Rebuilding from fragments Chromatographic and chemometric approaches to characterize polymeric and explosive materials

Comprehensive chemical analysis is essential to further enhance public health and safety, develop more sustainable materials, and support the energy transition. As such, the primary focus of this thesis was related to the characterization of polymeric as well as explosive materials.
The first part of the thesis revolves around challenges from the polymer industry focusing on the determination of block-length distributions (BLDs). The use of artificial intelligence (AI) for polymer characterization was first reviewed. Then, a machine learning-based algorithm was developed to accurately determine BLDs from copolymer fragment data. This algorithm was then applied to fragment data of polyamide and polyurethane products obtained by liquid chromatography (LC) coupled to tandem mass spectrometry (MS/MS).
The second part addressed challenges for characterizing explosive materials with a focus on nitrocellulose (NC) based explosives. The molecular-weight distribution (MWD) and nitration degree of NC were used to discriminate between commercial smokeless powders. A two-dimensional (2D)-LC system enabled additional quantification of the additive profile. Furthermore, a more universal 2D-LC method was developed to identify the most-common explosive traces.
The third part of the thesis covers recent trends in 2D-LC with respect to application, fundamentals, and implementation in industrial laboratories. Moreover, comprehensive 2D-LC was applied for the analysis of complex cell digests using different gradient programs to maximize the utilization of the second-dimension retention space. The effect of radial mixing to effective modulation was also investigated. The thesis ends with recent developments and applications for online sample transformation in multi-dimensional separations.