Revealing hidden polymeric distributions with multi-dimensional separations

Synthetic polymers play a crucial role in our daily life. To develop new products or improve existing applications, the relations between physical properties and the molecular distributions of synthetic polymers must be understood. This is a difficult task, because polymers consist of populations of chemically similar compounds, often exhibiting several distributions, including those of molecular weight (MW), chemical composition, end-group functionality, and topology (e.g. cyclization or branching). Ultimately, the mutual correlations between those distributions must be understood to establish structure-property relationships.
In this dissertation, we have aimed to develop various multi-dimensional separations and their on-line hyphenation with high-resolution mass spectrometry (HRMS) to establish a powerful platform for polymer characterization. By selecting the right (orthogonal) separation dimensions, the multidimensional distributions present in complex polymers can be deconvoluted. Such separations often revealed unknown distributions which could directly be identified with HRMS, deepening the chemical understanding of the studied material. This was often the case for many industrial samples studied throughout this dissertation, including polyether polyols, polyesters and silsesquioxanes.