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

Pursuing drug laboratories: Analysis of drug precursors with High Kinetic Energy Ion Mobility Spectrometry

High Kinetic Energy Ion Mobility Spectrometry (HiKE-IMS) is a technique for rapid and reliable detection of trace compounds down to ppbV-levels within one second. Compared to classical IMS operating at ambient pressure and providing the ion mobility at low electric fields, HiKE-IMS can also provide the analyte-specific field dependence of the ion mobility and a fragmentation pattern at high reduced electric field strengths. The additional information about the analyte obtained by varying the reduced electric field strength can contribute to reliable detection. Furthermore, the reduced number of ion-molecule reactions at the low operating pressure of 10 - 40 mbar and the shorter reaction times reduce the impact of competing ion-molecule reactions that can cause false negatives. In this work, we employ HiKE-IMS for the analysis of phenyl-2-propanone (P2P) and other precursor chemicals used for synthesis of methamphetamine and amphetamine. The results show that the precursor chemicals exhibit different behavior in HiKE-IMS. Some precursors form a single significant ion species, while others readily form a fragmentation pattern. Nevertheless, all drug precursors can be distinguished from each other, from the reactant ions and from interfering compounds. In particular, the field-dependent ion mobility as an additional separation dimension aids identification, potentially reducing the number of false positive alarms in field applications. Furthermore, the analysis of a seized illicit P2P sample shows that even low levels of P2P can be detected despite the complex background present in the headspace of real samples.

Reliable Detection of Chemical Warfare Agents Using High Kinetic Energy Ion Mobility Spectrometry

High Kinetic Energy Ion Mobility Spectrometers (HiKE-IMS) ionize and separate ions at reduced pressures of 10-40 mbar and over a wide range of reduced electric field strengths E/N of up to 120 Td. Their reduced operating pressure is distinct from that of conventional drift tube ion mobility spectrometers that operate at ambient pressure for trace compound detection. High E/N can lead to a field-induced fragmentation pattern that provides more specific structural information about the analytes. In addition, operation at high E/N values adds the field dependence of ion mobility as an additional separation dimension to low-field ion mobility, making interfering compounds less likely to cause a false positive alarm. In this work, we study the chemical warfare agents tabun (GA), sarin (GB), soman (GD), cyclosarin (GF) and sulfur mustard (HD) in a HiKE-IMS at variable E/N in both the reaction and the drift region. The results show that varying E/N can lead to specific fragmentation patterns at high E/N values combined with molecular signals at low E/N. Compared to the operation at a single E/N value in the drift region, the variation of E/N in the drift region also provides the analyte-specific field dependence of ion mobility as additional information. The accumulated data establish a unique fingerprint for each analyte that allows for reliable detection of chemical warfare agents even in the presence of interfering compounds with similar low-field ion mobilities, thus reducing false positives.

On the Arrival Time Distribution of Reacting Systems in Ion Mobility Spectrometry

Ion mobility spectrometry (IMS) is a widely used gas-phase separation technique, particularly when coupled with mass spectrometry (MS). Modern IMS instruments often apply elevated reduced field strengths for improved ion separation and ion focusing. These alter the collision dynamics and further drive ion reaction processes that can change the analyte's structure. As a result, the measured arrival time distribution (ATD) can change with the applied reduced field strengths. In this work, we systematically study how the ion collision dynamics and the ion reaction dynamics, as a function of the reduced field strength, can alter the ATD. To this end, we investigate 2,6-di-tert-butylpyridine, methanol, and ethyl acetate using a home-built drift tube IMS coupled to a home-built MS and extensive first-principles Monte Carlo modeling. We show how elevated reduced field strengths can actually lower resolving power through increased ion diffusion and how the field dependency of the ion mobility can introduce uncertainties to collision cross sections (CCS) calculated from the measured mobilities. On top of the collision dynamics, we show how chemical transformation processes that alter the analyte's CCS, e.g., dynamic clustering or fragmentation, can lead to broadened, shifted, or non-Gaussian ATDs and how sensitive these processes are to the applied field strengths. We highlight how first-principles ion dynamics simulations can help to understand and even harness the mentioned effects.

Structural studies of supramolecular complexes and assemblies by ion mobility mass spectrometry

Recent advances in instrumentation and development of computational strategies for ion mobility mass spectrometry (IM-MS) studies have contributed to an extensive growth in the application of this analytical technique to comprehensive structural description of supramolecular systems. Apart from the benefits of IM-MS for interrogation of intrinsic properties of noncovalent aggregates in the experimental gas-phase environment, its merits for the description of native structural aspects, under the premises of having maintained the noncovalent interactions innate upon the ionization process, have attracted even more attention and gained increasing interest in the scientific community. Thus, various types of supramolecular complexes and assemblies relevant for biological, medical, material, and environmental sciences have been characterized so far by IM-MS supported by computational chemistry. This review covers the state-of-the-art in this field and discusses experimental methods and accompanying computational approaches for assessing the reliable three-dimensional structural elucidation of supramolecular complexes and assemblies by IM-MS.

Non-destructive method to classify walnut kernel freshness from volatile organic compound (VOC) emissions using gas chromatography-differential mobility spectrometry (GC-DMS) and machine learning analysis

Analysis of volatile organic compounds (VOCs) can be an effective strategy to inspect the quality of horticultural commodities and following their degradation. In this work, we report that VOCs emitted by walnuts can be studied using gas chromatography-differential mobility spectrometry (GC-DMS), and those GC-DMS data can be analyzed to predict the rancidity of walnuts, i.e., classify walnuts into grades of freshness. Walnut kernels were assigned a class n depending on their level of freshness as determined by a peroxide assay. VOC samples were analyzed using GC-DMS. From these VOC data, a partial least square regression (PLSR) model provided a freshness prediction value m , which corresponded to the rancid class n when m = n ± 0.5 . The PLSR model had an accuracy of 80% to predict walnut grade and demonstrated a minimal root mean squared error of 0.42 for the m response variables (representative of walnut grade) with the GC-DMS data. We also conducted gas chromatography-mass spectrometry (GC-MS) experiments to identify volatiles that emerged or were enhanced with more rancid walnuts. The findings of the GC-MS study of walnut VOCs align excellently with the GC-DMS study. Based on our results, we conclude that a GC-DMS device deployed with a pre-trained machine learning model can be a very effective device for classifying walnut grades in the industry.

Evaluation of Hydrogen-Deuterium Exchange during Transient Vapor Binding of MeOD with Model Peptide Systems Angiotensin II and Bradykinin

The advancement of hybrid mass spectrometric tools as an indirect probe of molecular structure and dynamics relies heavily upon a clear understanding between gas-phase ion reactivity and ion structural characteristics. This work provides new insights into gas-phase ion-neutral reactions of the model peptides (i.e., angiotensin II and bradykinin) on a per-residue basis by integrating hydrogen/deuterium exchange, ion mobility, tandem mass spectrometry, selective vapor binding, and molecular dynamics simulations. By comparing fragmentation patterns with simulated probabilities of vapor uptake, a clear link between gas-phase hydrogen/deuterium exchange and the probabilities of localized vapor association is established. The observed molecular dynamics trends related to the sites and duration of vapor binding track closely with experimental observation. Additionally, the influence of additional charges and structural characteristics on exchange kinetics and ion-neutral cluster formation is examined. These data provide a foundation for the analysis of solvation dynamics of larger, native-like conformations of proteins in the gas phase.

Bridging the Gap between Differential Mobility, Log S, and Log P Using Machine Learning and SHAP Analysis

Aqueous solubility, log S, and the water-octanol partition coefficient, log P, are physicochemical properties that are used to screen the viability of drug candidates and to estimate mass transport in the environment. In this work, differential mobility spectrometry (DMS) experiments performed in microsolvating environments are used to train machine learning (ML) frameworks that predict the log S and log P of various molecule classes. In lieu of a consistent source of experimentally measured log S and log P values, the OPERA package was used to evaluate the aqueous solubility and hydrophobicity of 333 analytes. With ion mobility/DMS data (e.g., CCS, dispersion curves) as input, we used ML regressors and ensemble stacking to derive relationships with a high degree of explainability, as assessed via SHapley Additive exPlanations (SHAP) analysis. The DMS-based regression models returned scores of R² = 0.67 and RMSE = 1.03 ± 0.10 for log S predictions and R² = 0.67 and RMSE = 1.20 ± 0.10 for log P after 5-fold random cross-validation. SHAP analysis reveals that the regressors strongly weighted gas-phase clustering in log P correlations. The addition of structural descriptors (e.g., # of aromatic carbons) improved log S predictions to yield RMSE = 0.84 ± 0.07 and R² = 0.78. Similarly, log P predictions using the same data resulted in an RMSE of 0.83 ± 0.04 and R² = 0.84. The SHAP analysis of log P models highlights the need for additional experimental parameters describing hydrophobic interactions. These results were achieved with a smaller dataset (333 instances) and minimal structural correlation compared to purely structure-based models, underscoring the value of employing DMS data in predictive models.

MobCal-MPI 2.0: an accurate and parallelized package for calculating field-dependent collision cross sections and ion mobilities

Ion mobility spectrometry (IMS), which can be employed as either a stand-alone instrument or coupled to mass spectrometry, has become an important tool for analytical chemistry. Because of the direct relation between an ion's mobility and its structure, which is intrinsically related to its collision cross section (CCS), IMS techniques can be used in tandem with computational tools to elucidate ion geometric structure. Here, we present MobCal-MPI 2.0, a software package that demonstrates excellent accuracy (RMSE 2.16%) and efficiency in calculating low-field CCSs via the trajectory method (≤30 minutes on 8 cores for ions with ≤70 atoms). MobCal-MPI 2.0 expands on its predecessor by enabling the calculation of high-field mobilities through the implementation of the 2^nd order approximation to two-temperature theory (2TT). By further introducing an empirical correction to account for deviations between 2TT and experiment, MobCal-MPI 2.0 can compute accurate high-field mobilities that exhibit a mean deviation of <4% from experimentally measured values. Moreover, the velocities used to sample ion-neutral collisions were updated from a weighted to a linear grid, enabling the near-instantaneous evaluation of mobility/CCS at any effective temperature from a single set of N₂ scattering trajectories. Several enhancements made to the code are also discussed, including updates to the statistical analysis of collision event sampling and benchmarking of overall performance.

Validation of Field-Dependent Ion-Solvent Cluster Modeling via Direct Measurement of Cluster Size Distributions

Ion mobility spectrometry is widely used in analytical chemistry, either as a stand-alone technique or coupled to mass spectrometry. Ions in the gas phase tend to form loosely bound clusters with surrounding solvent vapors, artificially increasing the collisional cross section and the mass of the ion. This, in turn, affects ion mobility and influences separation. Further, ion-solvent clusters play an important role in most ionization mechanisms occurring in the gas phase. Consequently, a deeper understanding of ion-solvent cluster association and dissociation processes is desirable to guide experimental design and interpretation. A few computational models exist, which aim to describe the amount of clustering as a function of the reduced electric field strength, bath gas pressure and temperature, and the chemical species probed. It is especially challenging to model ion mobility under high reduced electrical field strengths due to the nonthermal conditions created by the field. In this work, we aim to validate a recently proposed first-principles model by comparing its predictions with direct measurements of cluster size distributions over a range of 20-120 Td as observed using a High Kinetic Energy Ion Mobility Spectrometer coupled to a mass spectrometer (HiKE-IMS-MS). By studying H⁺(H₂O)_n, [MeOH + H + n(H₂O)]⁺, [ACE + H + n(H₂O)]⁺, and [PhNH₂ + H + n(H₂O)]⁺ as test systems, we find very good agreement between model and experiment, supporting the validity of the computational workflow. Further, the detailed information gained from the modeling yields important insights into the cluster dynamics within the HiKE-IMS, allowing for better interpretation of the measured ion mobility spectra.

First-Principles Modeling of Preferential Solvation in Mixed-Modifier Differential Mobility Spectrometry

Differential mobility spectrometry (DMS) separates ions based on mobility differences between high and low electric field conditions. To enhance resolution, solvents such as water and acetonitrile are often used to modify the collision environment and take advantage of differing dynamic clustering behavior between analytes that coelute in hard-sphere environments (e.g., N₂). When binary solvent mixtures are used to modify the DMS environment, one solvent can have a dominant influence over the other with respect to ion trajectories. For example, for quinoline derivatives, a 9:1 water:acetonitrile solvent mixture exhibited identical behavior to an environment containing only acetonitrile as a modifier. It was hypothesized that this effect arises due to the significantly different binding strengths of the two solvents. Here, we utilize a first-principles model of DMS to study analytes in single and binary solvent mixtures and explore the effects governing the dominance of one solvent over the other. Computed DMS dispersion curves of quinoline derivatives are in excellent agreement with those measured experimentally. For mixed-modifier environments, the predicted cluster populations show a clear preferential solvation of ions with the stronger binding solvent. The influence of ion-solvent binding energies, solvent concentration, and solvent molecule size is discussed in the context of the observed DMS behavior. This work can guide the usage of binary solvent mixtures for improving ion separations in cases where compounds coelute in pure N₂ and in single-solvent modifier environments. Moreover, our results indicate that binary solvent mixtures can be used to create a relative scale for solvent binding energies.

Kinetics in DMS: Modeling Clustering and Declustering Reactions

Differential mobility spectrometry (DMS) uses high-frequency oscillating electrical fields to harness the differential mobility of ions for separating complex sample mixtures prior to detection. To increase the resolving power, a dynamic microsolvation environment is often created by introducing solvent vapors. Here, relatively large clusters are formed at low-field conditions which then evaporate to form smaller clusters at high-field conditions. The kinetics of these processes as the electrical field strength oscillates are not well studied. Here, we develop a computational framework to investigate how the different reactions (cluster association, cluster dissociation, and fast conformational changes) behave at different field strengths. We aim to better understand these processes, their effect on experimental outcomes, and whether DMS model accuracy is improved via incorporating their description. We find that cluster association and dissociation reactions for typical ion-solvent pairs are fast compared to the time scale of the varying separation fields usually used. However, low solvent concentration, small dipole moments, and strong ion-solvent binding can result in reaction rates small enough that a lag is observed in the ion's DMS response. This can yield differences of several volts in the compensation voltages required to correct ion trajectories for optimal transmission. We also find that the proposed kinetic approach yields generally better agreement with experiment than using a modified Boltzmann weighting scheme. Thus, this work provides insights into the chemical dynamics occurring within the DMS cell while also increasing the accuracy of dispersion plot predictions.

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.

Improved First-Principles Model of Differential Mobility Using Higher Order Two-Temperature Theory

Differential mobility spectrometry is a separation technique that may be applied to a variety of analytes ranging from small molecule drugs to peptides and proteins. Although rudimentary theoretical models of differential mobility exist, these models are often only applied to small molecules and atomic ions without considering the effects of dynamic microsolvation. Here, we advance our theoretical description of differential ion mobility in pure N₂ and microsolvating environments by incorporating higher order corrections to two-temperature theory (2TT) and a pseudoequilibrium approach to describe ion-neutral interactions. When comparing theoretical predictions to experimentally measured dispersion plots of over 300 different compounds, we find that higher order corrections to 2TT reduce errors by roughly a factor of 2 when compared to first order. Model predictions are accurate especially for pure N₂ environments (mean absolute error of 4 V at SV = 4000 V). For strongly clustering environments, accurate thermochemical corrections for ion-solvent clustering are likely required to reliably predict differential ion mobility behavior. Within our model, general trends associated with clustering strength, solvent vapor concentration, and background gas temperature are well reproduced, and fine structure visible in some dispersion plots is captured. These results provide insight into the dynamic ion-solvent clustering process that underpins the phenomenon of differential ion mobility.

Protonation-Induced Chirality Drives Separation by Differential Ion Mobility Spectrometry

Upon development of a workflow to analyze (±)-Verapamil and its metabolites using differential mobility spectrometry (DMS), we noticed that the ionogram of protonated Verapamil consisted of two peaks. This was inconsistent with its metabolites, as each exhibited only a single peak in the respective ionograms. The unique behaviour of Verapamil was attributed to protonation at its tertiary amino moiety, which generated a stereogenic quaternary amine. The introduction of additional chirality upon N-protonation of Verapamil renders four possible stereochemical configurations for the protonated ion: ( R,R ), ( S,S ), ( R,S ), or ( S,R ). The ( R,R )/( S,S ) and ( R,S )/( S,R ) enantiomeric pairs are diastereomeric and thus exhibit unique conformations that are resolvable by linear and differential ion mobility techniques.  Protonation-induced chirality appears to be a general phenomenon, as N -protonation of 12 additional chiral amines generated diastereomers that were readily resolved by DMS.

UVPD spectroscopy of differential mobility-selected prototropic isomers of protonated adenine

Two prototropic isomers of adenine are formed in an electrospray ion source and are resolved spatially in a differential mobility spectrometer before detection in a triple quadrupole mass spectrometer. Each isomer is gated in CV space before being trapped in the linear ion trap of the modified mass spectrometer, where they are irradiated by the tuneable output of an optical parametric oscillator and undergo photodissociation to form charged fragments with m/z 119, 109, and 94. The photon-normalised intensity of each fragmentation channel is measured and the action spectra for each DMS-gated tautomer are obtained. Our analysis of the action spectra, aided by calculated vibronic spectra and thermochemical data, allow us to assign the two signals in our measured ionograms to specific tautomers of protonated adenine.

Predicting differential ion mobility behaviour in silico using machine learning

Although there has been a surge in popularity of differential mobility spectrometry (DMS) within analytical workflows, determining separation conditions within the DMS parameter space still requires manual optimization. A means of accurately predicting differential ion mobility would benefit practitioners by significantly reducing the time associated with method development. Here, we report a machine learning (ML) approach that predicts dispersion curves in an N2 environment, which are the compensation voltages (CVs) required for optimal ion transmission across a range of separation voltages (SVs) between 1500 to 4000 V. After training a random-forest based model using the DMS information of 409 cationic analytes, dispersion curves were reproduced with a mean absolute error (MAE) of ≤ 2.4 V, approaching typical experimental peak FWHMs of ±1.5 V. The predictive ML model was trained using only m/z and ion-neutral collision cross section (CCS) as inputs, both of which can be obtained from experimental databases before being extensively validated. By updating the model via inclusion of two CV datapoints at lower SVs (1500 V and 2000 V) accuracy was further improved to MAE ≤ 1.2 V. This improvement stems from the ability of the "guided" ML routine to accurately capture Type A and B behaviour, which was exhibited by only 2% and 17% of ions, respectively, within the dataset. Dispersion curve predictions of the database's most common Type C ions (81%) using the unguided and guided approaches exhibited average errors of 0.6 V and 0.1 V, respectively.

Determining Collision Cross Sections from Differential Ion Mobility Spectrometry

The experimental determination of ion-neutral collision cross sections (CCSs) is generally confined to ion mobility spectrometry (IMS) technologies that operate under the so-called low-field limit or those that enable empirical calibration strategies (e.g., traveling wave IMS; TWIMS). Correlation of ion trajectories to CCS in other non-linear IMS techniques that employ dynamic electric fields, such as differential mobility spectrometry (DMS), has remained a challenge since its inception. Here, we describe how an ion's CCS can be measured from DMS experiments using a machine learning (ML)-based calibration. The differential mobility of 409 molecular cations (m/z: 86-683 Da and CCS 110-236 Å2) was measured in a N2 environment to train the ML framework. Several open-source ML routines were tested and trained using DMS-MS data in the form of the parent ion's m/z and the compensation voltage required for elution at specific separation voltages between 1500 and 4000 V. The best performing ML model, random forest regression, predicted CCSs with a mean absolute percent error of 2.6 ± 0.4% for analytes excluded from the training set (i.e., out-of-the-bag external validation). This accuracy approaches the inherent statistical error of ∼2.2% for the MobCal-MPI CCS calculations employed for training purposes and the <2% threshold for matching literature CCSs with those obtained on a TWIMS platform.

Clustering and Nonclustering Modifier Mixtures in Differential Mobility Spectrometry for Multidimensional Liquid Chromatography Ion Mobility-Mass Spectrometry Analysis

Modifiers provide fast and reliable tuning of separation in differential mobility spectrometry (DMS). DMS selectivity for separating isomeric molecules depends on the clustering modifier concentration, which is typically 1.5-3 mol % ratio of isopropanol or ethanol in nitrogen. Low concentrations (0.1%) of isopropanol were found to improve resolution and sensitivity but at the cost of practicality and robustness. Replacing the single-channel DMS pump with a binary high-performance liquid chromatography (HPLC) pump enabled the generation of modifier mixtures at a constant flow rate using an isocratic or gradient mode, and the analytical benefits of the system were investigated considering cyclohexane, n-hexane, or n-octane as nonclustering modifiers and isopropanol or ethanol as clustering modifiers. It was found that clustering and nonclustering modifier mixtures enable optimization of selectivity, resolution, and sensitivity for different positional isomers and diastereoisomers. Data further suggested different ion separation mechanisms depending on the modifier ratios. For 85 analytes, the absolute difference in compensation voltages (CoVs) between pure nitrogen and cyclohexane at 1.5 mol % ratio was below 4 V, demonstrating its potential as a nonclustering modifier. Cyclohexane's nonclustering behavior was further supported by molecular modeling using density functional theory (DFT) and calculated cluster binding energies, showing positive ΔG values. The ability to control analyte CoVs by adjusting modifier concentrations in isocratic and gradient modes is beneficial for optimizing multidimensional LCxDMS-MS. It is fast and effective for manipulating the DMS scanning window size to realize shorter mass spectrometry (MS) acquisition cycle times while maintaining a sufficient number of CoV steps and without compromising DMS separation performance.

The Charge-State and Structural Stability of Peptides Conferred by Microsolvating Environments in Differential Mobility Spectrometry

The presence of solvent vapor in a differential mobility spectrometry (DMS) cell creates a microsolvating environment that can mitigate complications associated with field-induced heating. In the case of peptides, the microsolvation of protonation sites results in a stabilization of charge density through localized solvent clustering, sheltering the ion from collisional activation. Seeding the DMS carrier gas (N₂) with a solvent vapor prevented nearly all field-induced fragmentation of the protonated peptides GGG, AAA, and the Lys-rich Polybia-MP1 (IDWKKLLDAAKQIL-NH₂). Modeling the microsolvation propensity of protonated n-propylamine [PrNH₃]⁺, a mimic of the Lys side chain and N-terminus, with common gas-phase modifiers (H₂O, MeOH, EtOH, iPrOH, acetone, and MeCN) confirms that all solvent molecules form stable clusters at the site of protonation. Moreover, modeling populations of microsolvated clusters indicates that species containing protonated amine moieties exist as microsolvated species with one to six solvent ligands at all effective ion temperatures (T_eff) accessible during a DMS experiment (ca. 375-600 K). Calculated T_eff of protonated GGG, AAA, and Polybia-MPI using a modified two-temperature theory approach were up to 86 K cooler in DMS environments seeded with solvent vapor compared to pure N₂ environments. Stabilizing effects were largely driven by an increase in the ion's apparent collision cross section and by evaporative cooling processes induced by the dynamic evaporation/condensation cycles incurred in the presence of an oscillating electric separation field. When the microsolvating partner was a protic solvent, abstraction of a proton from [MP1 + 3H]³⁺ to yield [MP1 + 2H]²⁺ was observed. This result was attributed to the proclivity of protic solvents to form hydrogen-bond networks with enhanced gas-phase basicity. Collectively, microsolvation provides analytes with a solvent "air bag," whereby charge reduction and microsolvation-induced stabilization were shown to shelter peptides from the fragmentation induced by field heating and may play a role in preserving native-like ion configurations.

Probing Gas-Phase-Clustering Thermodynamics with Ion Mobility-Mass Spectrometry: Association Energies of Phenylalanine Ions with Gas-Phase Alcohols

Vapor assisted mobility shift measurements were made with atmospheric pressure drift-tube ion mobility-mass spectrometry (IM-MS) to determine the thermodynamic properties of weakly bound ion-molecule clusters formed from protonated phenylalanine and neutral vapor molecules with hydroxyl functional groups. Relative binding energies and gas-phase association energies of amino acid ions clustered with small organic molecules have been established previously using high-pressure mass spectrometry. However, the issue of volatility largely prohibits the use of high-pressure mass spectrometry for the determination of gas-phase associations of amino acid ions clustered with neutral vapor molecules in many instances. In contrast, ion mobility measurements can be made at atmospheric pressure with volatile vapor additives near and above their boiling points, providing access to clustering equilibria not possible using high-vacuum techniques. In this study, we report the gas-phase association energies, enthalpies, and entropies for a protonated phenylalanine ion clustered with three neutral vapor molecules: 2-propanol, 1-butanol, and 2-pentanol based upon measurements at temperatures ranging from 120 to 180 °C. The gas-phase enthalpy and entropy changes ranged between -4 to -7 kcal/mol and -3 to 6 cal/(mol K), respectively. We found enthalpically favored ion-neutral cluster reactions for phenylalanine with entropic barriers for the formation of phenylalanine-1-butanol and phenylalanine-2-pentanol cluster ions, while phenylalanine-2-propanol cluster ion formation is both enthalpically and (weakly) entropically favorable. Under the measurement conditions examined, phenylalanine-vapor modifier cluster ion formation is clearly observed via shifts in the drift time for the three test vapor molecules. In comparison, negligible shifts in mobility are observed for protonated arginine exposed to the same vapor modifiers.

Fast Quantification Without Conventional Chromatography, The Growing Power of Mass Spectrometry

Mass spectrometry (MS) in hyphenated techniques is widely accepted as the gold standard quantitative tool in life sciences. However, MS possesses intrinsic analytical capabilities that allow it to be a stand-alone quantitative technique, particularly with current technological advancements. MS has a great potential for simplifying quantitative analysis without the need for tedious chromatographic separation. Its selectivity relies on multistage MS analysis (MSⁿ), including tandem mass spectrometry (MS/MS), as well as the ever-growing advancements of high-resolution MS instruments. This perspective describes various analytical platforms that utilize MS as a stand-alone quantitative technique, namely, flow injection analysis (FIA), matrix assisted laser desorption ionization (MALDI), including MALDI-MS imaging and ion mobility, particularly high-field asymmetric waveform ion mobility spectrometry (FAIMS). When MS alone is not capable of providing reliable quantitative data, instead of conventional liquid chromatography (LC)-MS, the use of a guard column (i.e., fast chromatography) may be sufficient for quantification. Although the omission of chromatographic separation simplifies the analytical process, extra procedures may be needed during sample preparation and clean-up to address the issue of matrix effects. The discussion of this manuscript focuses on key parameters underlying the uniqueness of each technique for its application in quantitative analysis without the need for a chromatographic separation. In addition, the potential for each analytical strategy and its challenges are discussed as well as improvements needed to render them as mainstream quantitative analytical tools. Overcoming the hurdles for fully validating a quantitative method will allow MS alone to eventually become an indispensable quantitative tool for clinical and toxicological studies.

Computational analysis of the proton-bound acetonitrile dimer, (ACN)2 H. RAPID COMMUNICATIONS IN MASS SPECTROMETRY : RCM 2020;34:e8767. [PMID: 32115782 DOI: 10.1002/rcm.8767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

RATIONALE

In atmospheric pressure ionization mass spectrometry the theoretical thermodynamic treatment of proton-bound cluster stabilities helps us to understand the prevailing chemical processes. However, such calculations are rather challenging because low-barrier internal rotations and strong anharmonicity of the hydrogen bonds cause the breakdown of the usually applied harmonic approximation. Even the implemented anharmonic treatment in standard ab initio software failed in the case of (ACN)₂ H⁺ .

METHODS

For a case study of the proton-bound acetonitrile dimer, (ACN)₂ H⁺ , we scan the potential energy surface (PES) for the internal rotation and the proton movement in all three spatial directions. We correct the partition functions by treating the internal rotation as a free rotor and by solving the nuclear Schrödinger equation explicitly for the proton movement. An additional PES scan for the dissociation surface further improves the understanding of the cluster behavior.

RESULTS

The internal rotation is essentially barrier free (V₀  = 2.6 × 10^-6 eV) and the proton's movement between the two nitrogen atoms follows a quartic rather than quadratic potential. As a figure of merit we calculate the free dissociation enthalpy of the dimer. Our description significantly improves the standard results from about 118.3 kJ/mol to 99.6 kJ/mol, compared with the experimentally determined value of 92.2 kJ/mol. The dissociation surface reveals strong crosstalk between modes and is essentially responsible for the observed errors.

CONCLUSIONS

The presented corrections to the partition functions significantly improve their accuracy and are rather easy to implement. In addition, this work stresses the importance of alternative theoretical methods for proton-bound cluster systems besides the standard harmonic approximations.

Understanding Nontraditional Differential Mobility Behavior: A Case Study of the Tricarbastannatrane Cation, N(CH2CH2CH2)3Sn. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2020;31:796-802. [PMID: 32129991 DOI: 10.1021/jasms.9b00042]

The effect of strong ion-solvent interactions on the differential mobility behavior of the tricarbastannatrane cation, N(CH₂CH₂CH₂)₃Sn⁺, has been investigated. Exotic "type D" dispersion behavior, which is intermediate to the more common types C and A behavior, is observed for gaseous N₂ environments that are seeded with acetone and acetonitrile vapor. Quantum chemical calculations and first-principles modeling show that under low-field conditions [N(CH₂CH₂CH₂)₃Sn + solvent]⁺ complexes containing a single solvent molecule survive the entire separation waveform duty cycle and interact weakly with the chemically modified environment. However, at high separation voltages, the ion-solvent bond dissociates and dynamic clustering ensues.

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.

Measuring Electronic Spectra of Differential Mobility-Selected Ions in the Gas Phase

We describe the modification of a commercially available tandem differential mobility mass spectrometer (DMS) that has been retrofitted to facilitate photodissociation (PD) of differential mobility-separated, mass-selected molecular ions. We first show that a mixture of protonated quinoline/isoquinoline (QH⁺/iQH⁺) can be separated using differential mobility spectrometry. Efficient separation is facilitated by addition of methanol to the DMS environment and increased residence time within the DMS. In action spectroscopy experiments, we gate each isomer using appropriate DMS settings, trap the ions in the third quadrupole of a triple quadrupole mass spectrometer, and irradiate them with tunable light from an optical parametric oscillator (OPO). The resulting mass spectra are recorded as the OPO wavelength is scanned, giving PD action spectra. We compare our PD spectra with previously recorded spectra for the same species and show that our instrument reproduces previous works faithfully.