School of Natural Sciences, Australian Centre for Research on Separation Science (ACROSS), University of Western Sydney, Australia.
J Sep Sci. 2010 Nov;33(22):3564-70. doi: 10.1002/jssc.201000511.
Size-exclusion chromatography (SEC or GPC) is the most widely used separation method to characterize polymers. The high level of complexity of most polymeric materials necessitates the use of not only concentration-sensitive detection but also structure-sensitive detection. Viscometry is usually used in conjunction with a concentration-sensitive detector and universal calibration to determine molecular weights of polymers. Goldwasser proposed to use a viscometer as a single detector to determine number-average molecular weights, M(n) (ACS Symposium Series, 521, 143). The method is particularly of interest when concentration-sensitive detection is not available, because the sample is isorefractive or not UV-absorbing, or because composition is not constant (copolymers). It has known very little applications so far. It actually does not only allow determining M(n), but also the number hydrodynamic volume distribution. This opens a wider range of applications for the Goldwasser method. Size-exclusion chromatography only yields inaccurate molecular weight distributions for some complex branched polymers. Hydrodynamic volume distributions have then a strong potential for comparative studies owing to their far higher accuracy. Our experimental tests highlight the fact that the method is highly sensitive to noise and careful optimization of the injection concentration is needed, but number distribution can be obtained as well as M(n).
体积排阻色谱法（SEC 或 GPC）是最广泛用于对聚合物进行特性分析的分离方法。大多数聚合材料的高度复杂性要求不仅使用浓度敏感的检测方法，而且还需要使用结构敏感的检测方法。通常使用粘度计与浓度敏感的检测器和通用校准相结合来确定聚合物的分子量。Goldwasser 提出使用粘度计作为单一检测器来确定数均分子量 M(n)（ACS 研讨会系列，521，143）。当无法进行浓度敏感检测时，该方法特别有趣，因为样品具有等折射度或不具有紫外吸收性，或者因为组成不恒定（共聚物）。到目前为止，它的应用非常有限。实际上，它不仅可以确定 M(n)，还可以确定数均流体力学体积分布。这为 Goldwasser 方法开辟了更广泛的应用领域。体积排阻色谱法仅对某些复杂支化聚合物产生不准确的分子量分布。由于其高得多的准确性，流体力学体积分布具有用于比较研究的强大潜力。我们的实验测试强调了这样一个事实，即该方法对噪声非常敏感，需要仔细优化进样浓度，但也可以获得数量分布和 M(n)。
