Reference-free quantitative mass spectrometry in the presence of nonlinear distortion caused by in situ chemical reactions among constituents

Abstract

Materials performance is primarily influenced by chemical composition, making compositional analysis (CA) essential in materials science. Traditional quantitative mass spectrometry, which deconvolutes analyte spectra into reference spectra, struggles with reactive systems where spectral variations occur, such as peak shifts and new peak emergences. Additionally, obtaining reference spectra for all pure constituents is often impractical. To address these challenges, I propose nonlinear reference-free quantitative mass spectrometry (NL-RQMS). This method simultaneously determines composition, reference spectra, and nonlinear interaction effects directly from a spectral dataset of mixtures, eliminating the need for prior reference spectra. In a benchmark test on ternary reactive polymers of epoxy and amines, NL-RQMS inferred compositions with an error margin of just 3 wt%, significantly outperforming the 6 wt% error margin of linear RQMS. The inferred interaction terms clearly indicate in situ reactions between epoxy and amine moieties. This framework enables accurate compositional analysis without prior knowledge of the constituents, even in systems with interactive components, and holds significant potential for applications such as grading recycled plastics, where pristine materials, degradation compounds, and stabilizers interact complexly, causing nonlinear spectral distortions.