Using Ion Mobility–Mass Spectrometry to Extract Physicochemical Enthalpic and Entropic Contributions from Synthetic Polymers

Abstract

Ion mobility–mass spectrometry (IM-MS) experiments are mostly used hand in hand with computational chemistry to correlate mobility measurements to the shape of the ions. Recently, we developed an automatable method to fit IM data obtained with synthetic homopolymers (i.e., collision cross sections; CCS) without resorting to computational chemistry. Here, we further develop the experimental IM data interpretation to explore physicochemical properties of a series of nine polymers and their monomer units by monitoring the relationship between the CCS and the degree of polymerization (DP). Several remarkable points of the CCS evolutions as a function of the DP were found: the first observed DP of each charge state (ΔDPfirst DP), the DPs constituting the structural rearrangements (ΔDPrearr), and the DPs at the half-rearrangement (DPhalf‑rearr). Given that these remarkable points do not rely on absolute CCS values, but on their relative evolution, they can be extracted from CCS or raw IM data without accurate IM calibration. Properties such as coordination numbers of the cations, steric hindrance, or side chain flexibility can be compared. This leads to fit parameter predictions based on the nature of the monomer unit. The interpretation of the fit parameters, extracted using solely experimental data, allows a rapid screening of the properties of the polymers.

Geometric Analysis of Shapes in Ion Mobility–Mass Spectrometry

“…Interpretations on the parameter A are the focus of a specifically dedicated paper. 16 When fitting the CCS evolutions, 16,23 the pow fit parameters of the polymer−cation complexes are found to roughly range from around 0.55 to 0.95.…”

“…The next step in the development of the CCS trend analysis 700 could now focus on more heterogeneously growing systems, 701 where the apparent densities 16,23 The polymer samples were dissolved in pure methanol (Biosolve) spiked with sodium cations (NaCl salt) to obtain concentrations of around 10 -6 to 5´10 -6 M in polymer-sodium complexes.…”

“…Such interpretations can then for example also be performed on raw data. 16,23 This creates an interpretation strategy focused on experimental data, which allows obtaining physicochemical interpretations on multiple ions at once without having to calculate atomic fine structures for every single data point aligning on a CCS trend.…”

“…They also allow building empirical interpretations based on polymer-to-polymer comparisons. 16,23 Similarly, artificial intelligence and deep learning approaches are gaining in popularity to build databases from experimental data trend analyses. 27−29 Here, we continue the development of the CCS trend analysis, by adding the structural shape dimension, in addition to the physicochemical dimension, 16,25,26 to the interpretation.…”

“…16,23 Similarly, artificial intelligence and deep learning approaches are gaining in popularity to build databases from experimental data trend analyses. 27−29 Here, we continue the development of the CCS trend analysis, by adding the structural shape dimension, in addition to the physicochemical dimension, 16,25,26 to the interpretation. For this purpose, we based our study on cation adducts of synthetic polymers where many data points can be generated (by increasing the polymer chain length, degree of polymerization, DP) without changing the essence of the interactions of the polymer−cation complexes.…”