Preferential Ion Microsolvation in Mixed-Modifier Environments Observed Using Differential Mobility Spectrometry


The preferential solvation behavior for eight different derivatives of protonated quinoline was measured in a tandem differential mobility spectrometer mass spectrometer (DMS-MS). Ion-solvent cluster formation was induced in the DMS by the addition of chemical modifiers (i.e., solvent vapors) to the N2 buffer gas. To determine the effect of more than one modifier in the DMS environment, we performed DMS experiments with varying mixtures of water, acetonitrile, and isopropyl alcohol solvent vapors. The results show that doping the buffer gas with a binary mixture of modifiers leads to the ions binding preferentially to one modifier over another. We used density functional theory to calculate the ion-solvent binding energies, and in all cases, calculations show that the quinolinium ions bind most strongly with acetonitrile, then isopropyl alcohol, and most weakly with water. Computational results support the hypothesis that the quinolinium ions bind exclusively to whichever solvent they have the strongest interaction with, regardless of the presence of other modifier gases.


Preferential solvation is a phenomenon whereby a solute that is solvated in a binary (or higher order) solvent system interacts preferentially with one solvent over another [1]. More specifically, the solute’s inner solvation shell does not resemble the bulk composition of the solvent medium, but rather is composed predominantly of the more strongly interacting species. From a chemical perspective, the nature of solvation is of obvious importance, since most chemical reactions take place in some solvated phase. The specific choice of solvent, mixed or otherwise, can drastically alter key reaction parameters for a given chemical reaction—for a review on the subject, see ref. [2]. Because it is difficult to directly interrogate the solvent shell in condensed phase experiments, properties of the solvated molecule are often targeted as a proxy to observe preferential solvation. For example, it has been shown that the infrared (IR) absorption characteristics [3], solvatochromic shifts [1, 4,5,6], acid-base equilibria [7, 8], enzyme denaturation [9], fluorescence quantum yields [10, 11], and proton chemical shifts [12, 13] vary non-linearly with respect to solvent composition in binary mixtures, illustrating that the inner solvent shell does not reflect the composition of the bulk medium in such cases.

This effect has also been explored in gas-phase experiments. Examples include the work of Kebarle and coworkers, where thermodynamic quantities for the solvation of various cations have been interrogated using mass spectrometry techniques [14,15,16], or the work of Duncombe and coworkers on solvation of Mn2+ cations [17]. Other gas-phase experiments have deployed mass spectrometry as a probe to determine the local structure of preferentially solvated systems—for illustrative examples, see the works of Nishi and Wakisaka [18,19,20,21,22].

In this article, we show that protonated quinoline derivatives exhibit preferential solvation behavior in the gas phase using differential mobility spectrometry (DMS) in tandem with a triple quadrupole mass spectrometer (MS). DMS has gained popularity over the last three decades since its inception in the 1980s [23] and has been used to disentangle a broad range of chemical problems; for detailed examples, see refs. [24,25,26,27] and the articles cited therein. In DMS, an oscillating asymmetric potential, or separation voltage (SV), is applied across two planar electrodes as ions travel toward an orifice leading to a detector. The SV is composed of a high-field and a low-field component. Spatial separation of ions within the DMS occurs due to differences in ion mobility as a function of field strength. Under the SV waveform, the trajectories of the ions deviate from the field-free transmission axis. These deviations can be corrected via application of an appropriate static compensation voltage (CV). Since isobaric ions of different collision cross-section (CCS) to charge ratio, Ω/z, adopt different trajectories within the DMS cell, these species may be selectively transmitted by choosing the appropriate CV and SV condition.

The spatial separation, or resolving power, of any DMS instrument is affected by the composition of the carrier gas. Dry N2 is used commonly as it is cheap and has favorable electrical breakdown properties. Mixtures of gases are sometimes chosen for enhanced electrical breakdown properties, allowing increased field strengths in the SV waveform, or to induce so-called “non-Blanc” effects [28, 29]. Blanc’s law [30] states that the mobility of ions in mixed carrier gases is a linear function of the gas composition:

$$ \frac{1}{K_{mix}}=\sum \limits_i{x}_i/{K}_i, $$

where xi = is the fractional concentration of gas i, and Ki is the mobility of gas i. Deviations from Blanc’s law have been reported in the literature from as early as 1961, and observations of such deviations have been made under both low-field and high-field conditions [28, 29]. Since then, non-Blanc effects have been exploited to separate many classes of chemical species in ion mobility experiments [31,32,33,34,35,36,37,38,39,40,41]. More recently, Shvartsburg and coworkers developed a theoretical framework that qualitatively captures non-Blanc effects in mixed buffer gas systems [42]. Whereas the use of mixed carrier gases to induce non-Blanc effects is a useful tool to enhance separation in DMS experiments, it is often expensive (i.e., if He is used) and can limit the maximum magnitude of the SV due to breakdown effects. An alternative way to increase the separation space is to add small amounts of a modifier gas to the DMS cell—a modifier gas is typically a solvent vapor and is chosen for its ability to cluster weakly with the analyte ion. As the ion swarm traverses the DMS cell, the ions undergo successive microsolvation and de-solvation cycles during the low-field and high-field periods of the SV waveform, respectively [43,44,45]. Microsolvation (i.e., solvent clustering) leads to an artificially increased CCS of the molecular ion and is sensitive to the interaction between the ion and clustering molecule [43,44,45]. Thus, if two otherwise indistinguishable ions differ in their solvent binding affinity, they may be separable using appropriate chemical modifiers. Note also that the addition of modifier gases can alter peak width as well as peak position [46].

It has been shown that one can achieve enhanced separation of molecular ions using a single modifier gas, due to the dynamic clustering/de-clustering phenomenon described above. One might expect then, for binary (or higher order) mixtures of modifier gases, that the observed shift in CV would display behavior analogous to mixed buffer gas experiments. Here, we show that this is not the case in modified DMS environments.

Using a tandem DMS-MS approach, we characterize the preferential solvation behavior of a series of protonated quinoline derivatives. For experiments with binary mixtures of chemical modifiers, we find that, instead of displaying Blanc-like behavior, the ions cluster preferentially with the modifier gas for which they have the strongest binding affinity. Our experimental results are interpreted with the aid of density functional theory (DFT) calculations, which support our argument that binding energy is the main determinant for DMS behavior involving binary mixtures of modifier gases.



The experimental apparatus used in this study has been described in detail previously [45, 47, 48] and is shown schematically in Figure 1. Quinoline derivatives were dissolved in acetonitrile (ACN) or isopropyl alcohol (IPA; ~ 5 nmol L−1), then electrosprayed (TurboV, SCIEX, Canada) into the entrance of a differential mobility spectrometer (SelexION, SCIEX, Canada) using N2 carrier gas. The SV was applied across the planar electrodes of the DMS, leading to spatial separation of ions based on their differential mobilities under the high- and low-field components of the SV waveform. A CV was applied to stabilize the ions’ trajectories and direct them toward the exit orifice of the DMS. The protonated molecules were passed through an orifice following separation in the DMS, into a triple quadrupole mass spectrometer (QTRAP 5500, SCIEX, Canada). The parent and any resulting fragment ions in the range of m/z 50–1250 were detected by a channeltron ion detector. For the DMS experiments discussed within this manuscript, the instrument was operated as follows. The SV was stepped upwards from 0 to 3000 V (in 500 V increments), after which it was stepped in 250 V increments up to 4000 V. At each step, the CV was scanned in 0.1-V increments, and the total ion transmission for the parent m/z at each CV step was measured to produce an ionogram. Each ionogram was fitted to a Gaussian distribution to determine the CV at which maximum ion transmission was obtained. These maxima were then plotted versus the SV, yielding dispersion plots. Under normal conditions, the carrier gas used was pure N2. For experiments involving modifier gases, solvent vapor was added to the resolving (aka throttle) gas that is admitted to the DMS chamber near the orifice leading to the mass spectrometer (see Figure 1). The modifier gases used in this experiment were H2O, ACN, and IPA. The concentration and composition of the modifier gas were systematically altered to determine the effects of mixed-modifier gases on the DMS behavior of the ions; the concentration of modifier gases in the DMS chamber did not exceed 1.5% (v/v).

Figure 1

Schematic illustration of the experimental apparatus used in this study (not to scale)


We deployed the Gaussian 16 computational suite to calculate optimized geometries and vibrational frequencies of both the bare and singly solvated quinolinium derivatives [49]. In cases were multiple protonation sites were possible, prototropic isomers were first investigated to identify the lowest energy structure, which was then used to generate ion-solvent clusters. Since the charge site was easily identifiable and solvent binding is likely to occur in the vicinity of the charge-carrying proton, ion-solvent clusters could be manually constructed with confidence. The standard Gibbs energies of binding for the clusters were determined using thermochemical data obtained from the frequency calculations. All calculations were performed at the B3LYP/6-311++g(d,p) level of theory [50, 51] with the GD3 empirical dispersion correction [52]. Optimized geometries and coordinates for optimized structures are provided in the SI (see S.3).

Results and Discussion

We measured the dispersion plots for eight different quinolinium ions with various mixtures of modifier gases. An example is shown in Figure 2; the remaining DMS data are presented in Fig. S1 of the supplementary information. Figure 2 shows eight dispersion plots for protonated quinoline-2-pyrazole with various concentrations of H2O, ACN, and IPA as modifier gases. For the data shown in Figure 2A, the sample was dissolved in ACN with 0.1% formic acid. In the first experiment (blue, open circles), the N2 carrier gas was doped with 1.5% (v/v) water. Because water molecules bind weakly to the protonated quinoline-2-pyrazole ions, Type B behavior (weak clustering) is observed; a small negative shift in CV is seen with increasing SV until a shallow minimum is reached, followed by a positive shift at SV ~ 2500 V. In the second experiment, the buffer gas is seeded with 0.15% (v/v) ACN and 1.35% (v/v) H2O (gray, filled circles) to make a 1:9 mixture. The dispersion plot shows Type A (strong clustering) behavior in this case, where negative CV shifts are observed with increasing SV. In the third data set (green, filled squares), where the conditions are the same as the second experiment except for the removal of the water modifier gas, the observed dispersion plot is nearly identical to that of the 1:9 mixture. Finally, the fourth experiment (red, open squares) shows an even more prominent Type A behavior when the modifier gas is changed to 1.5% (v/v) ACN. The same four experiments were repeated for quinoline-2-pyrazole dissolved in IPA, with the ACN modifier gas being replaced for IPA, as shown in Figure 2B. Again, we see that similar dispersion plots for 0.15% (v/v) IPA and the IPA/H2O mixture. The same methodology was applied to seven other quinoline derivatives, giving a total of 64 dispersion plots. These data are plotted in the supporting information that accompanies this manuscript.

Figure 2

Dispersion plots for protonated quinoline-2-pyrazole with different fractions of modifier gas added to the buffer gas. In (a), the sample was sprayed from ACN with 0.1% formic acid, whereas in (b), the sample was sprayed from IPA. Estimated error for the Gaussian distribution in CV space is FWHM ≈ 2 V (not shown). The charge-carrying proton is highlighted in red on the inset molecule

In all cases, when a weakly binding buffer gas modifier (H2O) is mixed with a more strongly binding modifier (ACN or IPA), the resulting dispersion plots are nearly identical to those recorded when the weakly binding modifier is removed and the strongly binding modifier is kept at the same concentration. This implies that if the buffer gas is doped with a mixture of modifier gases, the ions will preferentially bind to the modifier with which they have the highest binding affinity. Whereas one might expect to see a mixed behavior in the dispersion plots that reflects the mixture of modifiers in analogy to Blanc’s Law, this is not the case for the protonated quinoline derivatives studied here. It follows that during the electrospray process, the ESI solvent (ACN or IPA) must be completely evaporated before entering the DMS cell. If residual ESI solvent were to be present in the H2O-modified DMS cell, ion trajectories would be predominantly influenced by the preferential formation of ion-solvent clusters with IPA or ACN.

Several other trends are apparent after further examination of the dispersion plots. First, all three modifiers cluster at least weakly with the quinolinium derivatives, as evidenced by the Type B/Type A behavior observed in all cases. Second, the interaction with ACN is the strongest, followed by IPA, then H2O. This is shown by inflection points and CV minima in the individual dispersion plot curves. For example, the SV at the CV minimum (SV@CVmin) is ~ 3750 V for protonated quinoline-2-pyrazole in 0.15% (v/v) IPA (see Figure 2B). This is the condition at which dynamic clustering/de-clustering is at its maximum; at higher SV values, field-induced heating imparts sufficient energy to impede cluster formation under the low-field portion of the waveform. Thus, the higher the SV value at the CV minimum, the stronger the ion-solvent interaction [53, 54]. For the 1.5% (v/v) H2O case shown in Figure 2, SV@CVmin ~ 2500 V, whereas the curve for 0.15% (v/v) ACN (Figure 2A) is approaching a CV minimum by SV ~ 4000 V, indicating that the binding strength of ACN is higher than that of H2O. When the concentration of ACN is increased (and no H2O is present), the slope of the associated dispersion plot becomes steeper, and the inflection point—where the curve changes from positive to negative curvature—moves to higher SV, implying that the CV minimum must also move to higher SV. The same trends are present for experiments with IPA, indicating that both ACN and IPA bind more strongly with the quinolinium derivatives than do H2O. Third, when comparing the magnitude of the CV values for a given SV in the 0.15% (v/v) ACN versus 0.15% (v/v) IPA in Figure 2, the ACN case has a more negative value. This indicates that the dynamic change in ion mobility between the high- and low-field conditions is larger in the case of the ACN modifier. It is worth noting that this is not a consistent trend across all quinoline derivatives. In general, cases in which a second hydrogen-bonding moiety is in close proximity to the site of protonation exhibit larger CV values for IPA than ACN. This is likely related to the formation of larger IPA clusters due to stabilization via an extended hydrogen-bond network about the charge site. In these instances, evaporation of several solvent molecules under the high-field condition would yield a larger dynamic CCS. Finally, significant differences in the curvature of the associated dispersion plots are observed when the IPA or ACN concentration is increased from 0.15 to 1.5% (v/v). In all cases, the dispersion curve inflection points and CV minima are shifted to larger SV values at higher modifier partial pressures. This is consistent with a higher degree of cluster formation and evaporation at higher solvent-vapor pressures.

To further investigate the hypothesis that ACN binds more strongly than IPA, which in turn binds more strongly than H2O to the quinolinium derivatives, DFT was employed to model the ion-solvent cluster systems. In most cases, the global minimum structure was found to involve an ionic hydrogen bond between the excess proton on the ion and the solvent molecule with the exception of 2-hydroxyquinoline and quinaldic acid, for which the OH group acted as a hydrogen bond donor. Similar observations were reported in reference [54]. See the SI for optimized structures. The calculated standard Gibbs binding energies of single solvent molecules to the various analytes are presented in Table 1. Binding energies are exothermic for all quinolinium derivatives with all three solvents, and the expected trend in relative binding energy is confirmed. The ordering for binding energies is also consistent with the observed differences in DMS behavior between each different protonated quinoline derivative. For example, protonated quinoline-2-pyrazole is expected to have a higher binding energy with H2O than is protonated 2-methyl-8-quinolinamine based on experimental obervations, and this is borne out in calculation (see Table 1). As reported in previous studies [53,53,55], a correlation is observed between the physicochemical properties of the molecular ion and the DMS behavior. The correlation between the Gibbs energies of water binding and the SV@CVmin values for the protonated quinoline derivatives is shown in Figure 3. Also plotted in Figure 3 are the analogous data for the quinolinium derivatives studied in references [53] (blue open circles) and [54] (red open circles). That these data exhibit such correlation across multiple studies provides additional evidence to support the view that DMS trajectories are strongly influenced by ion-solvent interaction potentials and that the DMS technique is a robust means of assessing these interactions. We note, however, that there are instances where predicted binding energies do not correlate with DMS data, despite the otherwise excellent agreement between predicted binding energy and dispersion data. For example, Figs. S.2 (o) and (p) show different DMS behavior for 8-chloro-2-methylquinoline with the addition of IPA or ACN, respectively, although the calculated Gibbs binding energy for this complex solvated by a single molecule of either of these modifiers is nearly identical.

Table 1 Calculated standard Gibbs energies of binding (kJ/mol) for quinolinium ion-solvent complexes. Energies are given relative to the bare ion
Figure 3

Correlation between measured SV@CVmin values and standard Gibbs binding energies for the protonated quinoline derivatives complexes with a single H2O molecule. Also included are the data for the protonated quinoline derivatives reported in reference [54] (red open circles) and reference [53] (blue open circles). The data correspond to a modifier gas concentration of 1.5% (v/v)

Conclusions and Future Work

In summary, the ion-solvent interactions between a series of protonated quinoline derivatives with three different chemical modifiers were probed using differential mobility spectrometry. In agreement with previous studies, the presence of a modifier gas led to altered DMS behavior. However, the presence of a second modifier gas did not enhance the separation space of the instrument, nor did it reflect the composition of the buffer gas in analogy with Blanc’s law. Instead, the resulting dispersion plot reflected the behavior of whichever solvent had the strongest interaction with the ion. From this, we conclude that under the present experimental conditions, the solvation characteristics of the ion in the DMS environment is predominantly governed by the strength of the ion-solvent binding interaction, a result which is supported by DFT calculations. These conclusions should serve as a useful guide in aiming to enhance the separation capabilities of DMS experiments; rather than mixing modifier gases, it appears to be more useful to select a modifier on the basis of its predicted Gibbs binding interaction with the analyte ion. Further studies on this phenomenon will include a broader range and concentration of modifier gases, as well as an investigation of the effects of ternary gas mixtures. These studies will yield a more detailed insight into the mechanics of the solvation/de-solvation process occurring within DMS experiments.


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The authors acknowledge high-performance computing support from the SHARCNET consortium of Compute Canada. WSH acknowledges financial support from the Natural Sciences and Engineering Research Council (NSERC) via the Discovery Grant and Collaborative Research and Development Grant schemes. WSH also acknowledges financial support from the Ontario Centres of Excellence in the form of a VIP-II grant, as well as the government of Ontario for an Ontario Early Researcher Award. MJL acknowledges financial support from the NSERC for a Vanier Graduate Scholarship.

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Correspondence to J. Larry Campbell or W. Scott Hopkins.

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Coughlan, N.J.A., Liu, C., Lecours, M.J. et al. Preferential Ion Microsolvation in Mixed-Modifier Environments Observed Using Differential Mobility Spectrometry. J. Am. Soc. Mass Spectrom. 30, 2222–2227 (2019).

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  • Differential ion mobility
  • DMS
  • Ion mobility
  • Modifiers
  • Preferential solvation
  • Gas-phase solvation
  • DFT
  • Ion-solvent clustering