Mass Difference Matching Unfolds Hidden Molecular Structures of Dissolved Organic Matter

Ultrahigh-resolution Fourier transform mass spectrometry (FTMS) has revealed unprecedented details of natural complex mixtures such as dissolved organic matter (DOM) on a molecular formula level, but we lack approaches to access the underlying structural complexity. We here explore the hypothesis that every DOM precursor ion is potentially linked with all emerging product ions in FTMS² experiments. The resulting mass difference (Δm) matrix is deconvoluted to isolate individual precursor ion Δm profiles and matched with structural information, which was derived from 42 Δm features from 14 in-house reference compounds and a global set of 11 477 Δm features with assigned structure specificities, using a dataset of ∼18 000 unique structures. We show that Δm matching is highly sensitive in predicting potential precursor ion identities in terms of molecular and structural composition. Additionally, the approach identified unresolved precursor ions and missing elements in molecular formula annotation (P, Cl, F). Our study provides first results on how Δm matching refines structural annotations in van Krevelen space but simultaneously demonstrates the wide overlap between potential structural classes. We show that this effect is likely driven by chemodiversity and offers an explanation for the observed ubiquitous presence of molecules in the center of the van Krevelen space. Our promising first results suggest that Δm matching can both unfold the structural information encrypted in DOM and assess the quality of FTMS-derived molecular formulas of complex mixtures in general.

Ultrafast Energy Transfer Determines the Formation of Fluorescence in DOM and Humic Substances

Humification is a ubiquitous natural process of biomass degradation that creates multicomponent systems of nonliving organic matter, including dissolved organic matter (DOM) and humic substances (HS) in water environments, soils, and organic rocks. Despite significant differences in molecular composition, the optical properties of DOM and HS are remarkably similar, and the reason for this remains largely unknown. Here, we employed fluorescence spectroscopy with (sub)picosecond resolution to elucidate the role of electronic interactions within DOM and HS. We revealed an ultrafast decay component with a characteristic decay lifetime of 0.5-1.5 ps and spectral diffusion originating from excitation energy transfer (EET) in the system. The rate of EET was positively correlated to the fraction of aromatic species and tightness of aromatic species packing. Diminishing the number of EET donor-acceptor pairs by reduction with NaBH₄ (decrease of the acceptor number), decrease of pH (decrease of the electron-donating ability), or decrease of the average particle size by filtration (less donor-acceptor pairs within a particle) resulted in a lower impact of the ultrafast component on fluorescence decay. Our results uncover the role of electronic coupling among fluorophores in the formation of DOM and HS optical properties and provide a framework for studying photophysical processes in heterogeneous systems of natural fluorophores.

A new metric for relating macroscopic chromatograms to microscopic surface dynamics: the distribution function ratio (DFR)

Heterogeneous stationary phase chemistry causes chromatographic tailing that lowers separation efficiency and complicates optimizing mobile phase conditions. Model-free metrics are attractive for assessing optimal separation conditions due to the low quantity of information required, but often do not reveal underlying mechanisms that cause tailing, for example, heterogeneous retention modes. We report a new metric, which we call the Distribution Function Ratio (DFR), based on a graphical comparison between the chromatogram and Gaussian cumulative distribution functions, achieving correspondence to ground truth surface dynamics with a single chromatogram. Using a Monte Carlo framework, we show that the DFR can predict the prevalence of heterogeneous retention modes with high precision when the relative desorption rate between modes is known, as in during surface dynamics experiments. Ground truth comparisons reveal that the DFR outperforms both the asymmetry factor and skewness by yielding a one-to-one correspondence with heterogeneous retention mode prevalence over a broad range of experimentally realistic values. Perhaps of more value, we illustrate that the DFR, when combined with the asymmetry factor and skewness, can estimate microscopic surface dynamics, providing valuable insights into surface chemistry using existing chromatographic instrumentation. Connecting ensemble results to microscopic quantities through the lens of simulation establishes a new chemistry-driven route to measuring and advancing separations.