“…The alpha function (Eq. 1) from this plot is relatively shallow for a 13 mm long analyzer region yet consistent with the 5% duty cycle of the asymmetric waveform, against 30% for similar DMS analyzers 3,31 .Figure 3Dispersion plots for the reactant ion H + (H 2 O) n at moisture levels of ( A ) 1.0 × 10 2 , ( B ) 6.00 × 10 3 , and ( C ) 1.71 × 10 4 ppm in purified air; average values at these levels for n are 3.95, 5.83, and 6.50, respectively.…”
The performance of a differential mobility spectrometer was characterized at ambient pressure and ten values of water vapor concentration, from 1.0 × 10
2
to 1.7 × 10
4
ppm using a homologous series of seven ketones from acetone to 2-dodecanone. Dispersion plots at 30 °C with separation fields from 35 to 123 Td exhibited increased alpha functions for the hydrated proton, protonated monomers, and proton bound dimers with increased moisture levels. Increases in the level of moisture were accompanied by decreased quantitative response with progressive suppression in the formation of the proton bound dimer first and then protonated monomer. Product ions for 2-octanone at 7 ppb were not observed above a moisture level of 4.0 × 10
3
ppm, establishing a limit for observation of analyte ion formation. The observation limit increased from 1.1 × 10
3
ppm for acetone to 5.7 × 10
3
ppm for 2-dodecanone. These findings demonstrate that ketones can be determined with a differential mobility spectrometry (DMS) analyzer near room temperature in the presence of elevated levels of moisture expected with the use of membrane inlets or headspace sampling of surface or ground waters. Moisture levels entering this DMS analyzer employed as an environmental monitor should be kept at 1.0 × 10
3
ppm or below and quantitative studies for individual ketones should be made at a fixed moisture level.
“…The alpha function (Eq. 1) from this plot is relatively shallow for a 13 mm long analyzer region yet consistent with the 5% duty cycle of the asymmetric waveform, against 30% for similar DMS analyzers 3,31 .Figure 3Dispersion plots for the reactant ion H + (H 2 O) n at moisture levels of ( A ) 1.0 × 10 2 , ( B ) 6.00 × 10 3 , and ( C ) 1.71 × 10 4 ppm in purified air; average values at these levels for n are 3.95, 5.83, and 6.50, respectively.…”
The performance of a differential mobility spectrometer was characterized at ambient pressure and ten values of water vapor concentration, from 1.0 × 10
2
to 1.7 × 10
4
ppm using a homologous series of seven ketones from acetone to 2-dodecanone. Dispersion plots at 30 °C with separation fields from 35 to 123 Td exhibited increased alpha functions for the hydrated proton, protonated monomers, and proton bound dimers with increased moisture levels. Increases in the level of moisture were accompanied by decreased quantitative response with progressive suppression in the formation of the proton bound dimer first and then protonated monomer. Product ions for 2-octanone at 7 ppb were not observed above a moisture level of 4.0 × 10
3
ppm, establishing a limit for observation of analyte ion formation. The observation limit increased from 1.1 × 10
3
ppm for acetone to 5.7 × 10
3
ppm for 2-dodecanone. These findings demonstrate that ketones can be determined with a differential mobility spectrometry (DMS) analyzer near room temperature in the presence of elevated levels of moisture expected with the use of membrane inlets or headspace sampling of surface or ground waters. Moisture levels entering this DMS analyzer employed as an environmental monitor should be kept at 1.0 × 10
3
ppm or below and quantitative studies for individual ketones should be made at a fixed moisture level.
“…This relation enables the use of IMS for molecular identification and structural determination. However, similar development of a first-principles description of differential mobility spectrometry has been challenging despite some advances. − Consequently, practitioners often remain unable to complement experimental DMS data with theoretical predictions.…”
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.
“…Differential mobility spectrometry (DMS), also known as field asymmetric ion mobility spectrometry (FAIMS), has over the past two decades become a powerful tool in analytical chemistry. − Consequently, understanding the fundamental processes underpinning this technique has drawn the attention of researchers. − Two main difficulties arise for DMS when compared to the modeling of standard ion mobility spectrometry (IMS). First, the electrical field strengths applied in DMS well exceed what is known as the low-field limit ( ca .…”
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.
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