The influence of drift gas composition on the separation mechanism in traveling wave ion mobility spectrometry: insight from electrodynamic simulations

Abstract

The influence of three different drift gases (helium, nitrogen, and argon) on the separation mechanism in traveling wave ion mobility spectrometry is explored through ion trajectory simulations which include considerations for ion diffusion based on kinetic theory and the electrodynamic traveling wave potential. The model developed for this work is an accurate depiction of a second-generation commercial traveling wave instrument. Three ion systems (cocaine, MDMA, and amphetamine) whose reduced mobility values have previously been measured in different drift gases are represented in the simulation model. The simulation results presented here provide a fundamental understanding of the separation mechanism in traveling wave, which is characterized by three regions of ion motion: (1) ions surfing on a single wave, (2) ions exhibiting intermittent roll-over onto subsequent waves, and (3) ions experiencing a steady state roll-over which repeats every few wave cycles. These regions of ion motion are accessed through changes in the gas pressure, wave amplitude, and wave velocity. Resolving power values extracted from simulated arrival times suggest that momentum transfer in helium gas is generally insufficient to access regions (2) and (3) where ion mobility separations occur. Ion mobility separations by traveling wave are predicted to be effectual for both nitrogen and argon, with slightly lower resolving power values observed for argon as a result of band-broadening due to collisional scattering. For the simulation conditions studied here, the resolving power in traveling wave plateaus between regions (2) and (3), with further increases in wave velocity contributing only minor improvements in separations.

Characterization of Traveling Wave Ion Mobility Separations in Structures for Lossless Ion Manipulations

“…Therefore, a TW speed of 78 m/s would give a value of c = 1. To study the effect of c on the performance of TW device, the resolving power was calculated using eq 2: 43,44 …”

“…Thus, the net ion motion in the direction of the wave is reduced and a significant portion of the decreased resolving power can be attributed to ion diffusion over the longer drift period. 44 …”

Determination of ion mobility collision cross sections for unresolved isomeric mixtures using tandem mass spectrometry and chemometric deconvolution

“…The transfer cell is operated at lower pressure than the IM cell and ions therefore travel with the TW rather than falling over the wave as they might do in IM. 14,21,66,67 Therefore, assuming an ion entering the transfer cell had insufficient kinetic energy to overtake the TW in the transfer cell, and ignoring phase effects of the TW, the maximum amount of time an ion could spend in the transfer cell ( t transfer ) would be equal to the length of the cell ( d t ) divided by the transfer cell wave velocity ( v t ): 11 $$t_{\mathit{transfer}}=\frac{d_t}{v_t}$$For a cell length of 100 mm (0.1 m) and transfer cell wave velocity of 248 m/s (as used in our experiments), the maximum time an ion should spend in the transfer cell (under our experimental conditions) is ~0.40 ms. Based on these simple estimations, under our experimental conditions, we should expect shift factors between ~0.10 × 10 −2 ms (minimum TOF variation for fragment and precursor ions) 68 and ~0.40 ms (maximum transfer cell time).…”

“…10,12–15 Continuous improvements in achievable resolving power of contemporary instrumentation have extended the ability to differentiate isomeric systems. To improve IM resolving power, several groups have focused on different strategies including using alternative drift gases or gas modifiers, 16–21 developing new instruments, 22–27 or customizing existing systems. 28 Additional improvements in software and post-data acquisition approaches can also enhance data interpretation for convoluted IM peaks.…”

Advanced Multidimensional Separations in Mass Spectrometry: Navigating the Big Data Deluge

“…For example, MS calculations for peak capacity are based on methods developed by Frahm et al , which account for instrument resolving powers and isotope redundancy (78) and utilize typical, rather than optimal parameters for each method (79). For IM, peak capacities are obtained from measured values of different techniques where available (80,81,82), or otherwise calculated from reported resolving powers (83,84). For IM coupled to MS (IM-MS), there is a correlation between size and mass, and so IM-MS data in Figure 2 is scaled by a factor of 0.25 (25% unique space occupancy) to reflect this reduced orthogonality, which is based on experiments conducted in the authors’ laboratory.…”