Molecular dynamics simulation of ion mobility in gases

Gas-phase structure of polymer ions: Tying together theoretical approaches and ion mobility spectrometry

An increasing number of studies take advantage of ion mobility spectrometry (IMS) coupled to mass spectrometry (IMS-MS) to investigate the spatial structure of gaseous ions. Synthetic polymers occupy a unique place in the field of IMS-MS. Indeed, due to their intrinsic dispersity, they offer a broad range of homologous ions with different lengths. To help rationalize experimental data, various theoretical approaches have been described. First, the study of trend lines is proposed to derive physicochemical and structural parameters. However, the evaluation of data fitting reflects the overall behavior of the ions without reflecting specific information on their conformation. Atomistic simulations constitute another approach that provide accurate information about the ion shape. The overall scope of this review is dedicated to the synergy between IMS-MS and theoretical approaches, including computational chemistry, demonstrating the essential role they play to fully understand/interpret IMS-MS data.

The hitchhiker's guide to dynamic ion-solvent clustering: applications in differential ion mobility spectrometry

This article highlights the fundamentals of ion-solvent clustering processes that are pertinent to understanding an ion's behaviour during differential mobility spectrometry (DMS) experiments. We contrast DMS with static-field ion mobility, where separation is affected by mobility differences under the high-field and low-field conditions of an asymmetric oscillating electric field. Although commonly used in mass spectrometric (MS) workflows to enhance signal-to-noise ratios and remove isobaric contaminants, the chemistry and physics that underpins the phenomenon of differential mobility has yet to be fully fleshed out. Moreover, we are just now making progress towards understanding how the DMS separation waveform creates a dynamic clustering environment when the carrier gas is seeded with the vapour of a volatile solvent molecule (e.g., methanol). Interestingly, one can correlate the dynamic clustering behaviour observed in DMS experiments with gas-phase and solution-phase molecular properties such as hydrophobicity, acidity, and solubility. However, to create a generalized, global model for property determination using DMS data one must employ machine learning. In this article, we provide a first-principles description of differential ion mobility in a dynamic clustering environment. We then discuss the correlation between dynamic clustering propensity and analyte physicochemical properties and demonstrate that analytes exhibiting similar ion-solvent interactions (e.g., charge-dipole) follow well-defined trends with respect to DMS clustering behaviour. Finally, we describe how supervised machine learning can be used to create predictive models of molecular properties using DMS data. We additionally highlight open questions in the field and provide our perspective on future directions that can be explored.

Systematic Optimization of Ambient Ionization Ion Mobility Mass Spectrometry for Rapid Separation of Isomers

Current methods typically used for metabolite screening and disease diagnosis often require extensive sample preparation, which increases analysis time and associated costs. While ambient ionization techniques enable the analysis of various samples in complex matrices with little or no sample preparation in a short time (typically within a minute), their reduced selectivity, even when coupled with high-resolution mass spectrometers, limits their application in certain fields. In this study, we have optimized the coupling of paper spray (PS) and leaf spray (LS) ambient ionization techniques with a commercially available ion mobility mass spectrometer (IM-MS) and demonstrated the separation of geometric and constitutional isomers. Ambient ionization techniques allow simultaneous introduction and ionization of samples, while background noise and matrix interference from paper and leaf substrates are filtered out by IM separation, resulting in high sensitivity and selectivity of the PS-IM-MS and LS-IM-MS workflows. In addition, we introduced a novel approach to perform single-field collision cross section (CCS) measurements, which resulted in CCS values that differ by 0.15% and 0.25% from traditional stepped-field and single-field methods, respectively. In addition, we used advanced computational tools to confidently identify analyte structures by comparing CCS values from experimental IM measurements and theoretical calculations. These results suggest that the coupling of ambient ionization methods with ion mobility techniques enables rapid, sensitive, and highly selective analysis that can be used in different fields, such as agrochemical screening and disease diagnostics.

Field Induced Fragmentation (Fif) Spectra of Oxygen Containing Volatile Organic Compounds with Reactive Stage Tandem Ion Mobility Spectrometry and Functional Group Classification by Neural Network Analysis

Mobility isolated spectra were obtained for protonated monomers of 42 volatile oxygen containing organic compounds at ambient pressure using a tandem ion mobility spectrometer with a reactive stage between drift regions. Fragment ions of protonated monomers of alcohols, acetates, aldehydes, ketones, and ethers were produced in the reactive stage using a 3.3 MHz symmetrical sinusoidal waveform with an amplitude of 1.4 kV and mobility analyzed in a 19 mm long drift region. The resultant field induced fragmentation (FIF) spectra included residual intensities for protonated monomers and fragment ions with characteristic drift times and peak intensities, associated with ion mass and chemical class. High efficiency of fragmentation was observed with single bond cleavage of alcohols and in six-member ring rearrangements of acetates. Fragmentation was not observed, or seen weakly, with aldehydes, ethers, and ketones due to their strained four-member ring transition states. Neural networks were trained to categorize spectra by chemical class and tested with FIF spectra of both familiar and unfamiliar compounds. Rates of categorization were class dependent with best performance for alcohols and acetates, moderate performance for ketones, and worst performance for ethers and aldehydes. Trends in the rates of categorization within a chemical family can be understood as steric influences on the energy of activation for ion fragmentation. Electric fields greater than 129 Td or new designs of reactive stages with improved efficiency of fragmentation will be needed to extend the practice of reactive stage tandem IMS to an expanded selection of volatile organic compounds.

How Hot Are Your Ions in Differential Mobility Spectrometry?

Ions can experience significant field-induced heating in a differential mobility cell. To investigate this phenomenon, the fragmentation of several para-substituted benzylpyridinium "thermometer" ions (R = OMe, Me, F, Cl, H, CN) was monitored in a commercial differential mobility spectrometer (DMS). The internal energy of each benzylpyridinium derivative was characterized by monitoring the degree of fragmentation to obtain an effective temperature, T_eff, which corresponds to a temperature consistent with treating the observed fragmentation ratio using a unimolecular dissociation rate weighted by a Boltzmann distribution at a temperature T. It was found that ions are sufficiently thermalized after initial activation from the ESI process to the temperature of the bath gas, T_bath. Once a critical field strength was surpassed, significant fragmentation of the benzylpyridinium ions was detected. At the maximum bath gas temperature (450 K) and separation voltage (SV; 4400 V) for our instrument, T_eff for the benzylpyridinium derivatives ranged from 664 ± 9 K (p-OMe) to 759 ± 17 K (p-H). The extent of activation at a given SV depends on the ion's mass, degrees of freedom, (N_DoF), and collision frequency as represented by the ion's collision cross section. Plots of T_eff vs the product of ion mass and N_DoF and the inverse of collision cross section produce strong linear relationships. This provides an attractive avenue to estimate ion temperatures at a given SV using only intrinsic properties. Moreover, experimentally determined T_eff correlate with theoretically predicted T_eff using with a self-consistent method based on two-temperature theory. The various instrumental and external parameters that influence T_eff are additionally discussed.

Composition and charge state influence on the ion-neutral collision cross sections of protonated N-linked glycopeptides: an experimental and theoretical deconstruction of coulombic repulsion vs. charge solvation effects

Ion mobility spectrometry (IMS) is of significant interest as a platform for glycoanalysis. While much attention has been focused on the resolution of isomeric carbohydrates and glycoconjugates, another appealing aspect of IMS is the ability to sort different classes of biomolecules into distinct regions of mass vs. mobility space. This capability has potential to greatly simplify glycoproteomic analyses, as glycosylated and non-glycosylated peptides can be rapidly partitioned in the gas phase. Nevertheless, the physical and chemical characteristics of glycopeptides that dictate their mass vs. mobility loci have yet to be systematically investigated. This report presents an IMS study of model protonated glycopeptide ions with systematically varied oligosaccharide topologies, polypeptide sequences, and charge states. In all, over 110 ion-neutral collision cross sections (CCSs) were measured and analyzed in the context of the physicochemical characteristics of the analytes. Glycan size and composition emerged as a decisive factor in dictating the CCS space occupied by the glycopeptides and exerted this influence in a charge state dependent fashion. Furthermore, elongation of the glycan group was found to either increase or decrease glycopeptide CCSs depending on the ion charge state and the size of the glycan. Molecular dynamics (MD) simulations of the gas phase structures and CCSs of selected glycopeptides revealed that the experimental observations were consistent with a glycan size and charge state dependent interplay between destabilizing coulombic repulsion effects (tending to result in more extended structures) and stabilizing charge solvation effects in which the glycan plays a major role (tending to result in more compact structures). Taken together, these IMS and MD findings suggest the possibility of predicting and delineating glycopeptide-enriched regions of mass vs. mobility space for applications in glycoproteomics.

A First Principle Model of Differential Ion Mobility: the Effect of Ion-Solvent Clustering

The use of differential mobility spectrometry (DMS) as a separation tool prior to mass analysis has increased in popularity over the years. However, the fundamental principles behind the difference between high- and low-field mobility is still a matter of debate-especially regarding the strong impact of solvent molecules added to the gas phase in chemically modified DMS environments. In this contribution, we aim to present a thorough model for the determination of the ion mobility over a wide range of field strengths and subsequent calculation of DMS dispersion plots. Our model relies on first principle calculations only, incorporating the modeling of the "hard-sphere" mobility, the change in CCS with field strength, and the degree of clustering of solvent molecules to the ion. We show that all three factors have to be taken into account to qualitatively predict dispersion plots. In particular, type A behavior (i.e., strong clustering) in DMS can only be explained by a significant change of the mean cluster size with field strengths. The fact that our model correctly predicts trends between differently strong binding solvents, as well as the solvent concentration and the background gas temperature, highlights the importance of clustering for differential mobility.