecular physical and chemical structures. Therefore, the quantitative and quasi-quantitative characterization of polymer chemical and physical structures is essential in developing novel polymer materials. Among various characterization methods, solid-state nuclear magnetic resonance (SSNMR) plays an essential role in quantitative investigation, which is capable of providing quantitative information on chemical structure of polymers, phase composition of multi-phase systems as well as component content of block copolymers or polymer blends. However, traditional SSNMR quantitative method consumes very long experimental time for most of the systems. Cross polarization (CP) method, as a widely-used SSNMR technique, is capable of providing a spectrum within much shorter time. However, CP is mostly not quantitative. Therefore, several quantitative CP methods were proposed in past years, which are both quantitative and time-saving. In this article, we present an overview of the SSNMR quantitative methods, including the traditional techniques and the newly proposed CP methods in past twelve years. Basic principle and properties of each method are elucidated. In addition, several application examples are introduced which is aimed to assist the researchers in their works of polymer quantitative study.
Contents
1 Introduction
2 SSNMR quantitative techniques and the applications for polymer study
2.1 Direct polarization techniques (DP and DD)
2.2 Cross polarization technique (CP)
2.3 Quantitative CP methods
3 Conclusion
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