Accurate compositional analysis of unknown polymer systems within ±1 wt% errors via thermogravimetry-synchronized reference-free quantitative mass spectrometry.

Compositional analysis (CA)—identification and quantification of the system constituents—is the most fundamental and decisive approach to investigate the system of interest. Pyrolysis mass spectrometry (MS) with millidalton resolution is very effective for chemical identification and directly applicable to polymer materials regardless of their solubilities; however, it is less helpful for quantification especially when the references, i.e., pure constituents, are unknown, non-isolable and thus unpreparable. To compensate this weakness, herein we propose reference-free quantitative mass spectrometry (RQMS) with enhanced quantification accuracy assisted by synchronized thermogravimetry (TG). The key to success is the conversion of MS signal intensities of pyrolyzed fragments into weight abundances via mathematically incorporated TG data. In a benchmark test using ternary polymer systems, this new framework named TG-RQMS demonstrates accurate CA within ±1 wt% errors without using any knowledge nor spectra of the references. This simple yet accurate and versatile CA method would be an invaluable tool to investigate polymer materials whose composition is hardly accessible via other analytic methods

Polymer sequencing via unsupervised learning of pyrolysis-mass spectra

A sequence—an arrangement of monomers—dominates polymer properties, as best exemplified by proteins; however, an efficient sequencing method for synthetic polymers has not been established yet. Herein, we propose a polymer sequencer based on mass spectrometry of pyrolyzed oligomeric fragments. By interpreting an observed fragment pattern as one generated from a mixture of sequence-defined copolymers, sequencing can be simplified to compositional analysis. Our key development is a reference-free quantitative mass spectrometry. The reference spectra of the hardly synthesizable sequence-defined copolymers were not actually measured but virtually inferred via unsupervised learning of the spectral dataset of easily synthesizable random copolymers. The polymer sequencer quantitatively evaluates complex sequence distribution in versatile multi-monomer systems, which would allow sequence–property correlation studies and practical sequence-controlled polymerization