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.

Graphormer-IR: Graph Transformers Predict Experimental IR Spectra Using Highly Specialized Attention.

Infrared (IR) spectroscopy is an important analytical tool in various chemical and forensic domains and a great deal of effort has gone into developing in silico methods for predicting experimental spectra. A key challenge in this regard is generating highly accurate spectra quickly to enable real-time feedback between computation and experiment. Here, we employ Graphormer, a graph neural network (GNN) transformer, to predict IR spectra using only simplified molecular-input line-entry system (SMILES) strings. Our data set includes 53,528 high-quality spectra, measured in five different experimental media (i.e., phases), for molecules containing the elements H, C, N, O, F, Si, S, P, Cl, Br, and I. When using only atomic numbers for node encodings, Graphormer-IR achieved a mean test spectral information similarity (SISµ) value of 0.8449 ± 0.0012 (n = 5), which surpasses that the current state-of-the-art model Chemprop-IR (SISµ = 0.8409 ± 0.0014, n = 5) with only 36% of the encoded information. Augmenting node embeddings with additional node-level descriptors in learned embeddings generated through a multilayer perceptron improves scores to SISµ = 0.8523 ± 0.0006, a total improvement of 19.7σ (t = 19). These improved scores show how Graphormer-IR excels in capturing long-range interactions like hydrogen bonding, anharmonic peak positions in experimental spectra, and stretching frequencies of uncommon functional groups. Scaling our architecture to 210 attention heads demonstrates specialist-like behavior for distinct IR frequencies that improves model performance. Our model utilizes novel architectures, including a global node for phase encoding, learned node feature embeddings, and a one-dimensional (1D) smoothing convolutional neural network (CNN). Graphormer-IR's innovations underscore its value over traditional message-passing neural networks (MPNNs) due to its expressive embeddings and ability to capture long-range intramolecular relationships.

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 2nd 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.

Chemical Transformations Can Occur during DMS Separations: Lessons Learned from Beer's Bittering Compounds.

While developing a DMS-based separation method for beer's bittering compounds, we observed that the argentinated forms of humulone tautomers (i.e., [Hum + Ag]+) were partially resolvable in a N2 environment seeded with 1.5 mol % of isopropyl alcohol (IPA). Attempting to improve the separation by introducing resolving gas unexpectedly caused the peaks for the cis-keto and trans-keto tautomers of [Hum + Ag]+ to coalesce. To understand why resolution loss occurred, we first confirmed that each of the tautomeric forms (i.e., dienol, cis-keto, and trans-keto) responsible for the three peaks in the [Hum + Ag]+ ionogram were assigned to the correct species by employing collision-induced dissociation, UV photodissociation spectroscopy, and hydrogen-deuterium exchange (HDX). The observation of HDX indicated that proton transfer was stimulated by dynamic clustering processes between IPA and [Hum + Ag]+ during DMS transit. Because IPA accretion preferentially occurs at Ag+, which can form pseudocovalent bonds with a suitable electron donor, solvent clustering also facilitated the formation of exceptionally stable microsolvated ions. The exceptional stability of these microsolvated configurations disproportionately impacted the compensation voltage (CV) required to elute each tautomer when the temperature within the DMS cell was varied. The disparity in CV response caused the peaks for the cis- and trans-keto species to merge when a temperature gradient was induced by the resolving gas. Moreover, simulations showed that microsolvation with IPA mediates dienol to trans-keto tautomerization during DMS transit, which, to the best of our knowledge, is the first observation of keto/enol tautomerization occurring within an ion-mobility device.

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.

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.

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.

Charging Effects in Inlet Capillaries.

Glass or metal inlet capillaries are commonly used for flow restriction in atmospheric pressure ionization mass spectrometers. They exhibit a high ion transmission rate and stability at most operating conditions. However, transferring unipolar currents of ions through inlet capillaries can lead to sudden signal dropouts or drifts of the signal intensity, particularly when materials of different conductivity are in contact with the capillary duct. Molecular layers of water and other gases such as liquid chromatography solvents always form on the surfaces of inlet capillaries at atmospheric pressure ionization conditions. These surface layers play a major role in ion transmission and the occurrence of charging effects, as ions adsorb on the capillary walls as well, charging the walls to electric potentials of up to kilovolts and eventually leading to a hindrance of ion transport into or through the capillary duct. In this work, surface charging effects are reported in dependence on the capillary material, i.e., borosilicate glass, (reduced) lead silicate, quartz, and metal. Low electrical conductance materials show a more pronounced long-term signal drift (e.g., quartz), while higher electrical conductance materials lead to stable long-term behavior.

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.

Productive Alkyne Metathesis with "Canopy Catalysts" Mandates Pseudorotation.

Molybdenum alkylidyne complexes of the "canopy catalyst" series define new standards in the field of alkyne metathesis. The tripodal ligand framework lowers the symmetry of the metallacyclobutadiene complex formed by [2 + 2] cycloaddition with the substrate and imposes constraints onto the productive [2 + 2] cycloreversion; pseudorotation corrects this handicap and makes catalytic turnover possible. A combined spectroscopic, crystallographic, and computational study provides insights into this unorthodox mechanism and uncovers the role that metallatetrahedrane complexes play in certain cases.

Experimental and theoretical study of the reactivity of a series of epoxides with chlorine atoms at 298 K.

Evaluating the reactivity of epoxides in the gas phase is very important due to their wide distribution in the atmosphere, potential health implications and atmospheric impact. The kinetic rate constants for the oxidation of epoxides have been very little studied until now. From the experimental data obtained in this work has been observed that there is an increase in reactivity towards chlorine atoms as a CH2 group is added to the hydrocarbon chain. The Structure Activity Relationship (SAR) method usually provides a good approximation of the rate constant for a wide series of compounds especially for those without complex structure and multiple organic functions. However, a good determination of the factors included in SAR estimations depends largely on the database of these compounds, which in the case of epoxides is very limited. The SAR estimation method also does not take into account other possible factors that could affect reactivity, such as the geometry of the molecule. The aim of this work is to further evaluate the reactivity of epoxides with chlorine atoms using experimental determinations, theoretical calculations and SAR estimations. For this, rate coefficients have been measured at 298 ± 2 K and 1000 ± 4 mbar pressure of synthetic air in a 1080 l Quartz Reactor (QUAREC) and a 480 l Duran glass reactor for the reaction of chlorine atoms with cyclohexene oxide (CHO), 1,2-epoxyhexane (12EHX), 1,2-epoxybutane (12EB), trans-2,3-epoxybutane (tEB) and cis-2,3-epoxybutane (cEB). Theoretical calculations for the reactions studied are in good agreement with our experimental findings and provide insights about the position of the H atom abstraction and reactivity trends for a series of epoxides. The importance of taking into consideration the geometrical distribution and the ring influence to improve SAR calculations is discussed.

Understanding Nontraditional Differential Mobility Behavior: A Case Study of the Tricarbastannatrane Cation, N(CH2CH2CH2)3Sn.

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.

Computational analysis of the proton-bound acetonitrile dimer, (ACN)2 H.

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.

Charge Retention/Charge Depletion in ESI-MS: Experimental Evidence.

The effects of liquid and gas phase additives (chemical modifiers) on the ion signal distribution for Substance P (SP), recorded with a nanoelectrospray setup, are evaluated. Depletion of the higher charge state of Substance P ([SP+3H]3+) is observed with polar protic gas phase modifiers. This is attributed to their ability to form larger hydrogen-bonded clusters, whose proton affinity increases with cluster size. These clusters are able to deprotonate the higher charge state. "Supercharging agents" (SCAs) as well as aprotic polar gas phase modifiers, which promote the retention of the higher charge state of Substance P, do not form such large clusters under the given conditions and are therefore not able to deprotonate Substance P. Both SCAs and aprotic modifiers form clusters with the higher charge state, leading to stabilization of the charge. Whereas supercharging agents have low vapor pressures and are therefore enriched in late-stage electrospray droplets, the gas phase modifiers are volatile organic solvents. Collision induced dissociation experiments revealed that the addition of a modifier significantly delays the droplet evaporation and ion release process. This indicates that the droplet takes up the gas phase modifier to a certain extent (accommodation). Depending on the modifier's properties either charge depletion or retention may eventually be promoted.

Charge Retention/Charge Depletion in ESI-MS: Theoretical Rationale.

Gas phase modification in ESI-MS can significantly alter the charge state distribution of small peptides and proteins. The preceding paper presented a systematic experimental study on this topic using Substance P and proposed a charge retention/charge depletion mechanism, explaining different gas- and liquid-phase modifications [Thinius et al. J. Am. Soc. Mass Spec. 2020, 10.1021/jasms.9b00044]. In this work, we aim to support this rational by theoretical investigations on the proton transfer processes from (multiply) charged analytes toward solvent clusters. As model systems we use small (di)amines as analytes and methanol (MeOH) and acetonitrile (ACN) as gas phase modifiers. The calculations are supported by a set of experiments using (di)amines, to bridge the gap between the present model system and Substance P used in the preceding study. Upon calculation of the thermochemical stability as well as the proton transfer pathways, we find that both ACN and MeOH form stable adduct clusters at the protonation site. MeOH can form large clusters through a chain of H-bridges, eventually lowering the barriers for proton transfer to an extent that charge transfer from the analyte to the MeOH cluster becomes feasible. ACN, however, cannot form H-bridged structures due to its aprotic nature. Hence, the charge is retained at the original protonation site, i.e., the analyte. The investigation confirms the proposed charge retention/charge depletion model. Thus, adding aprotic solvent vapors to the gas phase of an ESI source more likely yields higher charge states than using protic 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, Teff, 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, Tbath. 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, Teff 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, (NDoF), and collision frequency as represented by the ion's collision cross section. Plots of Teff vs the product of ion mass and NDoF 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 Teff correlate with theoretically predicted Teff using with a self-consistent method based on two-temperature theory. The various instrumental and external parameters that influence Teff are additionally discussed.

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.