Unsymmetrical Imidazopyrimidine-Based Ligand and Bimetallic Complexes.

With bimetallic catalysts becoming increasingly important, the development of electronically and structurally diverse binucleating ligands is desired. This work describes the synthesis of unsymmetric ligand 2,7-di(pyridin-2-yl)imidazo[1,2-a]pyrimidine (dpip) that is achieved in four steps on a multigram scale in an overall 54% yield. The ability of dpip to act as a scaffold for the formation of bimetallic complexes is demonstrated with the one-step syntheses of the dicopper complex [Cu2(dpip)(µ-OH)(CF3COO)3] (4), the dipalladium complex [Pd2(dpip)(µ-OH)(CF3COO)2](CF3COO)·CF3COOH (5), and the dimeric dinickel complex [Ni4(dpip)2(µ-Cl)4Cl2MeOH6][2Cl] (6) in good yields (79-92%). All bimetallic complexes were characterized by spectroscopic methods and X-ray crystallography, which revealed metal-metal distances between 3.4821(9) and 4.106(2) Å. Additionally, quantum chemical calculations were conducted on complex 4 and an analogous 1,8-naphthyridine-based dicopper complex to investigate the differences between the imidazopyrimidine motif reported here and the widely used 1,8-naphthyridine motif. Natural bonding orbital (NBO) and Mayer bond order (MBO) analyses validated the ability of dpip to coordinate metals more strongly. Finally, NBO calculations quantified the differences in the binding energy between the two pockets of the unsymmetrical dpip ligand.

Argentination: A Silver Bullet for Cannabinoid Separation by Differential Mobility Spectrometry.

As the legality of cannabis continues to evolve globally, there is a growing demand for methods that can accurately quantitate cannabinoids found in commercial products. However, the isobaric nature of many cannabinoids, along with variations in extraction methods and product formulations, makes cannabinoid quantitation by mass spectrometry (MS) challenging. Here, we demonstrate that differential mobility spectrometry (DMS) and tandem-MS can distinguish a set of seven cannabinoids, five of which are isobaric: Δ9-tetrahydrocannabinol (Δ9-THC), Δ8-THC, exo-THC, cannabidiol, cannabichromene, cannabinol, and cannabigerol. Analytes were detected as argentinated species ([M + Ag]+), which, when subjected to collision-induced dissociation, led to the unexpected discovery that argentination promotes distinct fragmentation patterns for each cannabinoid. The unique fragment ions formed were rationalized by discerning fragmentation mechanisms that follow each cannabinoid's MS3 behavior. The differing fragmentation behaviors between species suggest that argentination can distinguish cannabinoids by tandem-MS, although not quantitatively as some cannabinoids produce small amounts of a fragment ion that is isobaric with the major fragment generated by another cannabinoid. By adding DMS to the tandem-MS workflow, it becomes possible to resolve each cannabinoid in a pure N2 environment by deconvoluting the contribution of each cannabinoid to a specific fragmentation channel. To this end, we used DMS in conjunction with a multiple reaction monitoring workflow to assess cannabinoid levels in two cannabis extracts. Our methodology exhibited excellent accuracy, limits of detection (10-20 ppb depending on the cannabinoid), and linearity during quantitation by standard addition (R2 > 0.99).

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.

Protomers of the green and cyan fluorescent protein chromophores investigated using action spectroscopy.

The photophysics of biochromophore ions often depends on the isomeric or protomeric distribution, yet this distribution, and the individual isomer contributions to an action spectrum, can be difficult to quantify. Here, we use two separate photodissociation action spectroscopy instruments to record electronic spectra for protonated forms of the green (pHBDI+) and cyan (Cyan+) fluorescent protein chromophores. One instrument allows for cryogenic (T = 40 ± 10 K) cooling of the ions, while the other offers the ability to perform protomer-selective photodissociation spectroscopy. We show that both chromophores are generated as two protomers when using electrospray ionisation, and that the protomers have partially overlapping absorption profiles associated with the S1 ← S0 transition. The action spectra for both species span the 340-460 nm range, although the spectral onset for the pHBDI+ protomer with the proton residing on the carbonyl oxygen is red-shifted by ≈40 nm relative to the lower-energy imine protomer. Similarly, the imine and carbonyl protomers are the lowest energy forms of Cyan+, with the main band for the carbonyl protomer red-shifted by ≈60 nm relative to the lower-energy imine protomer. The present strategy for investigating protomers can be applied to a wide range of other biochromophore ions.

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.

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.

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.

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.

A parallelized molecular collision cross section package with optimized accuracy and efficiency.

Ion mobility-based separation prior to mass spectrometry has become an invaluable tool in the structural elucidation of gas-phase ions and in the characterization of complex mixtures. Application of ion mobility to structural studies requires an accurate methodology to bridge theoretical modelling of chemical structure with experimental determination of an ion's collision cross section (CCS). Herein, we present a refined methodology for calculating ion CCS using parallel computing architectures that makes use of atom specific parameters, which we have called MobCal-MPI. Tuning of ion-nitrogen van der Waals potentials on a diverse calibration set of 162 molecules returned a RMSE of 2.60% in CCS calculations of molecules containing the elements C, H, O, N, F, P, S, Cl, Br, and I. External validation of the ion-nitrogen potential was performed on an additional 50 compounds not present in the validation set, returning a RMSE of 2.31% for the CCSs of these compounds. Owing to the use of parameters from the MMFF94 forcefield, the calibration of the van der Waals potential can be extended to additional atoms defined in the MMFF94 forcefield (i.e., Li, Na, K, Si, Mg, Ca, Fe, Cu, Zn). We expect that the work presented here will serve as a foundation for facile determination of molecular CCSs, as MobCal-MPI boasts up to 64-fold speedups over traditional calculation packages.

The structures and properties of anionic tryptophan complexes.

The physicochemical properties of [Trpn-H]- and [TrpnCl]- (n = 1, 2) have been investigated in a combined computational and experimental infrared multiple dissociation (IRMPD) study. IRMPD spectra within the 850-1900 cm-1 region indicate that deprotonation is localized on the carboxylic acid moiety in [Trpn-H]- clusters. A combination of hydrogen bonding and higher order charge-quadrupole interactions appear to influence cluster geometries for all investigated systems. Calculated global minimum and low energy geometries of [TrpCl]- and [Trp2Cl]- clusters favour coordination of the halide by the indole NH. [Trp2-H]- and [Trp2Cl]- exhibit additional π-π interactions between the heterocyclic side chains.

A Flexible Synthesis of 68Ga-Labeled Carbonic Anhydrase IX (CAIX)-Targeted Molecules via CBT/1,2-Aminothiol Click Reaction.

We herein describe a flexible synthesis of a small library of 68Ga-labeled CAIX-targeted molecules via an orthogonal 2-cyanobenzothiazole (CBT)/1,2-aminothiol click reaction. Three novel CBT-functionalized chelators (1⁻3) were successfully synthesized and labeled with the positron emitter gallium-68. Cross-ligation between the pre-labeled bifunctional chelators (BFCs) and the 1,2-aminothiol-acetazolamide derivatives (8 and 9) yielded six new 68Ga-labeled CAIX ligands with high radiochemical yields. The click reaction conditions were optimized to improve the reaction rate for applications with short half-life radionuclides. Overall, our methodology allows for a simple and efficient radiosynthetic route to produce a variety of 68Ga-labeled imaging agents for tumor hypoxia.

The structures and properties of proton- and alkali-bound cysteine dimers.

The proton-, lithium-, and sodium-bound cysteine dimers have been investigated in a joint computational and experimental infrared multiple photon dissociation (IRMPD) study. IRMPD spectra in the 1000-2000 cm(-1) region show that protonation is localized on an amine group, and that intermolecular hydrogen bonding occurs between the protonated amine and the carbonyl oxygen of the neutral Cys moiety. Alkali-bound dimers adopt structures reminiscent of those observed for the monomeric Cys·Li(+) and Cys·Na(+) species. Calculations of the heavier Cys2·M(+) (M = K, Rb or Cs) species suggest that these are significantly less strongly bound than the lighter (M = H, Li, or Na) dimers.

Using differential mobility spectrometry to measure ion solvation: an examination of the roles of solvents and ionic structures in separating quinoline-based drugs.

Understanding the mechanisms and energetics of ion solvation is critical in many scientific areas. Here, we present a methodlogy for studying ion solvation using differential mobility spectrometry (DMS) coupled to mass spectrometry. While in the DMS cell, ions experience electric fields established by a high frequency asymmetric waveform in the presence of a desired pressure of water vapor. By observing how a specific ion's behavior changes between the high- and low-field parts of the waveform, we gain knowledge about the aqueous microsolvation of that ion. In this study, we applied DMS to investigate the aqueous microsolvation of protonated quinoline-based drug candidates. Owing to their low binding energies with water, the clustering propensity of 8-substituted quinolinium ions was less than that of the 6- or 7-substituted analogues. We attribute these differences to the steric hinderance presented by subtituents in the 8-position. In addition, these experimental DMS results were complemented by extensive computational studies that determined cluster structures and relative thermodynamic stabilities.

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.

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.

"Thermometer" Ions Can Fragment Through an Unexpected Intramolecular Elimination: These Are Not the Fragments You Are Looking For.

Benzylpyridinium analogs are effective thermometer ions since monitoring the formation of the benzylium fragment produced from heterolytic cleavage of the C-N bond can be linked to the ion's internal energy. In this study, three para-substituted benzylpyridinium ions containing ethoxy (OEt), isopropoxy (OiPr) and tert-butoxy (OtBu) substitutents were synthesized and evaluated as chemical thermometers. Intriguingly, the product ion spectra of the three benzylpyridinium ions were dominated by m/z 107 instead of the anticipated benzylium species. Deuterium labeling suggested that the m/z 107 fragment resulted from an intramolecular elimination (Ei), which formed via a four-membered transition state (TS). The fragmentation pathway appears to be an anomaly within the mass spectrometry literature, as four-membered pericyclic TSs are usually accompanied by the formation of an exceptionally stable neutral molecule (e.g., CO2). Quantum-chemical calculations confirmed our hypothesis that stabilization of the strained TS is afforded by hyperconjugation (ΔG‡ tert-butoxy < isopropyoxy < ethoxy).

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 (Teff) accessible during a DMS experiment (ca. 375-600 K). Calculated Teff 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.

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.