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

Ieritano, Christian; Hopkins, W. Scott

doi:10.1039/d2cp02540j

Cited by 14 publications

(42 citation statements)

References 169 publications

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“…The separation power afforded by DMS can be enhanced by doping the DMS carrier gas with solvent vapor. − The increase in separation power is a consequence of microsolvation, which increases the difference between the ion’s high and low-field mobility and is usually ion-specific. This phenomenon is rooted in the heating/cooling cycles associated with the field-induced temperature imparted by the SV, where the oscillating waveform drives analyte solvation/desolvation cycles as the ion transitions between the low-field to the high-field portion of the duty cycle. − Mashmoushi et al showed that optimal sensitivity and resolution was afforded by detecting the cannabinoids as deprotonated anions in a N 2 DMS environment that is seeded with 1.5 mol % of IPA . However, our attempts to add Δ 8 -THC to this workflow resulted in its coelution with CBD using various chemical modifiers (Figures S2 and S3), indicating that an alternative separation strategy will be required.…”

Section: Introductionmentioning

confidence: 98%

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

2023

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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: Δ 9 -tetrahydrocannabinol (Δ 9 -THC), Δ 8 -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 MS 3 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 N 2 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 (R 2 > 0.99).

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Section: Introductionmentioning

confidence: 98%

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

2023

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“…Because the exact magnitude of the DR-induced temperature gradient is not known, it was necessary to explore whether invariant CV behavior would also be observed using a more controlled method to change the effective temperature ( T eff ) of the ion . The T eff of an ion is determined by the sum of the temperatures of the bath gas ( T bath ) and the electric field ( T field ) as described by two-temperature theory. , Since a relatively high SV is needed to resolve the [Hum + Ag] + tautomers, T eff can only be modulated by changing T bath , which is determined by the DT setting.…”

Section: Results and Discussionmentioning

confidence: 99%

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

Ieritano

Haack

Hopkins

2023

J. Am. Soc. Mass Spectrom.

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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 N 2 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.

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“…Recently, we have reported correlations between gas-phase dynamic ion-solvent clustering behavior and solution-phase p-chem properties. − We hypothesize that the clustering process effectively samples the ion-solvent interaction potential and that the gas-phase interactions correlate strongly with the condensed phase interactions that give rise to p-chem properties such as lipophilicity. To use these data to create general predictive models for p-chem properties of interest, it is necessary to treat dynamic clustering data with supervised machine learning (ML).…”

Section: Introductionmentioning

confidence: 99%

Using Machine Learning To Predict Partition Coefficient (LogP) and Distribution Coefficient (LogD) with Molecular Descriptors and Liquid Chromatography Retention Time

Win

Cheong

Hopkins

2023

J. Chem. Inf. Model.

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During preclinical evaluations of drug candidates, several physicochemical (p-chem) properties are measured and employed as metrics to estimate drug efficacy in vivo. Two such pchem properties are the octanol−water partition coefficient, Log P, and distribution coefficient, Log D, which are useful in estimating the distribution of drugs within the body. Log P and Log D are traditionally measured using the shake-flask method and highperformance liquid chromatography. However, it is challenging to measure these properties for species that are very hydrophobic (or hydrophilic) owing to the very low equilibrium concentrations partitioned into octanol (or aqueous) phases. Moreover, the shakeflask method is relatively time-consuming and can require multistep dilutions as the range of analyte concentrations can differ by several orders of magnitude. Here, we circumvent these limitations by using machine learning (ML) to correlate Log P and Log D with liquid chromatography (LC) retention time (RT). Predictive models based on four ML algorithms, which used molecular descriptors and LC RTs as features, were extensively tested and compared. The inclusion of RT as an additional descriptor improves model performance (MAE = 0.366 and R 2 = 0.89), and Shapley additive explanations analysis indicates that RT has the highest impact on model accuracy.

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

Abstract: This article highlights the fundamentals of ion-solvent clustering processes that are pertinent to an ion’s behaviour during differential mobility spectrometry (DMS) experiments. We contrast static-field ion mobility with differential ion...

Cited by 14 publications

References 169 publications

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

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

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

Using Machine Learning To Predict Partition Coefficient (LogP) and Distribution Coefficient (LogD) with Molecular Descriptors and Liquid Chromatography Retention Time

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