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

• Published in: Journal of Chromatography A 2022-05, Vol.1670, p.462973, Article 462973
• Main Authors: Knol, Wouter C., Gruendling, Till, Schoenmakers, Peter J., Pirok, Bob W.J., Peters, Ron A.H.
• Format: Article
• Language: English
• Subjects: composite polymers; gas chromatography; gel chromatography; molecular weight; Polymer; pyrolysis; Pyrolysis-gas chromatography; Sequence; sequence analysis; Size exclusion chromatography; spectral analysis
• ISSN: 0021-9673
• DOI: 10.1016/j.chroma.2022.462973

•Sequence heterogeneity determination across the molar-mass distribution by pyrolysis-GC was achieved for the first time.•Sensitive analysis of dimeric units by pyrolysis GC-MS allowed block co-polymers to be sequenced.•sophisticated calibration approach was established utilizing the superior sensitivity and resolution of py-GC over 13C-NMR to obtain accurate quantitative sequence information on styrene-methyl methacrylate co-polymers. 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 13C-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.