Knudsen effusion mass spectrometry (KEMS) was used to
measure the solid state saturation vapour pressure
(PSsat) of a range of atmospherically
relevant nitroaromatic compounds over the temperature range from 298 to 328 K. The selection of species analysed contained a range of geometric isomers
and differing functionalities, allowing for the impacts of these factors on
saturation vapour pressure (Psat) to be probed. Three
subsets of nitroaromatics were investigated: nitrophenols,
nitrobenzaldehydes and nitrobenzoic acids. The
PSsat values were converted to subcooled liquid
saturation vapour pressure (PLsat) values using
experimental enthalpy of fusion and melting point values measured using
differential scanning calorimetry (DSC). The
PLsat values were compared to those estimated by
predictive techniques and, with a few exceptions, were found to be up to 7
orders of magnitude lower. The large differences between the estimated
PLsat and the experimental values can be attributed to the predictive techniques not containing parameters to
adequately account for functional group positioning around an aromatic ring,
or the interactions between said groups. When comparing the experimental
PSsat of the measured compounds, the ability to hydrogen bond (H bond) and the strength of the H bond formed
appear to have the strongest influence on the magnitude of the
Psat, with steric effects and molecular weight also
being major factors. Comparisons were made between the KEMS system and data
from diffusion-controlled evaporation rates of single particles in an
electrodynamic balance (EDB). The KEMS and the EDB showed good agreement
with each other for the compounds investigated.
Introduction
Organic aerosols (OAs) are an important component of the atmosphere with
regards to resolving the impact aerosols have on both climate and air
quality (Kroll and Seinfeld, 2008).
To predict how OA will behave requires knowledge of their physiochemical
properties. OAs consist of primary organic aerosols (POAs) and secondary
organic aerosols (SOAs). POAs are emitted directly into the atmosphere as
solid or liquid particulates and make up about 20 % of OA mass globally
(Ervens et al.,
2011), but the exact percentage of POA varies by a significant amount from
region to region. SOAs are not emitted into the atmosphere directly as
aerosols but instead form through atmospheric processes such as gas-phase
photochemical reactions followed by gas-to-particle partitioning in the
atmosphere (Pöschl, 2005). A key property for predicting the
partitioning of compounds between the gaseous and aerosol phase is the pure
component equilibrium vapour pressure, also known as the saturation vapour
pressure (Psat) (Bilde et al., 2015).
It has been estimated that the number of organic compounds in the atmosphere
is in excess of 100 000 (Hallquist et al., 2009);
therefore it is not feasible to measure the Psat of
each experimentally. Instead, Psat values are often
estimated using group contribution methods (GCMs) that are designed to
capture the functional dependencies on predicting absolute values. GCMs
start with a base molecule with known properties, typically the carbon
skeleton. A functional group is then added to the base molecule. This
addition will change the Psat, and the difference
between the base molecule and the functionalised molecule is the
contribution from that particular functional group. If this concept is true
then the contribution from the functional group should not be affected by
the base molecule to which it is added (Bilde et al., 2015).
Whilst this is true in many cases, there are numerous exceptions. These
exceptions normally occur when proximity effects occur, such as neighbouring
group interactions or other mesomeric effects. In this work there will be a
focus on the Nannoolal et al. method
(Nannoolal et al., 2008), the Myrdal and
Yalkowsky method (Myrdal and Yalkowsky, 1997), and SIMPOL (Pankow
and Asher, 2008). Detailed assessments of such methods have been made by
Barley and McFiggans (2010) and O'Meara et al. (2014), often showing predicted
values differ significantly from experimental data. The limitations and
uncertainties of GCMs come from a range of factors including
underrepresentation of long-chain hydrocarbons (>C18);
underrepresentation of certain functional groups, such as nitro or nitrate
groups; a lack of data for the impact of intramolecular bonding; and the
temperature dependence due to the need for extrapolation over large
temperature ranges to reach ambient conditions (Bilde et al.,
2015). This has important implications for partitioning modelling, in a
mechanistic sense, such as an over- or underestimation of the fraction
partitioning to the particulate state. Different GCMs have different levels
of reliability for different classes of compounds and perform much more
reliably if the compound of interest resembles those used in the
parameterisation data set of the GCM
(Kurtén et al., 2016). For example, in
the assessment by O'Meara et al. (2014), for the compounds to which
it is applicable, EVAPORATION (Estimation of
VApour Pressure of ORganics, Accounting for Temperature, Intramolecular, and Non-additivity effects, Compernolle
et al., 2011) was found to give the minimum mean absolute error, the highest
accuracy for SOA loading estimates and the highest accuracy for SOA
composition. Despite this, EVAPORATION should not be used for aromatic
compounds, as there are no aromatic compounds in the parameterisation data set
(Compernolle et al., 2011). Methods
developed with OA in mind, such as EVAPORATION
(Compernolle et al., 2011), are not without
their limitations due to the lack of experimental data available for highly
functionalised, low-volatility organic compounds
(Bannan et al., 2017). As the degree of
functionality increases, so does the difficulty in predicting the
Psat as more intramolecular forces, steric effects and shielding effects must be considered. The majority of GCMs designed for
estimating Psat of organic compounds were developed
for the chemical industry with a focus on monofunctional compounds with
Psat on the order of 103–105 Pa
(Bilde et al., 2015). SOAs, in contrast, are typically
multifunctional compounds with Psat often many orders
of magnitude below 10-1 Pa (Barley and McFiggans,
2010). GCM development, with a focus on the Psat of
SOA, has to deal with a lack of robust experimental data and, historically,
large differences in measurement data depending on the technique and
instrument used to acquire the data. To address this problem Krieger et al. (2018) identified a reference data set
for validating Psat measurements using the
polyethylene glycol (PEG) series. To improve the performance of GCMs when
applied to highly functionalised compounds, more data are required that
probe both the effect of relative functional group positioning and the
effects of interaction between functional groups on
Psat, such as in the work by Booth et al. (2012) and Dang et al. (2019). In this study the solid state
saturation vapour pressure (PSsat) and
subcooled liquid saturation vapour pressure
(PLsat) of three families of
nitroaromatic compounds are determined using Knudsen effusion mass spectrometry (KEMS), building on the work done
by Dang et al. (2019) and Bannan et al. (2017). These include substituted
nitrophenols, substituted nitrobenzoic acids and nitrobenzaldehydes.
Nitroaromatics are useful tracers for anthropogenic emissions
(Grosjean, 1992), and many nitroaromatic compounds
are noted to be highly toxic (Kovacic and Somanathan, 2014).
Studies quantifying the overall role of nitrogen-containing organics on
aerosol formation would also benefit from more refined
Psat (Duporté et al., 2016;
Smith et al., 2008). Even if mechanistic models perform poorly in predicting
aerosol mass due to missing process phenomena, resolving the partitioning is
still important. Several studies have reported the observation of methyl
nitrophenols (Chow
et al., 2016; Kitanovski et al., 2012; Schummer et al., 2009) and
nitrobenzoic acids (van Pinxteren
and Herrmann, 2007). Nitrobenzaldehydes can form from the photo-oxidation of
toluene in a high-NOx environment (Bouya et al., 2017). Both
nitrophenols and nitrobenzoic acids were identified in the review paper by
Bilde et al. (2015) as compounds of interest and
recommendations for further study. Aldehyde groups tend to have little
impact on Psat by themselves but the =O of the
aldehyde group can act as a hydrogen bond acceptor.
There is a general lack of literature vapour pressure data for nitroaromatic
compounds, and despite recent work on nitrophenols by Bannan et al. (2017), there is still a lack of data on
such compounds in the literature. This is reflected, in part, in the
effectiveness of the GCMs to predict the Psat of such compounds.
Here we present PSsat and
PLsat data for 20 nitroaromatic
compounds. The PSsat data were collected
using KEMS with a subcooled correction
performed with thermodynamic data from a differential scanning calorimeter
(DSC). The trends in the PSsat data are considered, and chemical explanations are given to explain the observed
differences.
As identified by Bilde et al. (2015), experimental
Psat can differ by several orders of magnitude among
techniques. One way of mitigating this is to collect data for a compound
using multiple techniques, whilst running reference compounds to assess
consistency among the employed methods. We therefore use supporting data
from the electrodynamic balance (EDB) at ETH Zurich for three of the
nitroaromatic compounds.
The PLsat data are then compared with the
predicted PLsat of the GCMs, highlighting
where they perform well and where they perform poorly. Finally, these
measurements using the new PEG reference standards are compared to past KEMS
measurements using an old reference standard due to differences in
experimental Psat between this work and previous KEMS
work.
ExperimentalCompound selection
A total of 10 nitrophenol compounds were selected for this study including 9
monosubstituted, 4 nitrobenzaldehydes including 1 monosubstituted and 6
nitrobenzoic acids including 5 monosubstituted. The nitrophenols are shown
in Table 1, the nitrobenzaldehydes are shown in Table 2 and the nitrobenzoic acids are shown in Table 3. All compounds selected for this
study were purchased at a purity of 99 % and were used without further
preparation. All compounds are solid at room temperature.
CompoundStructureCASSupplier5-Chloro-2-nitrobenzoic acid2516-95-2Sigma-Aldrich3-Nitrobenzoic acid121-92-6Sigma-Aldrich4-Methyl-3-nitrobenzoic acid96-98-0Sigma-Aldrich2-Chloro-3-nitrobenzoic acid3970-35-2Sigma-Aldrich2-Hydroxy-5-nitrobenzoic acid96-97-9Sigma-Aldrich3-Methyl-4-nitrobenzoic acid3113-71-1Sigma-AldrichKnudsen effusion mass spectrometry system (KEMS)
The KEMS system is the same system that has been used in previous studies
(Bannan
et al., 2017; Booth et al., 2009, 2010), and a summary of the measurement
procedure will be given here. For a more detailed overview see Booth et al. (2009). To calibrate the KEMS, a
reference compound of known Psat is used. In this
study the polyethylene glycol series (PEG series), PEG-3 (P298=6.68×10-2 Pa) and PEG-4 (P298=1.69×10-2 Pa) (Krieger et al., 2018), were used.
The KEMS has been shown to accurately measure the
Psat of PEG-4 in the study by Krieger et al. (2018), but the KEMS did not measure the
Psat of PEG-3. In this study when using PEG-4 as a
reference compound for PEG-3 the measured Psat of
PEG-3 had an error of 30 % compared to the experimental values from
Krieger et al. (2018), which is well within the
quoted 40 % error margin of the KEMS
(Booth et al., 2009). When using PEG-3
as the reference compound for PEG-4, the measured Psat
of PEG-4 had an error of 20 %.
The reference compound is placed in a temperature-controlled Knudsen cell.
The cell has a chamfered orifice through which the sample effuses, creating a
molecular beam. The size of the orifice is ≤1/10 the mean free path of
the gas molecules in the cell. This ensures that the particles effusing
through the orifice do not disturb the thermodynamic equilibrium of the
cell. The molecular beam is then ionised using a standard 70 eV electron
impact ionisation and analysed using a quadrupole mass spectrometer.
After correcting for the ionisation cross section
(Booth et al., 2009), the signal
generated is proportional to the Psat. Once the
calibration process is completed it is possible to measure a sample of
unknown Psat. When the sample is changed it is
necessary to isolate the sample chamber from the measurement chamber using a
gate valve so that the sample chamber can be vented, whilst the ioniser
filament and the secondary electron multiplier (SEM) detector can remain on
and allow for direct comparisons with the reference compound. The
Psat of the sample can be determined from the
intensity of the mass spectrum, if the ionisation cross section at 70 eV
and the temperature at which the mass spectrum was taken are known. The
samples of unknown Psat are typically solid so it is
the PSsat that is determined. After the
PSsat (Pa) has been determined for
multiple temperatures, the Clausius–Clapeyron equation (Eq. 1) can be used
to determine the enthalpy and entropy of sublimation as shown in Booth et
al. (2009).
lnPsat=ΔHsubRT+ΔSsubR,
where T is the temperature (K), R is the ideal gas constant (J mol-1 K-1), ΔHsub is the enthalpy of sublimation (J mol-1) and ΔSsub is the entropy of sublimation (J mol-1 K-1). Psat was obtained over a range
of 30 K in this work, starting at 298 K and rising to 328 K. The reported
solid state vapour pressures are calculated from a linear fit of ln
(Psat) vs. 1/T using the Clausius–Clapeyron equation.
Differential scanning calorimetry (DSC)
According to the reference state used in atmospheric models, and as
predicted by GCMs, PLsat is required.
Therefore it is necessary to convert the
PSsat determined by the KEMS system into
a PLsat. As with previous KEMS studies
(Bannan
et al., 2017; Booth et al., 2010, 2017) the melting point (Tm) and the
enthalpy of fusion (ΔHfus) are required for the conversion.
These values were measured with a TA Instruments DSC 2500 differential
scanning calorimeter (DSC). Within the DSC, heat flow and temperature were
calibrated using an indium reference and heat capacity using a sapphire reference. A heating rate of 10 K min-1 was used. A sample of 5–10 mg was
measured using a microbalance and then pressed into a hermetically sealed
aluminium DSC pan. A purge gas of N2 was used with a flow rate of 30 mL min-1. Data processing was performed using the Trios software
supplied with the instrument. Δcp,sl was estimated using
Δcp,sl=ΔSfus
(Grant et
al., 1984; Mauger et al., 1972).
Electrodynamic balance (EDB)
The recently published paper by Dang et al. (2019) measured the
Psat of several of the same compounds that are
studied in this paper using the same KEMS system; however, in this study the
newly defined best-practice reference sample was used
(Krieger et al., 2018), whereas Dang et
al. (2019) used malonic acid. The difference
in reference compound led to a discrepancy in the experimental
Psat. Supporting measurements for the compounds were
performed using the EDB from ETH Zurich in order to rule out instrumental
problems with the KEMS. The EDB from ETH Zurich has been used to investigate
Psat of low-volatility compounds in the past
(Huisman
et al., 2013; Zardini et al., 2006; Zardini and Krieger, 2009), and a brief
overview will be given here. For full details see Zardini et al. (2006) and Zardini and Krieger (2009). The EDB can be applied to both liquid particles
and non-spherical solid particles (Bilde et al., 2015). The
EDB uses a double ring configuration (Davis et al., 1990) to
levitate a charged particle in a cell with a gas flow free from the
evaporating species under investigation. There is precise control of both
temperature and relative humidity within the cell. Diffusion-controlled
evaporation rates of the levitated particle are measured at a fixed
temperature and relative humidity by precision sizing using optical
resonance spectroscopy in backscattering geometry with a broadband LED
source and Mie theory for the analysis
(Krieger et al., 2018).
Psat is calculated at multiple temperatures, and the
Clausius–Clapeyron equation can be used to calculate
Psat at a given temperature (Eq. 1).
As single particles injected from a dilute solution may either stay in a
supersaturated liquid state or crystallise, it is important to identify its
physical state.
For 4-methyl-3-nitrophenol a 3 % solution dissolved in isopropanol was
injected into the EDB. After the injection and fast evaporation of the
isopropanol, all particles were non-spherical but with only small
deviations from a sphere, meaning that it was unclear whether the phase was
amorphous or crystalline. To determine the phase of this first experiment, a
second experiment was performed, where a solid particle was injected
directly into the EDB. Mass loss with time was measured by following the DC
voltage necessary to compensate for the gravitational force acting on the
particle to keep the particle levitating. When comparing the
Psat from both of these experiments it is clear
that the initial measurement of 4-methyl-3-nitrophenol was in the
crystalline phase.
3-Methyl-4-nitrophenol was only injected as a solution but the particle
crystallised and was clearly in the solid state.
4-Methyl-2-nitrophenol was injected as both a 3 % and 10 % solution.
Despite being able to trap a particle, the particle would completely
evaporate within about 30 s. This evaporation timescale is too small
to allow the EDB to collect any quantitative data. Using the equation for
large particles neglecting evaporative cooling (Hinds, 1999) (Eq. 2), it is possible to estimate PLsat:
t=Rρ⋅dp28DMPsatT,
where t is the time that the particle was trapped within the cell of the
EDB, R is the ideal gas constant, ρ is the density of the particle,
dp is the diameter of the particle, D is the diffusion coefficient, M
is the molecular mass, T is the temperature, and Psat is the saturation
vapour pressure. Equation (2) gives approximately 4.3×10-3 Pa for
PLsat at 290 K.
TheorySubcooled correction
The conversion between PSsat and
PLsat is done using the Prausnitz
equation (Prausnitz et al., 1998) (Eq. 3):
lnPLsatPSsat=ΔHfusRTmTmT-1-Δcp,slRTmT-1+Δcp,slRlnTmT,
where PLsat/PSsat
is the ratio between PLsat and
PSsat, ΔHfus is the enthalpy
of fusion (J mol-1), Δcp,sl is the change in heat capacity
between the solid and liquid states (J mol-1 K-1), T is the
temperature (K), and Tm is the melting point (K).
Vapour pressure predictive techniques
The most common Psat prediction techniques are GCMs.
Several different GCMs have been developed
(Moller
et al., 2008; Myrdal and Yalkowsky, 1997; Nannoolal et al., 2008; Pankow and
Asher, 2008) with some being more general and others, such as the
EVAPORATION method (Compernolle et al.,
2011), having been developed with OA as the target compounds. The Myrdal and
Yalkowsky method (Myrdal and Yalkowsky, 1997), the
Nannoolal et al. method (Nannoolal et al., 2008), and the Moller et al. method (Moller et al., 2008) are combined methods requiring a boiling point, Tb, as an input. If the Tb of a
compound is known experimentally it is an advantage, but most
atmospherically relevant compounds have an unknown Tb so the Tb
that is used as an input is calculated using a GCM. The combined methods use
a Tb calculated using a GCM for many of the same reasons that GCMs are
used to calculate Psat, i.e. the difficulty in
acquiring experimental data for highly reactive compounds or compounds with
short lifetimes. The Nannoolal et al. method
(Nannoolal et al., 2004), Stein and Brown
method (Stein and Brown, 1994), and Joback and Reid
method (Joback et al., 1987) are most commonly
used. The Joback and Reid method is not considered in this paper due to its
known biases (Barley and McFiggans, 2010), with the
Stein and Brown method being an improved version of Joback and Reid. The
Tb used in the combined methods is, however, another source of
potential error, and for methods that extrapolate Psat
from Tb, the size of this error increases with increasing difference
between Tb and the temperature to which it is being extrapolated
(O'Meara et al., 2014). EVAPORATION
(Compernolle et al., 2011) and SIMPOL
(Pankow
and Asher, 2008) do not require a boiling point, only requiring a structure
and a temperature of interest. The main limitation for many GCMs, aside from
the data required to create and refine them, is not accounting for
intramolecular interactions, such as hydrogen bonding, or steric effects.
The Nannoolal et al. method (Nannoolal et al., 2008), Moller et al. method
(Moller et al., 2008) and EVAPORATION (Compernolle et al., 2011) attempt to
address this by having secondary interaction terms. In the Nannoolal et al.
method (Nannoolal et al., 2008), there are
terms to account for ortho, meta and para isomerism of aromatic compounds;
however, there are no terms for dealing with tri- or greater substituted
aromatics, and in these instances all isomers give the same prediction. A
common misuse of GCMs occurs when a GCM is applied to a compound containing
functionality not included in the training set, e.g. using EVAPORATION
(Compernolle et al., 2011) with aromatic compounds or using SIMPOL
(Pankow and Asher, 2008) with compounds containing halogens. As the GCM does not have the tools to deal with this functionality it will either misattribute a contribution, in the EVAPORATION
(Compernolle et al., 2011) example the
aromatic structure would be treated as a cyclical aliphatic structure, or
simply ignore the functionality, as is the case when SIMPOL
(Pankow
and Asher, 2008) is used for halogen-containing compounds. When selecting a
GCM to model Psat it is essential to investigate
whether the method is applicable to the compounds of interest. Of the
popular Psat GCMs, the Myrdal and Yalkowsky method
(Myrdal and Yalkowsky, 1997) contains only three nitroaromatic compounds, the Nannoolal et al. method (Nannoolal et al., 2008) contains 13,
the Moller et al. (2008) method contains no more than 14, SIMPOL (Pankow and Asher, 2008) contains 25 and EVAPORATION
(Compernolle et al., 2011) contains zero.
The specific nitroaromatics used by the Nannoolal et al. method and the
Moller et al. method are not stated (to the author's knowledge) as the data
were taken directly from the Dortmund Data Bank. Despite the SIMPOL (Pankow and Asher, 2008)
method containing 25 nitroaromatic compounds, 11 of these are
taken from a gas chromatography method using a single data point from a
single data set (Schwarzenbach et
al., 1988).
Inductive and resonance effects
All functional groups around an aromatic ring either withdraw or donate
electron density. This is a result of two major effects, the inductive
effect and the resonance effect, or a combination of the two
(Ouellette et al., 2015a). The inductive effect is the
unequal sharing of the bonding electron through a chain of atoms within a
molecule. A methyl group donates electron density, relative to a hydrogen
atom, so is therefore considered an electron-donating group, whereas a
chloro group withdraws electron density and is therefore considered an
electron-withdrawing group. The resonance effect occurs when a compound can
have multiple resonance forms. In a nitro group, as the oxygen atoms are
more electronegative than the nitrogen atom, a pair of electrons from the
nitrogen–oxygen double bond can be moved onto the oxygen atom followed by a
pair of electrons being moved out of the ring to form a carbon–nitrogen
double bond and leaving the ring with a positive charge. This leads to the
nitro group acting as an electron-withdrawing group. In an amino group, on
the other hand, the hydrogens are not more electronegative than the
nitrogen; instead the lone pair on the nitrogen can be donated into the
ring, causing the ring to have a negative charge and the amino group to act
as an electron-donating group. Examples of the inductive effect and the
resonance effect are given in Fig. 1 (Ouellette et al.,
2015a).
The inductive effect and the resonance effect.
Some functional groups, such as an aromatic OH group, can both donate and
withdraw electron density at the same time. In phenol the OH group withdraws
electron density via the inductive effect, but it also donates electron
density via the resonance effect. This is shown in Fig. 2. As the resonance
effect is typically much stronger than the inductive effect, OH has a net
donation of electron density in phenol (see Fig. 2).
Phenol can withdraw electron density via the inductive effect (a) and donate electron density via the resonance effect (b).
The positioning of the functional groups around the aromatic ring determines
to what extent the inductive and resonance effects occur. The changes in
electron density due to the inductive effect and the resonance effect also
change the partial charges on the atoms within the aromatic ring. These
changes impact the strength of any potential H bonds that may form.
Results and discussionSolid state vapour pressure
PSsat values measured directly by the KEMS are
given in Tables 4, 5 and 6 for the nitrophenols, nitrobenzaldehydes and
nitrobenzoic acids respectively. Measurements were made at increments of 5 K
from 298 to 328 K, with the exception of the following compounds that melted
during the temperature ramp. 2-Nitrophenol was measured between 298 and
318 K, 3-methyl-4-nitrophenol was measured between 298 and 313 K,
4-methyl-2-nitrophenol was measured between 298 and 303 K,
5-fluoro-2-nitrophenol was measured between 298 and 308 K, and
2-nitrobenzaldehyde was measured between 298 and 313 K. The
Clausius–Clapeyron equation (Eq. 1) was used to calculate the enthalpies and
entropies of sublimation. The melting points of compounds studied are given
in Table 7. Generally speaking, considering the different groups of compounds as a
whole, the nitrobenzaldehydes studied exhibit higher
PSsat (order of magnitude) than the
nitrophenols and nitrobenzoic acids studied. This is most likely due to the
fact that none of the nitrobenzaldehydes studied herein are capable of
undergoing hydrogen bonding (H bonding), whilst all of the nitrophenols and
nitrobenzoic acids, to varying extents, are capable of hydrogen bonding. The
nitrophenols and nitrobenzoic acids studied exhibit a range of overlapping
PSsat so nothing can be inferred when
considering these two types of compounds together as groups; therefore the
differences within each of the groups must be considered.
PSsat
at 298 K, enthalpies and entropies of sublimation, and partial charge of the
phenolic carbon of nitrophenols determined using KEMS.
CompoundP298ΔHsubΔSsubPartial charge of the(Pa)(kJ mol-1)(J mol-1 K-1)phenolic carbon2-Nitrophenol8.94×10-479.32206.780.3623-Methyl-2-nitrophenol9.90×10-394.79279.500.3784-Methyl-2-nitrophenol3.11×10-395.26271.450.3435-Fluoro-2-nitrophenol4.25×10-395.84276.140.3964-Amino-2-nitrophenol3.36×10-3111.24325.810.2644-Methyl-3-nitrophenol1.08×10-296.14284.980.2494-Chloro-3-nitrophenol2.26×10-3104.49299.830.2663-Methyl-4-nitrophenol1.78×10-390.85251.970.3622-Fluoro-4-nitrophenol2.75×10-2103.76317.900.2753-Fluoro-4-nitrophenol4.55×10-3108.61319.550.379
Considering first the nitrophenols, Table 4, the highest
PSsat compound is 2-fluoro-4-nitrophenol
(2.75×10-2 Pa). There are two potential H-bonding explanations for why this
compound has such a high PSsat relative
to the other nitrophenols and fluoro nitrophenols. First, in this isomer the
presence of the F atom on the C adjacent to the OH group gives rise to
intramolecular H bonding (Fig. 3a), which reduces the extent of
intermolecular interaction possible and increases
PSsat. This effect can clearly be seen
from the fact that in 3-fluoro-4-nitrophenol, where the F atom is positioned
further away from the OH group, the PSsat
is significantly lower (4.55×10-3) due to the fact that intermolecular
H bonding can occur (Fig. 3b). However, in the work by Shugrue et al. (2016) it is stated that neutral organic fluoro and
nitro groups form very weak hydrogen bonds, which whilst they do exist, can
be difficult to even detect by many conventional methods.
Intramolecular hydrogen bonding in 2-fluoro-4-nitrophenol (a) in comparison to intermolecular hydrogen bonding in
3-fluoro-4-nitrophenol (b).
The second explanation depends on the inductive effect mentioned previously.
By using MOPAC2016 (Stewart, 2016), a semi-empirical quantum
chemistry program based on the neglect of diatomic differential overlap
(NDDO) approximation (Dewar and Thiel, 1977), the partial
charges of the phenolic carbon can be calculated. The partial charge of the
phenolic carbon can be dependent on the orientation of the OH if the
molecule does not have a plane of symmetry, so in this work the partial
charge used is an average of the two extreme orientations of the OH, as
shown in Fig. 4. A plot of PSsat vs. the
partial charge of the phenolic carbon for the nitrophenols can be found in
Fig. 5.
The orientation of the OH group can impact the partial charge of
the phenolic carbon.
PSsat vs. partial charge of the phenolic carbon of the nitrophenols.
The partial charge of the phenolic carbon in 2-fluoro-4-nitrophenol is 0.275
with a PSsat of 2.75×10-2 Pa, whereas for
3-fluoro-4-nitrophenol it is 0.379 with a
PSsat of 4.55×10-3 Pa. The more positive
the partial charge of the phenolic carbon the better it is able to stabilise
the increased negative charge which will develop on the O atom as a result
of H-bond formation. As a result stronger intermolecular H bonds are formed,
therefore giving rise to a lower PSsat.
Moving the nitro group from being para to the OH in 3-fluoro-4-nitrophenol
to meta to the OH in 5-fluoro-2-nitrophenol further reduces the
PSsat to 4.25×10-3 Pa. This reduction in
PSsat can also be explained via the
combination of the inductive effect and the resonance effect as the partial
charge of the phenolic carbon rises from 0.379 to 0.396, again implying
stronger intermolecular H bonds and, therefore, a lower
PSsat. For the fluoro nitrophenols, as
shown in Fig. 5, as the partial charge of the phenolic carbon increases the
PSsat increases.
A similar trend occurs in the methyl nitrophenols as in the fluoro
nitrophenols with a larger partial charge of the phenolic carbon
corresponding to a lower PSsat, as shown
in Fig. 5. 3-Methyl-2-nitrophenol is an exception to this and is discussed
shortly. 3-Methyl-4-nitrophenol has the most positive partial charge with
0.362 and the lowest PSsat of 1.78×10-3 Pa, 4-methyl-2-nitrophenol has the next most positive partial charge of
0.343 and the next lowest PSsat of
3.11×10-3, and 4-methyl-3-nitrophenol has the least positive partial charge
of 0.249 and the highest PSsat of
1.08×10-2. 3-Methyl-2-nitrophenol does not follow this trend, however, with
it having a partial charge of 0.378 and a
PSsat of 9.90×10-3. As shown in Fig. 5,
3-methyl-2-nitrophenol would be expected to have a much lower
PSsat than is observed due to the high
partial charge on the phenolic carbon. A possible explanation as to why
3-methyl-2-nitrophenol does not follow this same trend is the positioning of
its functional groups. As shown in Fig. 6a, all of the functional
groups are clustered together and the proximity of the functional groups
sterically hinders the formation of H bonds, thus increasing the
PSsat. Conversely as shown in Fig. 6b the fact that the methyl group is further away in
4-methyl-2-nitrophenol leads to less steric hindrance of H-bond formation.
Diagram emphasising how the proximity of the bulky methyl
group sterically hinders intermolecular interactions with the nitro group in
3-methyl-2-nitrophenol (a) but not in 4-methyl-2-nitrophenol (b).
Whilst 3-methyl-2-nitrophenol has a higher
PSsat than is expected given the
partial charge on the phenolic carbon, 4-amino-2-nitrophenol has a much
lower PSsat (Fig. 5). This is likely due
to 4-amino-2-nitrophenol being capable of forming more than one hydrogen
bond, whereas all the other compounds investigated were only capable of
forming one H bond. However, despite 4-amino-2-nitrophenol being capable of
forming more than 1 H bond, replacing the methyl group on
4-methyl-2-nitrophenol with an amino group to form 4-amino-2-nitrophenol
surprisingly increases the PSsat from
3.11×10-3 to 3.36×10-3 Pa. The higher
PSsat can be explained via the
combination of the inductive effect and the resonance effect. Whilst the
partial charge of the phenolic carbon in 4-methyl-2-nitrophenol is 0.343,
the partial charge of the phenolic carbon in 4-amino-2-nitrophenol is only
0.264, and the partial charge of the carbon bonded to the amine group is only
0.211. So whilst 4-amino-2-nitrophenol is capable of forming two
intermolecular H bonds compared to 4-methyl-2-nitrophenol's one, they will
be much weaker. 4-Amino-2-nitrophenol is a good example of a compound with
multiple competing factors affecting
PSsat leading to higher
PSsat than would be expected due to one
factor and lower PSsat than expected from
another.
Similar to 4-amino-2-nitrophenol, 4-chloro-3-nitrophenol also has a lower
PSsat than expected according to the
partial charge of the phenolic carbon. This can be seen in Fig. 5. Unlike
4-amino-2-nitrophenol the explanation for 4-chloro-3-nitrophenol is simpler.
Replacing the methyl group on 4-methyl-3-nitrophenol with a chloro group to
form 4-chloro-3-nitrophenol reduces the
PSsat from 1.08×10-2 to 2.26×10-3 Pa.
This reduction in PSsat can be explained
by the increase in partial charge of the phenolic carbon from 0.249 to
0.266, as well as a 13 % increase in molecular weight.
Replacing the F atom in 3-fluoro-4-nitrophenol with a methyl group to form
3-methyl-4-nitrophenol further reduces the
PSsat (1.78×10-3), although exactly why is unclear. The methyl group cannot engage in intermolecular H bonding; it will sterically hinder any H bonding that the NO2 group undergoes; and it reduces the partial charge of the phenolic carbon of the molecule (from
0.379 to 0.362) (Stewart, 2016), which would reduce the strength
of H-bonding interactions between the molecules. It is possible that the
crystallographic packing density of 3-methyl-4-nitrophenol is higher
although no data are available to support this, although when looking at
PLsat data (Sect. 4.2)
3-methyl-4-nitrophenol exhibits a higher
PLsat than 3-fluoro-4-nitrophenol, which
is what would be expected given the respective partial charges of the
phenolic carbons.
Removing the methyl group from 4-methyl-2-nitrophenol to give 2-nitrophenol
causes the PSsat to drop from 3.11×10-3
to 8.94×10-4 Pa. This reduction in PSsat
matches an increase in the positive partial charge of the phenolic carbon,
from 0.343 to 0.383, implying an increase in the strength of the
intermolecular H bonds and therefore a reduction in
PSsat.
PSsat
at 298 K, enthalpies and entropies of sublimation, and crystallographic
packing densities of nitrobenzaldehydes determined using KEMS.
PSsat vs. packing density of the nitrobenzaldehydes.
Now considering the nitrobenzaldehydes (Table 5) the highest
PSsat compound is 2-nitrobenzaldehyde
(3.32×10-1). Comparing this to 2-nitrophenol (8.94×10-4) shows how significant
the ability to form H bonds is to the
PSsat of a compound, with replacing a
hydroxyl group (capable of H bonding) with an aldehyde group (incapable of
H bonding) raising the PSsat of the
compound by more than 2 orders of magnitude. The decrease in
PSsat observed by moving the nitro group
from being ortho to the aldehyde group in 2-nitrobenzaldehyde to being meta
in 3-nitrobenzaldehyde (1.21×10-1) and para in 4-nitrobenzaldehyde (3.40×10-2)
can be explained using the different crystallographic packing densities of
the three isomers as shown in Fig. 7. Crystallographic packing density is a
measure of how densely packed the molecules of a given compound are when
they crystallise – the more closely packed molecules are the greater the
overall extent of interaction between them and the lower the
PSsat. The order of the
PSsat observed here for the three isomers
of nitrobenzaldehyde matches that of their crystallographic packing
densities (Coppens and Schmidt,
1964; Engwerda et al., 2018; King and Bryant, 1996), with the lowest
PSsat correlating with the highest
packing density and vice versa.
The addition of a Cl atom to 3-nitrobenzaldehyde is also observed to
decrease the PSsat compounds. This can be
simply rationalised due to the greater than 25 % increase this causes to
the molecular weight. The higher a compound's molecular weight the greater
the overall extent of interaction between its molecules and the lower its
PSsat.
PSsat
at 298 K, enthalpies and entropies of sublimation, and partial charge of the
carboxylic carbon of nitrobenzoic acids determined using KEMS.
Finally, considering the nitrobenzoic acids (Table 6), the highest
PSsat compound is 4-methyl-3-nitrobenzoic
acid (4.67×10-3). Its isomer, 3-methyl-4-nitrobenzoic acid, possesses a slightly lower PSsat (3.97×10-3) as well
as a slightly lower partial charge of the carboxylic carbon (0.644 vs. 0.628)
although the difference in PSsat is not
significant.
Removing the methyl group from 4-methyl-3-nitrobenzoic acid to give
3-nitrobenzoic acid (1.10×10-3) reduces the observed
PSsat most likely due to the reduction in
steric hindrance around the nitro group, which would allow for more effective
H bonding. In addition 3-nitrobenzoic acid possesses a lower
PSsat than the corresponding
3-nitrobenzaldehyde due to its ability to form H bonds. Adding a hydroxyl
group or a Cl atom to 3-nitrobenzoic acid to give 2-hydroxy-5-nitrobenzoic
acid (1.79×10-3) or 2-chloro-3-nitrobenzoic acid (1.97×10-3) respectively
increases the observed PSsat as the
addition of the extra functional group leads to increased intramolecular
H bonding occurring. Additionally, comparing 2-hydroxy-5-nitrobenzoic acid
with 2-fluoro-4-nitrophenol demonstrates how the increased ability of
carboxylic acid to partake in H bonding compared to an F atom leads to a
suppression of PSsat.
5-Chloro-2-nitrobenzoic acid has a higher
PSsat (2.98×10-3 Pa) than
2-chloro-3-nitrobenzoic acid (1.97×10-3 Pa), its structural isomer. The
increase in PSsat can be attributed to
the increased partial charge of the carbon within the carboxylic acid group
(0.627 increasing to 0.640).
PSsat vs. partial charge of the phenolic/carboxylic carbon of the nitrophenols and
nitrobenzoic acids.
When comparing nitrobenzoic acids as a whole with nitrophenols, nitrobenzoic
acids have a much higher PSsat than would
be expected based solely on the partial charges of the carboxylic carbon. As
can be seen in Fig. 8, there is overlap in the range of
PSsat for the nitrobenzoic acids and many
of the nitrophenols; however, there is no overlap in terms of partial charges
of the carboxylic and phenolic carbons, with all of the nitrobenzoic acids
having partial charges of the carboxylic carbon greater than 0.6, whilst the
nitrophenols had much lower partial charges of the phenolic carbon between
0.2 and 0.4. It is widely known that the H bonds of carboxylic acids are
stronger than the H bonds of alcohols (Ouellette et al.,
2015b), so therefore it would be expected that the carboxylic acids would
have a lower PSsat. A likely reason as to
why the PSsat of the nitrobenzoic acids
is higher than would be expected, compared to the nitrophenols, based only
on the partial charge of the carboxylic carbon is the propensity for
carboxylic acids to dimerise (see Fig. 9). Nitrophenols are unable to
dimerise, instead being able to form H bonds with up to two other molecules as
shown in Fig. 9. By dimerising, the nitrobenzoic acids, despite having much
stronger H bonds than the nitrophenols, will not have a proportionally lower
PSsat.
Diagram demonstrating how a carboxylic acid functionality
allows a molecule to dimerise using H bonds in 4-methyl-3-nitrobenzoic acid (a) whilst a hydroxyl group only allows for hydrogen bonding to two other
molecules with no opportunity to dimerise in 4-methyl-3-nitrophenol (b).
In summary the ability to form H bonds appears to be the most significant
factor affecting the PSsat of a compound,
where molecules that are able to form these strong intermolecular
interactions generally always exhibit lower
PSsat than those that cannot.
Additionally different functional groups are able to form different numbers
of H bonds, with those that are able to form more H bonds generally
suppressing PSsat to a greater extent than
those that form less. The relative positioning of those functional groups
responsible for the H bonding is also important as when positioned too close
together intramolecular H bonding can occur, which competes with
intermolecular H bonding and generally raises
PSsat. The positioning of non-H-bonding
functional groups within the molecule can also have an impact upon the
extent of H bonding, with bulky substituents positioned close to H-bonding
groups causing steric hindrance, which reduces the extent of H bonding and
generally raises PSsat. The positioning
of all the functional groups around the aromatic ring affect the partial
charges of the atoms, via a combination of the inductive effect and the
resonance effect. The inductive effect and the partial charges appear to be
most important when comparing isomers and less important when one
functional group has been swapped for another. In addition greater molecular
weight and increased crystallographic packing density also negatively
correlate with PSsat as they both lead to
increased overall intermolecular interactions. However in many cases these
different factors compete with each other, making it difficult to predict
the expected PSsat, and currently it is
not possible to determine which factor will dominate in any given case.
Dipole moments were also investigated but overall showed very little impact
on PSsat.
Subcooled liquid vapour pressure
The PLsat were obtained from the
PSsat using thermochemical data obtained
through use of a DSC and Eq. (3). The results are detailed in Table 7.
PLsat, melting
point, and the enthalpy and entropy of fusion of the nitrophenols.
Comparing the PLsat of the nitrophenols
with the solid state values there are a few changes in the overall ordering,
but they mostly have little effect upon the preceding discussion. A few
previously significant increases/decreases in Psat
become insignificant, and a few that were insignificant are now significant.
One point of note, however, is that 3-methyl-4-nitrophenol (5.86×10-2) now
exhibits a higher Psat than 3-fluoro-4-nitrophenol
(3.32×10-2). This trend is what would be expected based on the reduction in
steric hindrance, increased potential for H bonding and increase in the
partial charge of the phenolic carbon that the F atom provides in comparison
to the methyl group.
For the nitrobenzaldehydes one change in the overall ordering of the
Psats is observed after converting to
PLsat, but this has no effect on the
preceding discussion.
Finally, for the nitrobenzoic acids, whilst some previously insignificant
differences in PSsat have now become
significant, the only change that impacts upon the discussion is that the
Psat of 3-methyl-4-nitrobenzoic acid (3.04×10-1) is
now higher than that of 4-methyl-3-nitrobenzoic acid (5.76×10-2). This change
could be explained as a result of the higher partial charge of the
carboxylic carbon of 4-methyl-3-nitrobenzoic acid (0.646 vs. 0.628)
(Stewart, 2016) playing a more important role in the subcooled
liquid state than in the solid state.
Comparison with estimations from GCMs
In Fig. 10 the experimentally determined
PLsat values of the nitroaromatics are compared
to the predicted values of several GCMs. All predicted values can be found in Table S1 in the Supplement. The average difference between the
experimental PLsat and the predicted
PLsat for each class of compound and
overall is shown in Table 8. These GCMs are SIMPOL (Pankow
and Asher, 2008), the Nannoolal et al. method (Nannoolal et al., 2008), and the Myrdal and Yalkowsky method (Myrdal and Yalkowsky, 1997). The
Nannoolal et al. method (Nannoolal et al., 2008) and the Myrdal and Yalkowsky method (Myrdal and Yalkowsky, 1997) are both combined methods which require a boiling point to
function. As for many compounds where the experimental boiling point is
unknown, boiling point group contribution methods are required. The Nannoolal
et al. method (Nannoolal et al., 2004) and the Stein and Brown method (Stein and Brown, 1994) are used.
Comparison of estimated and measured subcooled saturation vapour
pressures. N_Vp (Nannoolal vapour pressure), MY_Vp (Myrdal and Yalkowsky vapour pressure), SIMPOL (SIMPOL vapour pressure), N_Tb (Nannoolal boiling point), SB_Tb (Stein and Brown boiling point), literature – black
triangle (2-nitrophenol, 3-methyl-2-nitrophenol, 4-methyl-2-nitrophenol,
5-fluoro-2-nitrophenol and 4-nitrophenol from Schwarzenbach et
al., 1988; 3-nitrophenol from Ribeiro da
Silva et al., 1992; 2-nitrobenzaldehyde and 3-nitrobenzaldehyde from
Perry et al., 1984; 2-nitrobenzoic acid, 3-nitrobenzoic acid and 4-nitrobenzoic acid from Ribeiro Da Silva et
al., 1999; 4-methyl-3-nitrobenzoic acid and 3-methyl-4-nitrobenzoic acid from
Monte et al., 2001), and literature data for previous KEMS studies – black diamond (3-nitrophenol and 4-nitrophenol from Bannan
et al., 2017; 4-methyl-2-nitrophenol, 4-methyl-3-nitrophenol and
3-methyl-4-nitrophenol from Dang et al., 2019).
Error bars on the experimental data points are ±1 standard deviation.
Panel (a) contains nitrophenols, panel (b) contains nitrobenzaldehydes
and panel (c) contains nitrobenzoic acids.
Average difference between the experimental PLsat and the predicted PLsat. N_Vp is the Nannoolal et al. vapour pressure method (Nannoolal et al., 2008), MY_Vp is the Myrdal and Yalkowsky vapour pressure method (Myrdal and Yalkowsky, 1997), N_Tb is the Nannoolal et al. boiling point method (Nannoolal et al., 2004), and SB_Tb is the Stein and Brown boiling point method (Stein and Brown, 1994).
Average differenceN_Vp_N_TbN_Vp_SB_TbMY_Vp_N_TbMY_Vp_SB_TbSIMPOL(orders of magnitude)Nitrophenols4.243.494.213.402.92Nitrobenzaldehydes3.182.503.172.460.29Nitrobenzoic acids2.060.912.561.52-0.83All compounds3.382.523.502.651.26
The Myrdal and Yalkowsky method (Myrdal and Yalkowsky,
1997) shows poor agreement with the experimental data for almost all
compounds but is not particularly surprising given that it only contains three nitroaromatic compounds in this method's fitting data set, with none of
these compounds containing both a nitro group and another oxygen-containing
group. The Myrdal and Yalkowsky method (Myrdal and
Yalkowsky, 1997) is the oldest method examined in this study, and much of
the atmospherically relevant Psat data have been
collected after the end of the development of this model. The Myrdal and
Yalkowsky method's (Myrdal and Yalkowsky, 1997) reliance
on a predicted boiling point may also be a major source of error in the
Psat predictions of the nitroaromatics.
On average the SIMPOL method (Pankow
and Asher, 2008) predicts values closest to the experimental data, on
average predicting PLsat 1.3 orders of
magnitude higher than the experimental values, despite absolute differences
of up to 4.4 orders of magnitude.
The Nannoolal et al. method (Nannoolal et al.,
2004) is persistently worse than the Stein and Brown method
(Stein and Brown, 1994) for the nitroaromatic
compounds involved in this study as shown in Table 8. When discussing the
Nannoolal et al. method (Nannoolal et al.,
2008) and the Myrdal and Yalkowsky method (Myrdal and
Yalkowsky, 1997) from this point onwards they are used with the Stein and Brown
method (Stein and Brown, 1994) unless stated
otherwise.
The Nannoolal et al. method (Nannoolal et
al., 2008) has slightly better agreement with the experimental data when
compared to the Myrdal and Yalkowsky method (Myrdal and
Yalkowsky, 1997), on average predicting PLsat 2.52 orders of magnitude higher than
the experimental values, whereas the Myrdal and Yalkowsky method
(Myrdal and Yalkowsky, 1997) on average predicts PLsat 2.65 orders of magnitude higher than
the experimental values. The Nannoolal et al. method
(Nannoolal et al., 2008), unlike the others,
contains parameters for ortho, meta and para isomerism and even demonstrates
the same trend as the experimental data for 2-nitrobenzaldehyde,
3-nitrobenzaldehyde and 4-nitrobenzaldehyde, although 3 orders of magnitude
higher. Despite the ortho, meta and para parameters, as soon as a third
functional group is present around the aromatic ring the Nannoolal et al.
method (Nannoolal et al., 2008) no longer
accounts for relative positioning of the functional groups.
Figure 10a shows the comparison between the experimental and predicted
PLsat for the nitrophenols. Both SIMPOL
(Pankow and Asher, 2008) and the Nannoolal et al. method
(Nannoolal et al., 2008) contain nitrophenol
data from Schwarzenbach et al. (1988). These data of Schwarzenbach et al. (1988), however, are questionable in reliability due to being taken from
a single data point from a single data set. The values given are also 3–4 orders of magnitude greater than those measured in this work as well as
those measured by Bannan et al. (2017) and those measured by Dang et al. (2019). The use of the
Schwarzenbach et al. (1988) nitrophenol Psat data,
which make up 11 of the 12 nitrophenol data points within the fitting data
set of the SIMPOL method (Pankow and Asher, 2008), is a likely cause of the SIMPOL method (Pankow and Asher, 2008) overestimating the
Psat of nitrophenols by 3 to 4 orders of magnitude. The one nitrophenol used in the SIMPOL
method (Pankow
and Asher, 2008) not from Schwarzenbach et al. (1988), 3-nitrophenol from Ribeiro da Silva et al. (1992), has a much lower
Psat than those of Schwarzenbach et
al. (1988) and is only 1 order of magnitude higher than that from Bannan et al. (2017). Additionally,
whilst the Nannoolal et al. (2008) method performs slightly better than the Myrdal and Yalkowsky
method (Myrdal and Yalkowsky, 1997) overall for this
study, when taking the nitrophenol data in isolation this performance is
flipped, with the Myrdal and Yalkowsky method (Myrdal and
Yalkowsky, 1997) showing better performance (overestimating on average by
3.4 to 3.5 orders of magnitude).
Figure 10b shows the comparison between the experimental and predicted
PLsat for the nitrobenzaldehydes. There
are no nitrobenzaldehydes present in any fitting data set of the GCMs
considered in this study. Despite this, whilst not capturing the effects of
ortho, meta and para isomerism, SIMPOL (Pankow
and Asher, 2008) predicts the Psat of the
nitrobenzaldehydes to, on average, 0.29 orders of magnitude. As polar groups
such as aldehydes have been shown to have little impact on volatility in the
pure component, and by extension Psat (Bilde et al., 2015), this implies that SIMPOL (Pankow
and Asher, 2008) captures the contribution of the nitro group very well.
Similar to the nitrophenols the performance of the Nannoolal et al. method
(Nannoolal et al., 2008) and the Myrdal and
Yalkowsky method (Myrdal and Yalkowsky, 1997) has
switched for the nitrobenzaldehydes compared to the entire data set. The
Myrdal and Yalkowsky method (Myrdal and Yalkowsky, 1997)
overestimates by 2.4 orders of magnitude compared to the Nannoolal et al.
method (Nannoolal et al., 2008), which
overestimates by 2.5 orders of magnitude.
Figure 10c shows the comparison between the experimental and predicted
PLsat for the nitrobenzoic acids. SIMPOL
(Pankow
and Asher, 2008) contains, though in limited amounts, nitrobenzoic acid data
in its fitting parameters. Although there are no lists of the data used to
form the Nannoolal et al. method (Nannoolal
et al., 2008) available (to the authors' knowledge), it is stated that the
values come from the Dortmund Data Bank, and from searches on this database
there are nitrobenzoic acid Psat data available.
Having even this limited number of data available for the nitrobenzoic acids allows
for SIMPOL (Pankow
and Asher, 2008) to predict the PLsats of
5-chloro-2-nitrobenzoic acid, 3-nitrobenzoic acid, 2-chloro-3-nitrobenzoic
acid and 2-hydroxy-5-nitrobenzoic acid to within 1 order of magnitude of
the experimental values. On average the SIMPOL (Pankow
and Asher, 2008) method underestimates PLsat by 0.8 orders of magnitude. The nitrobenzoic acids that had large discrepancies with SIMPOL (Pankow and Asher, 2008), 4-methyl-3-nitrobenzoic acid and 3-methyl-4-nitrobenzoic acid, as well as 2-hydroxy-5-nitrobenzoic acid, agreed to within 1 order of magnitude of the Nannoolal et al. method
(Nannoolal et al., 2008). On average the Nannoolal et al. method (Nannoolal et al., 2008) overestimates PLsat by 0.9 orders
of magnitude.
Overall SIMPOL (Pankow
and Asher, 2008) performs relatively well for the nitrobenzaldehydes and the
nitrobenzoic acids, and the Nannoolal et al. method
(Nannoolal et al., 2008) performs moderately
well for the nitrobenzoic acids when compared to the experimental values
found in this study. All of the methods perform poorly when compared to the
experimental nitrophenol values. These observations are not particularly
surprising when taking into account how the methods were fitted and what
data are present in the fitting set.
One surprising observation comes when looking at the halogenated
nitroaromatics. SIMPOL (Pankow
and Asher, 2008) has the smallest order of magnitude difference between
experimental and predicted PLsat for all
of the halogenated nitroaromatics in this study. This is particularly
surprising as SIMPOL (Pankow
and Asher, 2008) contains no halogenated compounds in its fitting data set,
whereas the other GCMs do. This implies that accurately predicting the
impact on PLsat of the carbon skeleton and
other functional groups such as, nitro, hydroxy, aldehyde and carboxylic
acid is more important than the impact of a chloro or fluoro group.
When looking at nitroaromatics as a whole, SIMPOL (Pankow
and Asher, 2008) shows the smallest difference between experimental and
predicted PLsat (as shown in Table 8) and
would therefore be the most appropriate method to use when predicting
PLsat for this group of compounds. In the
case of nitrophenols, despite SIMPOL (Pankow
and Asher, 2008) showing the best performance the absolute differences are
still close to 3 orders of magnitude, so any work using these predictions
should be aware of the very larger errors that these predictions could
introduce. For nitrobenzaldehydes SIMPOL (Pankow
and Asher, 2008) shows very good agreement and is the clear choice to be
used when predicting PLsat. For
nitrobenzoic acids the preferred method for predicting
PLsat is not quite as clear. Both the
Nannoolal et al. method (Nannoolal et al., 2008) and SIMPOL (Pankow
and Asher, 2008) predict PLsat within an
order of magnitude, with Nannoolal et al. (Nannoolal et al., 2008) generally
overestimating and SIMPOL (Pankow and Asher, 2008) underestimating.
Comparison with existing experimental data
For the compounds in this study that had previous literature data there are
differences from the values determined experimentally in this work. The
differences between the values from this work and those of Dang et al. (2019) are discussed in Sect. 4.5 but can be
attributed to the use of a different reference compound.
For the nitrophenols, shown in Fig. 10a, the differences between the
experimental values and the literature values from Schwarzenbach et al. (1988) range from 3
to 4 orders of magnitude. The relationship between the
PLsat and temperature from Schwarzenbach
et al. (1988) was
derived from gas chromatographic (GC) retention data. This GC method
requires a reference compound of known Psat, as well as for
the reference compound and the compound of interest to have very similar
interactions with the stationary phase of the GC. Schwarzenbach et al. (1988) used
2-nitrophenol as the reference compound for all of the other nitrophenol
data they collected. In this work the
PLsat at 298 K was 1.38×10-3 Pa, whereas
Schwarzenbach et al. (1988) reported it
as 2.69×101 Pa. As the difference between the Psat
of 2-nitrophenol in this work and Schwarzenbach et al. (1988) differs by
approximately 4 orders of magnitude, this could explain why the other
nitrophenol measurements also differ by 3–4 orders of magnitude.
For the nitrobenzaldehydes, shown in Fig. 10b, the literature data from
Perry et al. (1984) and
the experimental data from this work agree within 1 order of magnitude,
with 2-nitrobenzaldehyde especially agreeing very closely (2.39×100 Pa vs. 2.15×100 Pa).
The nitrobenzoic acids are shown in Fig. 10c. The value for 3-nitrobenzoic
acid from this work is 1.90×10-3 Pa compared to 5.05×10-3 from Ribeiro da
Silva et al. (1999)
Whilst not matching perfectly, the Psat of
3-nitrobenzoic acid is on this order of magnitude. The disagreements between
the values of this work and the values from Monte et al. (2001) for
4-methyl-3-nitrobenzoic acid and 3-methyl-4-nitrobenzoic acid are quite
large. 4-Methyl-3-nitrobenzoic acid differs by over 1 order of magnitude,
and 3-methyl-4-nitrobenzoic acid is closer to 2 orders of magnitude. The Psat values from Monte
et al. (2001) were collected using a Knudsen mass loss method. Knudsen mass loss is
similar to KEMS in that it also utilises a Knudsen cell which effuses the
compound of interest. However for an amount of mass to be lost such that it
can be detected the experiments need to be performed at higher temperatures
than the KEMS. This means that the data must be extrapolated further to
reach ambient temperatures. This is a potential source of error and could
explain the difference. Measurement by a third or even fourth technique
would be required to confirm this.
Sensitivity of vapour pressure measurement techniques to
reference standards
The recently published paper by Dang et al. (2019) measured the
Psat of several of the same compounds that are
studied in this paper using the same KEMS system; however, in this study the
newly defined best-practice reference sample was used
(Krieger et al., 2018), whereas Dang et
al. (2019) used malonic acid. These compounds
were 4-methyl-3-nitrophenol, 3-methyl-4-nitrophenol and
4-methyl-2-nitrophenol. The difference in reference compound led to a
discrepancy in the experimental Psat (shown in Table 9). Due to these differences additional measurements were made using malonic
acid as the reference material. Additionally, supporting measurements for
the compounds were performed using the EDB from ETH Zurich in order to rule
out instrumental problems with the KEMS.
Comparison between nitrophenols measured in this paper and by Dang
et al. (2019).
CompoundSolid state P298 (Pa)Subcooled P298 (Pa)4-Methyl-3-nitrophenol1.08±0.43×10-26.85±5.14E-02This work – PEG reference1.94±0.78×10-31.23±0.92×10-2This work – malonic acid reference2.46±0.98×10-34.85±3.64×10-3Dang et al. (2019)1.84-0.27+0.30×10-2EDB3-Methyl-4-nitrophenol1.78±0.71×10-35.86±4.40×10-2This work – PEG reference2.45±0.98×10-47.80±5.85×10-3This work – malonic acid reference2.28±0.91×10-43.78±2.84×10-3Dang et al. (2019)7.20-3.10+9.30×10-44.70-2.00+6.00×10-2EDB4-Methyl-2-nitrophenol3.11±1.24×10-33.29±2.47×10-3This work – PEG reference5.61±2.24×10-45.76±4.32×10-4This work – malonic acid reference5.72±2.29×10-45.97±4.48×10-4Dang et al. (2019)
Comparisons between Psat at 298 K from the KEMS
using a PEG reference, the KEMS using a malonic acid reference, Dang et al. (2019) and the EDB are shown in Table 9.
Following this, PLsat values, extrapolated down
to 290 K, from the KEMS using a PEG reference and the KEMS using a malonic acid
reference are compared to the estimated
PLsat based on the findings from the EDB
using Eq. (2).
Whilst the absolute values of the nitrophenols shown in Table 9 changed, the
Psat trends did not. The values from Dang et al. (2019) are between 4.39 and 7.81 times lower
than those in this work using the PEGs as the reference compound, which is
now deemed as best practice in the community. To ensure that the difference
in reference compound was the cause of the difference in
Psat 4-methyl-2-nitrophenol, 4-methyl-3-nitrophenol
and 3-methyl-4-nitrophenol were also measured using malonic acid as a
reference again. The differences between the Psat
determined by Dang et al. (2019) and those in
this work using malonic acid as a reference compound were between 2 % and
27 %, which is well within the quoted 40 % error margin of the KEMS
(Booth et al., 2009), therefore showing
that the instrument is behaving reproducibly but with now improved reference
standards being used, as is discussed below.
Comparison of Psat
between the EDB and the KEMS using both PEGs and malonic acid as the
reference compound (SS – solid state, SCL – subcooled liquid).
Starting with 4-methyl-3-nitrophenol the EDB has much better agreement with
the KEMS when the PEGs are used as the reference compound than when malonic
acid is used as the reference compound. When the quoted errors of both the
EDB (shown in Table 9) and the KEMS (±40 % for
PSsat and ±75 % for
PLsat; Booth et al., 2009) are taken into
account, the lower limit of the EDB (1.57×10-2 Pa) and the upper limit of the
KEMS using the PEG references (1.51×10-2 Pa) almost overlap, whereas the EDB
data are almost 1 order of magnitude larger than the KEMS when the malonic
acid reference is used (shown in Fig. 11).
For 3-methyl-4-nitrophenol a comparison can be made for both
PSsat and
PLsat. Looking first at the
PSsat the EDB appears to be somewhere in
between the KEMS depending on what the KEMS is using as a reference, with
its absolute value being closer to that of the malonic acid reference.
However when the quoted errors are taken into account (shown in Table 9) the
EDB actually has better agreement with the KEMS when the PEG references are
used. This can be seen more clearly in Fig. 11. For
PLsat the EDB and the KEMS when using the
PEG references appears to agree very well with a large overlap when the
quoted errors are taken into account. This can also be seen in Fig. 11.
The confidence with which the comparison between the EDB and the KEMS can be
made for 4-methyl-2-nitrophenol is lower than with the other compounds
looked at due to how quickly 4-methyl-2-nitrophenol evaporated in the EDB.
To make this comparison the PLsat from
the KEMS measurements has been extrapolated down to 290 K to match that of
the EDB estimation. The predicted EDB value (shown in Fig. 11) is higher
than the KEMS for both references but has a very large error margin
(approximately a factor of 5). When this error is considered the KEMS using
the PEG reference is within this range, whereas there is close to an order
of magnitude difference between the lower limit of this estimate and the
upper limit of the KEMS when malonic acid is used as the reference.
In all cases the EDB showed better agreement with the KEMS using the PEGs as
the reference material compared to when malonic acid was used as the
reference material. For 4-methyl-3-nitrophenol the agreement was very close
between the EDB and the KEMS using the PEGs as the reference compounds, and
for 3-methyl-4-nitrophenol the measurements for the EDB and the KEMS agreed
with each other within the quoted errors. For 4-methyl-2-nitrophenol the
KEMS with PEG as a reference also showed the best agreement with the EDB,
but as this was an estimate with a large error range this comparison is the
least certain.
Conclusions
Experimental values for the PSsat and
PLsat have been obtained using KEMS and
DSC for nitrophenols, nitrobenzaldehydes and nitrobenzoic acids.
The predictive models have been shown to overestimate
PLsat in almost every instance by several
orders of magnitude. As the Psat from these
predictive techniques are often used in mechanistic partitioning models
(Lee-Taylor et
al., 2011; Shiraiwa et al., 2013), the overestimation of the
Psat can lead to an overestimation of the fraction in
gaseous state. The experimental values from this study can be used in
conjunction with other measurements to improve the accuracy of GCMs and
give an insight into the impact of functional group positioning which is
missing, or only available in a limited capacity, for the currently
available GCMs.
The differences in trends of the experimental Psat
have been explained chemically, with the potential and strength of H bonding
appearing to be the most significant factor, where present, in determining
the Psat and the stronger hydrogen bond and
increasing number of possible hydrogen bonds decreasing the
Psat. Whilst H bonding is typically the most
important factor, it is not the only factor. Steric effects by functional
groups can also have significant effects on the Psat.
In the solid state crystallographic packing density can also be an important
factor. To further investigate the impacts of H bonding, inductive and
resonance effects, and steric effects on Psat, more
compounds need to be investigated, with select compounds being chosen to
probe these effects.
The predictive models consistently overestimate the
PLsats by up to 6 orders of magnitude
with the nitrophenols performing especially poorly. This demonstrates a need
for more experimental data to be used in the fitting data sets of the GCMs
to reduce the errors and give more accurate results for nitroaromatic
compounds.
Deviations between the measurements in Dang et al. (2019) and this work can be explained by the
difference of the reference material used, which demonstrates the necessity
of a consistent, widely used reference compound. The PEG series, looked at
by Krieger et al. (2018), is
currently the preferred reference/calibration series.
Comparisons between the KEMS and the EDB from ETH were made for several
nitrophenols. The EDB showed close agreement with the KEMS when the PEG
series was used as the reference compounds.
Compounds such as the nitrobenzaldehydes, which are capable of being H-bond
acceptors but not H-bond donors, are likely to deviate negatively from
Raoult's law in mixtures with compounds that can act as H-bond donors, due
to the adhesive forces present. This could call into question the validity
of pure component vapour pressure measurements for looking at atmospheric
systems due to the atmosphere not being made up of the pure component. This
would be an interesting avenue of research and the natural progression from
pure component measurements to investigate their usefulness.
Data availability
All data in this paper are available from 10.5281/zenodo.3613581 (Shelley et al., 2020b). Supplementary material is available from
10.5281/zenodo.3625641 (Shelley et al., 2020a).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-20-8293-2020-supplement.
Author contributions
PDS carried out the experiments on the KEMS and DSC. UKK carried out the experiments on the EDB. Formal analysis of the data
was carried out by PDS, SDW and UKK. Project supervision was undertaken by DT, MRA and TJB. KEMS training was performed by TJB. Access to and training on the DSC was undertaken by AG.
Verification on the reliability of the KEMS was carried out by UKK, with the EDB measurements being used to validate the KEMS
measurements. The original draft manuscript was written by PDS, SDW and CJP. Internal review and editing was performed by TJB, DT, MRA,
SDW and UKK.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The work contained in this paper contains work conducted during a PhD study
supported by the Natural Environment Research Council (NERC) EAO Doctoral
Training Partnership and is fully funded by NERC, whose support is gratefully
acknowledged (grant no. NE/L002469/1).
The work by Carl J. Percival was carried out at the Jet Propulsion Laboratory,
California Institute of Technology, under contract with the National
Aeronautics and Space Administration (NASA), and was supported by the Upper
Atmosphere Research Program and Tropospheric Chemistry Program.
Financial support
This research has been supported by the Natural Environment Research Council (grant no. NE/L002469/1).
Review statement
This paper was edited by Alexander Laskin and reviewed by Thomas Mentel and one anonymous referee.
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