Power of Ultra Performance Liquid Chromatography/Electrospray Ionization-MS Reconstructed Ion Chromatograms in the Characterization of Small Differences in Polymer Microstructure

摘要

From simple homopolymers to functionalized, 3-dimensional structured copolymers, the complexity of polymeric materials has become more and more sophisticated. With new applications, for instance, in the semiconductor or pharmaceutical industry, the requirements for the characterization have risen with the complexity of the used polymers. For each additional distribution, an additional dimension in analysis is needed. Small, often isomeric heterogeneities in topology or microstructure can usually not be simply separated chromatographically or distinguished by any common detector but affect the properties of materials significantly. For a drug delivery system, for example, the degree of branching and branching distribution is crucial for the formation of micelles. Instead of a complicated, time-consuming, and/or expensive 2D-chromatography or ion mobility spectrometry (IMS) method, that also has its limitations, in this work, a simple approach using size exclusion chromatography (SEC) coupled with electrospray ionization (ESI) mass spectrometry is proposed. The online coupling allows the analysis of reconstructed ion chromatograms (RICs) of each degree of polymerization. While a complete separation often cannot be achieved, the derived retention times and peak widths lead to information on the existence and dispersity of heterogeneities. Although some microstructural heterogeneities like short chain branching can for large polymers be characterized with methods such as light scattering, for oligomers where the heterogeneities just start to form and their influence is at the maximum, they are inaccessible with these methods. It is also shown that with a proper calibration even quantitative information can be obtained. This method is suitable to detect small differences in, e.g., branching, 3D-structure, monomer sequence, or tacticity and could potentially be used in routine analysis to quickly determine deviations.