Introduction
Organic species in the atmosphere – their chemical transformation, mass
transport, and phase transitions – are essential for the interaction and
coevolution of life and climate (Pöschl and
Shiraiwa, 2015). Organic species are released into the atmosphere through
biogenic processes and anthropogenic activities. Once in the atmosphere,
they actively evolve via multiphase chemistry and gas/particle-phase conversion. The complexity and dynamic behaviors of organic species
have limited our capability to accurately predict their levels, temporal
and spatial variability, and oxidation dynamics associated with the
formation and evolution of organic aerosols in the atmosphere.
Several two-dimensional frameworks have been developed in an effort to
deconvolve the complexity of organic mixtures and visualize their
atmospheric transformations. The van Krevelen diagram, which cross-plots the
hydrogen-to-carbon atomic ratio (H : C) and the oxygen-to-carbon atomic ratio
(O : C), has been widely used to represent the bulk elemental composition and
the degree of oxygenation of organic aerosol
(Heald et al., 2010). The average carbon
oxidation state (OS‾C), a quantity that necessarily
increases upon oxidation, can be estimated from the elemental ratios
(Kroll et al., 2011). When coupled with carbon number (nC), it
provides constraints on the chemical composition of organic mixtures and
defines key classes of atmospheric processes based on the unique trajectory
of the evolving organic chemical composition on the
OS‾C-nC space. The degree of oxidation has
also been combined with the volatility (expressed as the effective
saturation concentration, C∗), forming a 2-D volatility basis set to
describe the coupled aging and phase partitioning of organic aerosol
(Donahue et al., 2012). These three spaces are designed to
represent fundamental properties of the organic mixtures and provide insight
into their chemical evolution in the atmosphere. Organic species span large
varieties in the physicochemical properties. Species of similar volatility
or elemental composition can differ vastly in structures and
functionalities. One weakness of these frameworks is that they do not
provide information on the organic components at molecular level.
In this article we introduce a new framework that is based on the collision
cross section (Ω), a quantity that is related to the structure and
geometry of a molecule. The collision cross section of a charged molecule
determines its mobility as it travels through a neutral buffer gas such as
N2 under the influence of a weak and uniform electric field. Species
with open conformation undergo more collisions with buffer gas molecules and
hence travel more slowly than the compact ones (Shvartsburg
et al., 2000; Eiceman et al., 2013). Mobility measurements are usually
performed with an ion mobility spectrometer (IMS), where ions are separated
mainly on the basis of their size, geometry, and subsequent interactions
with the buffer gas. The combination of IMS with a mass spectrometer (MS)
allows for further selection of ions based on their mass-to-charge ratios.
The resulting IMS-MS plot provides separation of molecules according to two
different properties: geometry (as reflected by the collision cross section)
and mass (as reflected by the mass-to-charge ratio) (Kanu et
al., 2008). The IMS-MS
analytical technique has been widely employed in the fields of biochemistry
(McLean et al., 2005; Liu et al., 2007; Dwivedi et al., 2008; Roscioli et
al., 2013; Groessl et al., 2015) and homeland security (Eiceman and
Stone, 2004; Ewing et al., 2001; Fernandez-Maestre et al., 2010). To our
knowledge, the application of IMS-MS to study organic species in the
atmosphere, however, has only been explored very recently
(Krechmer et al., 2016).
We propose a two-dimensional collision cross section vs. mass-to-charge
ratio (Ω-m/z) space to facilitate the comprehensive
investigation of complex organic mixtures in the atmosphere. Despite the
typical complexity of the detailed molecular mechanism involved in the
atmospheric oxidation of organics molecules, they can be characterized by
the distinctive functional groups attached to the carbon backbone
(Zhang and Seinfeld, 2013). We show that the investigated
organic classes (m/z < 600), characterized by functional groups
including amine, alcohol, carbonyl, carboxylic acid, ester, and organic
sulfate, exhibit unique distribution patterns on the Ω-m/z
space. Species of the same chemical class, despite variations in the
molecular structures, tend to develop a narrow band and follow a trend line
on the space. Reactions involving changes in functionalization and
fragmentation can be represented by directionalities along or across these
trend lines. The locations and slopes of the measured trend lines are shown
to be predicted by the core model (Mason et al., 1972), which characterizes
the ion-neutral interactions as elastic sphere collisions. Within the narrow
band produced by each chemical class on the Ω-m/z space,
molecular structural assignment is achieved with the assistance of collision-induced dissociation (CID) analysis. Measured collision cross sections are also
shown to be consistent with theoretically predicted values from the
trajectory method (Mesleh et al., 1996; Shvartsburg and Jarrold, 1996) and
are used to identify isomers that are separated from an isomeric mixture.
Collision cross section measurements
Materials
A collection of chemical standards (ACS grade, ≥ 96 %, purchased from
Sigma Aldrich, St. Louis, MO, USA), classified as amines, alcohols,
carbonyls, carboxylic acids, esters, phenols, and organic sulfates, were
used to characterize the performance of IMS-MS. These chemicals were
dissolved in an HPLC-grade solvent consisting of a
70 % methanol/29 % water with 1 % formic acid, at a concentration of approximately
10 µM.
Instrumentation
Ion mobility measurements were performed using an electrospray ionization
(ESI) drift-tube ion mobility spectrometer (DT-IMS) interfaced to a
time-of-flight mass spectrometer (TOFMS). The instrument was designed and
manufactured by TOFWERK (Switzerland), with detailed descriptions and
schematics provided by several recent studies (Kaplan et al., 2010; Zhang
et al., 2014; Groessl et al., 2015; Krechmer et al., 2016). In the next few
paragraphs, we will present the operating conditions of the ESI-IMS-TOFMS
instrument.
Solutions of chemical standards were delivered to the ESI source via a 250 µL gas-tight syringe (Hamilton, Reno, NV, USA) held on a syringe pump
(Harvard Apparatus, Holliston, MA, USA) at a flow rate of 1 µL min-1. A deactivated fused silica capillary (360 µm OD,
50 µm ID, 50 cm length, New Objective, Woburn, MA, USA) was used as the sample
transfer line. The ESI source was equipped with an uncoated SilicaTip
Emitter (360 µm OD, 50 µm ID, 30 µm tip ID, New Objective,
Woburn, MA, USA) and connected to the capillary through a conductive micro
union (IDEX Health & Science, Oak Harbor, WA, USA). The charged droplets
generated at the emitter tip migrate through a desolvation region in
nitrogen atmosphere at room temperature, where ions evaporate from the
droplets and are introduced into the drift tube through a Bradbury–Nielsen
ion gate located at the entrance. The ion gate was operated in the Hadamard
transform mode, with a closure voltage of ±50 V and an average gate
pulse frequency of 1.2 × 103 Hz. The drift tube was held at a
constant temperature (340 ± 3 K) and atmospheric pressure
(∼ 1019 mbar). A counterflow of N2 drift gas was introduced
at the end of the drift region at a flow rate of 1.2 L min-1. Ion
mobility separation was carried out at a typical filed strength of 300–400 V cm-1,
resulting in a reduced electric field of approximately 1.4–1.8 Td. After exiting from the drift tube, ions were focused into TOFMS
through a pressure–vacuum interface that includes two segmented quadrupoles
that were operated at ∼ 2 mbar and ∼ 5 × 10-3 mbar, respectively. CID of parent
ions is achieved by adjusting the voltages on the ion optical elements
between the two quadruple stages (Kaplan et al., 2010).
The ESI-IMS-TOFMS instrument was operated in the m/z range of 40 to 1500 with a
total recording time of 90 s for each dataset. The MS was
calibrated using a mixture of quaternary ammonium salts, reserpine, and a
mixture of fluorinated phosphazenes (Ultramark 1621) in the positive mode
and ammonium phosphate, sodium dodecyl sulfate, sodium taurocholate hydrate,
and Ultramark 1621 in the negative mode. The ion mobility measurements were
calibrated using tetraethyl ammonium chloride as the instrument standard and
2,4-lutidine as the mobility standard, as defined shortly
(Fernández-Maestre et al., 2010). Mass spectra and ion mobility
spectra were recorded using the acquisition package “Acquility” (v2.1.0,
http://www.tofwerk.com/acquility). Post-processing was performed using the
data analysis package “Tofware” (version 2.5.3, www.tofwerk.com/tofware)
running in the Igor Pro (Wavemetrics, OR, USA) environment.
Calculations
The average velocity of an ion in the drift tube (vd) is proportional to
its characteristic mobility constant
(K, cm-2 V-1 s-1) and the
electric field intensity (Ed), provided that the field is weak (McDaniel and Mason, 1973):
vd=KEd.
Experimentally, ion mobility constants can be approximated from the time of
ion clouds spent in the drift tube
(td, s-1), given by the rearranged
form of Eq. (1):
td=1KLd2Vd,
where Ld (cm) is the length of the
drift tube and Vd (V) is the drift voltage. In the present study, drift
time measurements were carried out at six different drift voltages ranging
from 5 to 8 kV in ∼ 1019 mbar of nitrogen gas at 340 K (Fig. S1 in the Supplement). The ion mobility constant (K) is derived by linear
regression of the recorded arrival time (ta) of the ion clouds at the
detector vs. the reciprocal drift voltage:
ta=Ld2K1Vd+t0.
Note that the arrival time was
determined from the centroid of the best-fit Gaussian distribution; see
Fig. S2. The y intercept of the best-fit line represents
the transport time of the ion from the exit of the drift tube to the MS
detector (t0), which exhibits strong m/z dependency that is attributable to
a time-of-flight separation in the ion optics; see Fig. S3.
It is practical to discuss an ion's mobility in terms of the reduced
mobility constant (K0), defined as
K0=K273.15TP1013.25,
where P (mbar) is the pressure in the drift region and T (K) is the buffer gas
temperature. In theory, the parameter K0 is constant for a given ion in
a given buffer gas and can be used to characterize the intrinsic
interactions of that particular ion–molecule pair. In practice, however,
K0 values from different measurements might not be in good agreement,
primarily due to uncertainties in instrumental parameters such as
inhomogeneities in drift temperature and voltage (Fernández-Maestre et
al., 2010). In view of these uncertainties, the instrument standard is
needed to provide an accurate constraint on the instrumental parameters,
such as voltage, drift length, pressure, and temperature.
K0×td=Ld2VdP1013.25273.15T=Ci.
Tetraethyl ammonium chloride (TEA) is used here as the instrument standard,
as its reduced mobility is not affected by contaminants in the buffer gas
(Fernández-Maestre et al., 2010). Given the well-known K0
and measured td of the protonated TEA ion (m/z=130), Eq. (5)
yields an instrument constant Ci to calibrate the IMS performance.
Unlike TEA, the reduced mobility of species that are more likely to cluster
with contaminants can be significantly affected by impurities of the buffer
gas. This category of species can be used as a “mobility standard” to
qualitatively indicate the potential contamination in the buffer gas.
2,4-Lutidine, with a well-characterized K0 value of 1.95 cm2 V-1 s-1, is used as such a mobility standard. As shown
Fig. S4, the measured mobility of 2,4-Lutidine is
1.5 % lower than its theoretical value, indicative of the absence of
contaminations in the buffer gas.
In the low field limit, the collision cross section of an ion (Ω)
with a buffer gas is related to its reduced mobility (K0) through the
modified zero field (so-called Mason–Schamp) equation
(McDaniel and Mason, 1973; Siems et al., 2012):
Ω=3ze16N02πkBμT01/21K01+βMTαMT2vdvT2-1/2,
where z is the net number of integer charges on the ion, e is the elementary
charge, N0 is the number density of buffer gas at 273 K and 1013 mbar,
kB is the Boltzmann constant, μ is the reduced mass for the
molecule-ion pair, T0 is the standard temperature, vd is the drift
velocity given by Eq. (1), vT is the thermal velocity, and αMT
and βMT are correction coefficients for collision
frequency and momentum transfer, respectively, given by
αMT=231+m^fc+M^fhβMT=2m^1+m^1/2,
where m^ and M^ are molecular mass fractions of the ion and
buffer gas molecule, respectively, and fc and fh are the fractions
of collisions in the cooling and heating classes, respectively. Note that
the reduced electric field used in this study is maximized at
∼ 2 Td, at which the drift velocity of any given ion is
∼ 2 orders magnitude lower than its thermal velocity; thus
the values for fc and fh are assigned to be 0.5 and 0.5,
respectively. As all measurements in this study were carried out with
nitrogen as the buffer gas, the reported collision cross sections will be
referred to ΩN2. Matlab codes for
calculating ΩN2 are given in the
Supplement. Experimental ΩN2 values
for a selection of ionic species are consistent with those reported in
literatures (see Table S1 in the Supplement).
Collision cross section modeling
Kinetic theory indicates that the quantity Ω is an orientationally
averaged collision integral (Ωavg(l,l)), which depends on the nature of
ion-neutral interaction potential (McDaniel and Mason, 1973). Given the
potential, the collision integral can be calculated through successive
integrations over collision trajectories, impact parameters, and energy. Here
we adopt two computational methods, i.e., trajectory method and core model,
to simulate the average collision integral. The trajectory method is a
rigorous calculation of Ωavg(l,l)
by propagating classical trajectories of neutral molecules in a realistic
neutral/ion potential consisting of a sum of pairwise Lennard-Jones
interactions and ion-induced dipole interactions
(Mesleh et al., 1996; Shvartsburg and
Jarrold, 1996). The core model treats the polyatomic ion as a rigid sphere
where the center of charge is displaced from the geometry center. The
ion-neutral interaction is approximately represented by the cross section of
two rigid spheres during elastic collisions. The potential during
interaction includes a long-range attraction term and a short-range
repulsion term (Mason et al., 1972).
The two models employed here represent opposite directions in the
Ωavg(l,l) computation methods. The
trajectory method is a rigorous calculation of Ωavg(l,l) in a realistic intermolecular potential
yet the computation is time consuming. The core model, however,
substantially simplifies the calculation of Ωavg(l,l) as rigid sphere collisions at the expense
of simulation accuracy. We will show shortly that the core model is used for
locating individual chemical classes on the 2-D ΩN2-m/z space. Within the band developed by each
chemical class, molecular structure information can be deduced by comparing
the measured collision cross section with those calculated by the trajectory
method.
Trajectory method
Molecular structures for L-leucine and D-isoleucine were initially
constructed by Avogadro v1.1.1 (Hanwell et al., 2012). For
each molecule, both protonation and deprotonation sites are created by
placing a positive charge on the N-terminal amino group and a negative
charge on the C-terminal carboxyl group, respectively. The geometry of each
ion is further optimized using the Hartree–Fock method with the 6-31G(d,p)
basis set via GAMESS (Schmidt et al., 1993). Partial
atomic charges were estimated using Mulliken population analysis.
A freely available software, MOBCAL, developed by Jarrold and coworkers
(http://www.indiana.edu/~nano/software/) was used for
computing the collision integrals. The potential term employed in the
trajectory method takes the form
Φθ,ϕ,γ,b,r=4ϵ∑inσri12-σri6-αp2zen2∑inxiri32+∑inyiri32+∑inziri32,
where θ, φ, and γ are three angles that define the
geometry of ion-neutral collision, b is the impact parameter, ϵ is
the depth of the potential well, σ is the finite distance
at which the interaction potential is zero, αp is the
polarizability of the neutral, which is 1.710 × 10-24 cm3
for N2 (Olney et al., 1997), n is the number of atoms in
the ion, and ri, xi, yi, and zi are coordinates that
define the relative positions of individual atoms with respect to the buffer
gas. Values of the Lennard-Jones parameters, ϵ and σ, are taken from the universal force field (Casewit
et al., 1992). The ion–quadruple interaction and the orientation of N2
molecule are not considered here (Kim et al., 2008; Campuzano et al.,
2012).
Core model
The core model, consisting of a (12-4) central potential displaced from the
origin, is used to represent interactions of polyatomic ions with N2
molecules (Mason et al., 1972). The (12-4) central potential
includes a repulsive r-12 term, which describes the Pauli repulsion at
short ranges due to overlapping electron orbitals, as well as an attractive
r-4 term, which describes attractions at long ranges due to ion-induced
dipole:
Φ(r)=ϵ2rm-ar-a12-3rm-ar-a4,
where r is the distance between the ion-neutral geometric centers, a is the
location of the ionic center of charge measured from the geometrical center
of the ion, and rm is the value of r at the potential minimum. At
temperature of 0 K, the “polarization potential” can be expressed as
Φpol(r)=-e2αp2r4,
where αp is the polarizability of the neutral. Thus
ϵ is given by
ϵ=e2αp3rm-a4.
The collision cross section can be expressed in dimensionless form by
extracting its dependence on rm:
Ω=Ω(l,l)∗πrm2.
Tabulations of the dimensionless collision integral (Ω(l,l)∗) can be found in literatures (Mason
et al., 1972) as a function of dimensionless temperature (T∗) and
core diameter (a∗), given by
T∗=kTϵ=3kTrm-a4e2αpa∗=arm.
Polynomial interpolation of the tabulated Ω(l,l)∗ yielded an analytical expression of the collision
cross section, with rm and a as adjustable parameters. This expression
was then fit to the ion mobility datasets measured in N2 buffer gas
using a nonlinear least-square regression procedure (Matlab code is
available upon request) (Johnson et al., 2004; Kim et al., 2005, 2008). Best-fit parameters, rm and a, along with predicted
vs. measured collision cross section are given in Table S2.
Distribution of organic species including alcohol (R-(OH)n,
n=2-8), amine (NR3), quaternary-ammonium (NR4), carbonyl (R-(C=O)n,
n=1-2), carboxylic acid (R-(COOH)n, n=1-3), ester
(R1-COO-R2), organic sulfate (R-SO4), and multifunctional
compounds ((OH)-R-(COOH)2) on the (a) ΩN2-m/z space and (b) ΔΩN2-m/z space. Note that species
that are detected in different ion modes (±) are plotted
separately.
Measured collision cross sections (ΩN2) for (a) tertiary-amine and
quaternary-ammonium, (b) (di/poly/sugar-)alcohol, and (c) (mono/oxo/hydroxy-)carboxylic acid as a function of the mass-to-charge
ratio. Also shown are the predicted ΩN2-m/z trend lines for amine, alcohol, and
carboxylic acid by the core model. Here, quaternary-ammonium, propylene glycol, and
C8-C18 alkanoic-acid are used to optimize the adjustable parameters in the
core model (the markers are in the same color as the trend lines). The
colored shade in each figure represents the maximum deviations (8.21,
3.54, and 6.69 % for amine, alcohol, and carboxylic acid,
respectively) of the predicted ΩN2
from the measured ΩN2 for species
that are not used to constrain the core model. A single plot showing the
separation of these three chemical classes is given in Fig. S5.
Collision cross section vs. mass-to-charge ratio 2-D space
Distribution of multifunctional organic species
Figure 1a shows the distribution of organic species, classified as
(di/poly/sugar-)alcohol, tertiary-amine, quaternary-ammonium,
(mono/di-)carbonyl, (mono/di/tri-)carboxylic acid, (di-)ester,
organic sulfate, and multifunctional compounds, on the collision cross section
vs. mass-to-charge ratio (ΩN2-m/z)
2-D space. One feature of the distribution pattern is that species with
higher density as pure liquids and carbon oxidation state tend to occupy the
lower region of the ΩN2-m/z space.
This is not surprising given that molecules of smaller collision cross
sections tend to be much denser, and potentially more functionalized, than
those with extended and open geometries. Furthermore, species of the same
chemical class tend to occupy a narrow region and follow a trend line on the
ΩN2-m/z space. These observations
form the basis of potentially utilizing locations and trends on the 2-D
space to identify chemical classes to which an unknown compound belongs.
Small molecules (m/z < 200) with similar size and geometry are situated
closely together, as visualized by the “overlaps” on the space. Improved
visual separation of the species within the overlapping region is obtained
by transforming ΩN2 to a quantity
ΔΩN2, defined as the
percentage difference between the measured collision cross section for any
given molecular ion and the calculated projection area for a rigid spherical
ion–N2 pair with the same molecular mass. Since this idealized
ion–N2 pair does not account for interaction potentials and molecular
conformation, it is only used as a reference state to improve visualization
of the ΩN2-m/z 2-D space, as shown
in Fig. 1b.
ΩN2-m/z trend lines
The ΩN2-m/z trend line visualized
on the 2-D space describes the intrinsic increase in collision cross
sections resulting from the increase in molecular mass by extending the
carbon backbone or adding functional groups. It has been used for
conformation space separation of different classes of biomolecules including
lipids, peptides, carbohydrates, and nucleotides (McLean et
al., 2005). Here we demonstrate for the first time the presence of trend
lines for small molecules of atmospheric interest, and the trend line
pattern for each chemical class can be predicted by the core model
simulations.
Figure 2 shows the measured ΩN2 as
a function of mass-to-charge ratio for (A) tertiary-amine and quaternary-ammonium,
(B) (di/poly/sugar-)alcohol, and (C) (mono/oxo/hydroxy-)carboxylic acid. Also shown is the
ΩN2 predicted by the core model, with
adjustable parameters optimized by the measured ΩN2 for the subcategory spanning the largest m/z range
in each chemical class. Specifically, quaternary-ammonium, propylene glycol, and
alkanoic-acid are used in constraining the core model performance to predict the
ΩN2-m/z trend lines for amines,
alcohols, and carboxylic acids. Species in each chemical class, regardless
of the variety in the carbon skeleton structure, occupy a narrow range and
appear along a ΩN2-m/z trend line.
Such a relationship can be further demonstrated by the goodness of the core
model predictions, i.e., the difference between predicted and measured
ΩN2 for compounds that are not used
to optimize the core model performance. For amine series, predicted
ΩN2 values for lutidine and
pyridine are 8.2 and 0.8 % higher, respectively, than the
measurements. For alcohol series, the best-fit ΩN2-m/z trend line constrained by propylene glycol
can be used to predict the distribution of sugars and polyols within 3.5 %
difference on the space. For carboxylic acid series, hydroxyl-hexadecanoic
acid falls closely on the predicted ΩN2-m/z trend line despite the presence of an
alcohol group on the C16 carbon chain. Predicted ΩN2 values for oxo-carboxylic acids are
4.4–6.1 % lower than the observations. Benzoic acid exhibits a relatively
large measurement–prediction gap (6.7 %) potentially due to the presence
of an aromatic ring.
The demonstrated ΩN2-m/z trend
lines provide a useful tool for categorization of structurally related
compounds. Mapping out the locations and distribution patterns for various
functionalities on the 2-D space would therefore facilitate classification
of chemical classes for unknown compounds. It is likely that trend lines
extracted from a complex organic mixture overlap and, as a result, the
distribution pattern of unknowns on the space alone would not provide
sufficient information on their molecular identities. In this case, the
fragmentation pattern of unknowns upon CID
needs to be explored for the functionality identification, as discussed in
detail in Sect. 4.4. As it is highly unlikely that two distinct molecules
will produce identical IMS, MS, as well as CID-based MS spectra, the 2-D
framework therefore virtually ensures reliable identification of species of
atmospheric interest.
Trajectories for atmospheric transformation processes
Functionalization (the addition of oxygen-containing functional groups) and
fragmentation (the oxidative cleavage of C–C bonds) are key processes
during atmospheric transformation of organics. Reactions involving changes
in functionalization and fragmentation can be represented by
directionalities on the ΩN2-m/z
space, as illustrated by the distribution pattern of carboxylic acids in Fig. 3. Addition of one carbon atom always leads to an increase in mass
and collision cross section, with a generic slope of approximately 5 Å2 Th-1. Although the addition of one oxygen atom in the form of a
carbonyl group results in a similar increase in the molecular mass, it leads
to a shallower slope compared with that from expanding the carbon chain.
Addition of carboxylic or hydroxyl groups does not necessarily lead to an
increase in the collision cross section, as the formation of the
intramolecular hydrogen bonding (O-H…O-) could result in a more compact conformation of the
molecule. In general, fragmentation moves materials to the bottom left and
functionalization to the right on the space.
Trajectories associated with reactions involving functionalization
(changes in the type and number of functional groups) and fragmentation
(changes in the carbon chain length) through the 2-D ΩN2-m/z space using carboxylic acid series as an
illustration.
Molecular structure elucidation of multifunctional
species
The demonstrated ΩN2-m/z
relationship provides a useful tool to identify the chemical class to which
an unknown species belongs. To further identify its molecular structure,
knowledge of the electrospray ionization mechanism for the generation of
quasi-molecular ions, as well as fragmentation patterns of the molecular ion upon
CID, is required.
Overview of organic standards investigated in this study.
Continued.
Continued.
Collision-induced dissociation patterns for molecular ions
generated from cinnamaldehyde, dioctyl phthalate, 2,6-di-tert-butylpyridine,
4-nitrophenol, 16-hydroxyhexadecanoic acid, and sebacic acid on the 2-D
framework with mass-to-charge ratio on the x axis and drift time on the
y axis. The corresponding mobility-selected MS spectra for each species is
given in Fig. S6.
For species investigated in this study, their integral molecular structures
are maintained during electrospray ionization. An exhibition of molecular
formulas of ionic species is given in Table 1. Depending on the proton
susceptibility of functional groups, amines, esters, and aromatic aldehydes
are sensitive to the ESI(+) mode, whereas carboxylic acids and organic
sulfates yield high signal-to-noise ratios in the ESI(-) spectra.
Specifically, the positive mass spectra collected for amines and amino acids
show major ions at m/z values corresponding to the protonated cations
([M+H]+). Sodiated clusters ([M+Na]+) of esters were observed
as the dominant peak in the ESI(+) spectra. Aromatic aldehydes combine
with a methyl group ([M+CH3]+) via the gas-phase aldol reaction
between protonated aldehydes and methanol in the positive mode. Sugars and
polyols can be readily ionized in both positive and negative mode with the
addition of a proton or sodium ion or deprotonation. Extensive formation of
oligomers is observed from the positive mass spectra of propylene glycol,
with the deprotonated propanol (-OCH2CH(CH3)-) as the primary
building block. Monoanions ([M-H]-) were exclusively observed in the
negative mass spectra of (mono/di/tri/multi-)carboxylic acids due to the facile ionization
afforded by the carboxylic group. It is worth noting that quantification of
these species requires prior chromatographic separation to avoid matrix
suppression on the analyte of interest (Zhang et al.,
2016) or alternative ionization scheme that is compatible with the
high-voltage IMS inlet and does not induce matrix effects.
The instrument used in this study enables the CID
of the abovementioned precursor ions after ion mobility separation but prior
to the mass spectrometer (IMS-CID-MS). As a consequence, product ions
exhibit the identical mobility (drift time) with that of the precursor ion.
IMS-CID-MS spectra for individual compounds are then generated by the
extraction of “mobility-selected” MS spectra that contain both precursor
and fragments. The major advantage of this approach is that it is possible
to obtain fragmentation spectra for all precursor ions simultaneously. This
is in contrast to MS/MS techniques which require the isolation of a small
mass window prior to fragmentation which can be a problem for very complex
samples or time-resolved analysis. Figure 4 shows the measured drift time
for the precursor and product ions generated from species representative of
amines, aldehydes, carboxylic acids, esters, and nitro compounds. CID patterns of these species are used to elucidate the
fragmentation mechanisms for corresponding functional groups. The
deprotonated carboxylic acid is known to undergo facile decarboxylation to
produce a carbanion. If additional carboxylic groups are present in the
molecule, combined loss of water and carbon dioxide is expected
(Grossert et al., 2005). Alternatively, the presence of an
-OH group adjacent to the carboxylic group would usually result in a
neutral loss of formic acid (Greene et al., 2013); see the
fragmentation pattern for 16-hydroxyhexadecanoic acid as an illustration.
Scission of the C–O bond in the ester structure or the C–O bond between
the secondary/tertiary carbon and the alcoholic oxygen is observed for the
ester series examined, consistent with previous studies
(Zhang et al., 2015). A primary fragmentation resulting
in loss of CO was evident in the spectrum of methylate derivative of
protonated carbonyls (RCHOCH3+)
(Neta et al., 2014). The IMS-CID-MS spectrum of deprotonated
4-nitrophenol is shown as a representative of organic nitro compounds. Two
dominant peaks at m/z 108 and m/z 92 are observed, resulting from the neutral loss
of NO and NO2, respectively.
Precursor and product ion peak intensities as a function of collision voltage in
the “mobility-selected” MS spectra of (a) deprotonated sebacic acid,
(b) deprotonated 16-hydroxyhexadecanoic acid, (c) protonated dioctyl phthalate,
and (d) deprotonated 4-nitrophenol.
Signal intensities of the fragments from the CID pathway of the precursor
ion depend on the collision voltage, as shown in Fig. 5. At low collision
voltages, the precursor ions predominate with transmission optimized at
approximately 5 V potential gradient. As the collision voltage increases,
the intensity of the precursor ion decreases and that of each product ion
increases, eventually reaching a maximum level, and then decreases due to
subsequent fragmentation. The dependence of the product ion abundance on the
collision voltage provides information on the relative strength of the
covalent bond at which the parent molecule fragments. Consequently, the
energy required to induce a certain fragmentation pathway could potentially
also serve as an additional parameter for structure elucidation. For
example, the predominance of the product ion at m/z 149 suggests that
cleavage of the carbonyl–oxygen bond in the ester moiety is the dominant
fragmentation pathway upon CID of dioctyl phthalate
(C24H38O4).
(a, b) ESI mass spectra collected for an equi-molar mixture
(20 µM each) of L-leucine and D-isoleucine in positive and negative mode.
(c, d) Measured drift time distributions for the leucine mixture in positive
and negative mode. (e, f) Measured vs. predicted ΩN2 for D-isoleucine, together with its drift time
distributions in positive and negative mode. (g, h) Measured vs. predicted
ΩN2 for L-leucine, together with
its drift time distributions in positive and negative mode. Note that all
measurements were performed at ∼ 303 K and ∼ 1019 mbar with an electric field strength of 414 and 403 V cm-1 in the
positive and negative mode, respectively.
Resolving isomeric mixtures
Here we demonstrate the separation of isomers on the ΩN2-m/z space using the mixture of L-leucine and
D-isoleucine as an illustration, as they can be directly ionized by
electrospray in both positive and negative modes due to the presence of
amino and carboxyl groups. We refer the reader to Krechmer et al. (2016) for
the mobility separation of atmospheric relevant isomeric species. Figure 6a and b show a single peak that corresponds to the protonated
([M+H]+, m/z=132) and deprotonated ([M–H]-, m/z=130) forms of
the leucine mixture, respectively, in the positive and negative MS spectra.
Upon further separation based on their distinct mobility in the N2
buffer gas, the leucine mixture is clearly resolved in the positive mode,
while a broad peak is observed in the negative ion mobility spectrum (see Fig. 6c, d). Note that a typical IMS resolving power
(t/dt50) of 100 leads to a baseline separation of leucine isomers that
differ by 0.3 ms in the measured drift time. Figure 6e–h show the IMS
spectra for individual leucine isomeric configurations, which provide
precise constraints for the peak assignment in the leucine mixture. Also
given here are the measured vs. predicted collision cross sections for each
isomer, with predictions lower by 3.3–6.9 % compared with
the measurements. However, despite the underprediction, the model using
trajectory method correctly predicts the relative collision cross sections
of the isomers and therefore also the order in which they appear in the IMS
spectrum. The underprediction of ΩN2 may result from the simplification that linear
N2 molecules are considered as elastic and specular spheres in the
current model configuration (Larriba-Andaluz and Hogan Jr.,
2014). Further development of the model to more appropriately predict
ΩN2 values is needed.
Conclusions
We propose a new metric, collision cross section (Ω), for
characterizing organic species of atmospheric interest. Collision cross
section represents an effective interaction area between a charged molecule
and neutral buffer gas as it travels through under the action of a weak
electric field and thus relates to the chemical structure and 3-D
conformation of this molecule. The collision cross section of individual
molecular ions is calculated from the ion mobility measurements using an ion
mobility spectrometer. In this study, we provide the derived ΩN2 values for a series of organic species
including amines, alcohols, carbonyls, carboxylic acids, esters, organic
sulfates, and multifunctional compounds.
The collision cross section, when coupled with mass-to-charge ratio,
provides a 2-D framework for characterizing the molecular signature of
atmospheric organic components. The ΩN2-m/z space is employed to guide our fundamental
understanding of chemical transformation of organic species in the
atmosphere. We show that different chemical classes tend to develop unique
narrow bands with trend lines on the ΩN2-m/z space. Trajectories associated with
atmospheric transformation mechanisms either cross or follow these trend
lines through the space. The demonstrated ΩN2-m/z trend lines provide a useful tool for
resolving various functionalities in the complex organic mixture. These
intrinsic trend lines can be predicted by the core model, which provides a
guide for locating unknown functionalities on the ΩN2-m/z space.
Within each band that belongs to a particular chemical class on the
space, species can be further separated based on their distinct structures
and geometries. We demonstrate the utility of CID
technique, upon which the resulted product ions share the identical drift
time as the precursor ion, to facilitate the elucidation of molecular
structures of organic species. We employ the ΩN2-m/z framework for separation of isomeric
mixtures as well by comparing the measured collision cross sections with
those predicted using the trajectory method. Further advances in algorithms
to correctly predict collision cross sections ab initio from molecular coordinates
are therefore also expected to significantly improve identification of
unknowns.