AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-11-5315-2018Cleaning up our water: reducing interferences from nonhomogeneous freezing
of “pure” water in droplet freezing assays of ice-nucleating particlesCleaning up our waterPolenMichaelBrubakerThomasSomersJoshuaSullivanRyan C.rsullivan@cmu.eduhttps://orcid.org/0000-0003-0701-7158Center for Atmospheric Particle Studies, Carnegie Mellon University,
Pittsburgh, Pennsylvania, USARyan C. Sullivan (rsullivan@cmu.edu)24September20181195315533420April201826April201827August201829August2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://amt.copernicus.org/articles/11/5315/2018/amt-11-5315-2018.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/11/5315/2018/amt-11-5315-2018.pdf
Droplet freezing techniques (DFTs) have been used for half a century to
measure the concentration of ice-nucleating particles (INPs) in the atmosphere
and determine their freezing properties to understand the effects of INPs on
mixed-phase clouds. The ice nucleation community has recently adopted droplet
freezing assays as a commonplace experimental approach. These droplet
freezing experiments are often limited by contamination that causes
nonhomogeneous freezing of the “pure” water used to generate the droplets
in the heterogeneous freezing temperature regime that is being measured.
Interference from the early freezing of water is often overlooked and not
fully reported, or measurements are restricted to analyzing the more
ice-active INPs that freeze well above the temperature of the background
water. However, this avoidance is not viable for analyzing the freezing
behavior of less active INPs in the atmosphere that still have potentially
important effects on cold-cloud microphysics. In this work we review a number
of recent droplet freezing techniques that show great promise in reducing these
interferences, and we report our own extensive series of measurements using
similar methodologies. By characterizing the performance of different
substrates on which the droplets are placed and of different pure water
generation techniques, we recommend best practices to reduce these
interferences. We tested different substrates, water sources, droplet
matrixes, and droplet sizes to provide deeper insight into what methodologies
are best suited for DFTs. Approaches for analyzing droplet freezing
temperature spectra and accounting and correcting for the background “pure”
water control spectrum are also presented. Finally, we propose experimental
and data analysis procedures for future homogeneous and heterogeneous ice
nucleation studies to promote a more uniform and reliable methodology that
facilitates the ready intercomparison of ice-nucleating particles measured by
DFTs.
Introduction
Pure water experiences extensive supercooling. Water droplets of cloud-relevant diameters (∼10–20 µm) freeze homogeneously at
temperatures < -38∘C, and this temperature increases
with increasing droplet volume
(Koop and Murray, 2016;
O and Wood, 2016). Freezing between -38 and 0 ∘C requires a
catalyst, which in the atmosphere is provided by rare ice-nucleating
particles (INPs). Most precipitation over land is triggered through the ice
phase (Mülmenstädt et al., 2015), and INPs may have
large impacts on cold-cloud microphysics, optical properties, lifetime, and
structure
(Creamean
et al., 2013; DeMott et al., 2010; Lohmann and Feichter, 2005;
Vergara-Temprado et al., 2018b; Yin et al., 2002).
Droplet freezing techniques (DFTs) have been utilized for decades to assess
the homogeneous freezing of pure water, and the immersion freezing
properties of INPs immersed in the droplets
(Bigg,
1953; Murray et al., 2012; Vali, 1971, 2014; Wex et al., 2015; Wright and
Petters, 2013). In general, these experiments work by depositing droplets
containing particles onto a surface which is then cooled down to a low
temperature by a cold-plate heat sink
(Cziczo et al., 2017). Droplets are then
assigned a freezing temperature based on the temperature they were observed
to freeze at during the cooling process. These data are compiled to produce a
plot of a frozen fraction of droplets versus temperature, referred to as the
droplet freezing temperature spectrum. DFTs are utilized for both
homogeneous and heterogeneous ice nucleation experiments
(Hiranuma
et al., 2015; Murray et al., 2010, 2012; Vali and Stansbury, 1966; Wilson et
al., 2015; Zobrist et al., 2008). Homogeneous freezing can sometimes present
a challenge for DFTs as it is difficult to avoid interference from
unintended heterogeneous freezing
(Hader
et al., 2014; O'Sullivan et al., 2015; Whale et al., 2015). There are a
number of variables within DFT setups that can influence the apparent
homogeneous freezing temperature of pure water droplets that determines the
background temperature spectrum and sets the lower temperature limit for
assessing heterogeneous ice nucleation. Water contamination or substrate
interferences can also induce freezing well above the homogeneous freezing
temperature regime that ensues in the temperature range of -35 to -40∘C (Koop and Murray,
2016), restricting the heterogeneous temperature regime accessible by DFTs.
Particles and cloud droplets experience a wide range of cloud temperatures
and it is important to characterize as much of the heterogeneous ice
nucleation temperature spectrum down to -35∘C as possible. This
requires reducing the influence of water contaminants and substrate effects
in DFTs. Recently droplet freezing measurements in the warmer heterogeneous
temperature regime > -25∘C have been combined with
measurements in the colder regime of -20 < T < -35∘C by a continuous flow diffusion chamber to characterize the
complete heterogeneous ice nucleation temperature spectrum of ambient
particles (DeMott et
al., 2017). We seek to improve and refine DFTs so that they can
independently characterize the complete freezing temperature spectrum.
Nanoscale ice-active surface sites on particles, macromolecules, and other
surfaces are thought to control heterogeneous ice nucleation by helping
supercooled water molecules to arrange into an ice embryo, thus reducing the
nucleation energy barrier
(Gurganus et
al., 2014; Koop and Murray, 2016; Marcolli et al., 2007). In DFTs the
surface on which the droplets reside is thought to be one of the biggest
factors that induces nonhomogeneous freezing behavior, similar to other
nucleation and crystallization processes (Diao et
al., 2011; Hader et al., 2014). Properties such as the contact angle between
the droplets and the surface can be used to attempt to assess the ideality
of the surface (Budke and Koop,
2015; Koop et al., 1998; Murray et al., 2010). However, despite a large
contact angle, surfaces may have micro- or nanoscale defects that induce
ice nucleation. Recent work indicates that cracks, scratches, and other
surface defects on surfaces and particles impact heterogeneous freezing
(Fitzner et al., 2015;
Kiselev et al., 2017; Lo et al., 2017; Varanasi et al., 2010; Wang et al.,
2016). In general, these studies have found that defects, especially those
with crystalline faces similar to ice, lower the barrier for ice nucleation
and enhance ice formation above homogeneous temperatures. Price et al. (2018) reported observing
lower freezing temperatures when droplets were placed on a Teflon substrate
compared to on a standard silanized hydrophobic glass surface. This provides
further support for the important role that substrate choice can have on the
freezing temperature spectrum observed in droplet freezing techniques.
Aside from surface induced effects, the environment surrounding the droplets
may also influence freezing. Some research groups, including ours, deposit
their droplets into an oil or other inert liquid to prevent contamination
from the lab environment and eliminate the impact of the
Wegener–Bergeron–Findeisen (WBF) process
(Beydoun
et al., 2017; Broadley et al., 2012; Polen et al., 2016; Pummer et al.,
2015; Reicher et al., 2018; Wright et al., 2013; Zolles et al., 2015). The
WBF process occurs when one droplet freezes and takes up water vapor at the
expense of unfrozen droplets, potentially inducing evaporation of nearby
droplets. Contact by the growing frost halo around the frozen droplet can
also induce freezing of neighboring droplets
(Budke and Koop, 2015; Jung et al., 2012).
Freezing assays that do not use oil typically use fast cooling rates of up to
10 ∘C min-1
so there is not enough time for these WBF effects to
manifest, but this shifts the observed freezing temperature several
∘C colder
(Mason et al.,
2015). A cooling rate of 1 ∘C min-1 is more representative of
typical atmospheric updraft velocities and the associated cooling rates. The
oil environment prevents evaporation and these interferences, enabling
slower cooling rates and droplet refreeze experiments. However, little
assessment has been done to determine how or if these oils are influencing
droplet freezing. We found that the surrounding squalene oil reduces the
observed freezing temperature of ice-active biological particles (protein
aggregate macromolecules) in successive droplet freeze–thaw–refreeze
experiments of Snomax bacterial ice nucleants (Polen et al.,
2016). We interpreted this as caused by the hydrophobic partitioning of the
largest and most ice-active macromolecules into the highly hydrophobic
squalene oil that was accelerated by droplet freezing, which was previously
suggested by Budke and Koop (2015). Some recent microfluidic
ice nucleation techniques use fluorinated oils and/or large concentrations
of surfactant to stabilize the emulsified droplets
(Reicher
et al., 2018; Stan et al., 2009; Tarn et al., 2018). Their measured
homogeneous freezing temperatures are typically within the expected range
(-35 to -37∘C), but the surfactant may have unrecognized
influences on heterogeneous freezing processes since freezing is enhanced
via contact between the immersed particle and droplet interface
(Durant and
Shaw, 2005; Fukuta, 1975; Gurganus et al., 2014; Tabazadeh et al., 2002).
However, Tarn et al. (2018) concluded that surfactants seemed to have little
effect on the heterogeneous freezing temperatures for a number of particle
types examined using droplets prepared by microfluidics with added
surfactant.
A number of nonoil immersion alternatives to DFTs have arisen in the last
few years. Some groups choose to keep droplets open to air and rely on a
clean, dry flow of air or N2 to prevent contamination and frost growth
(Whale et al., 2015). One recent
study created a completely enclosed droplet chamber by sandwiching an
O-ring, water, and silicon substrate between cover slips (CSs) and sealing it with
vacuum grease (Li et al., 2012). This resulted in a very clean
environment conducive to homogeneous freezing of droplets with no need for a
dry air flow over the droplets. In a comparison of droplets in oil and
droplets in air, Inada et al. (2014) froze individual
3 mL droplets in n-heptane and in air and found similar freezing activity on
noncoated glass slides. They correlated early freezing for these tests to
the interfacial surface contact with the glass.
In addition to issues with surfaces and droplet matrixes, the “pure” water
itself can introduce artifacts. Almost no work has comprehensively examined
the impact of source or purity of water on homogeneous freezing. Inada et al. (2014) briefly compared tap water and MilliQ
water, but these sources showed little difference when droplets were in
n-heptane with a surfactant. Aside from this one report, to our knowledge,
no one else has compared freezing temperatures of water from different
sources. Most groups either use in-house MilliQ water systems or purchase
commercial purified water, such as HPLC-grade water that is typically
reserved for highly sensitive chemical analysis. A few groups additionally
filter their water to remove larger particles
(Hader
et al., 2014; Hill et al., 2014). It is difficult to assess how well
different substrates, water purification, and other method details influence
the background water freezing spectrum as these important details are often
not described in papers that use DFTs and the water background freezing
spectrum is not always presented.
Here we report a series of experiments we have performed on the ice
nucleation ability of “pure” water as is dictated by variables, including
the substrate, water source, and droplet matrix. The following sections
describe our experimental methodology, data analysis methods, results and
analysis for the aforementioned method variables, as well as our recommendations
for best practices for future ice nucleation experiments that use DFTs. We
compare our results with those of previous reports that used analogous
method parameters. Finally, we advance a simple proposal for future ice
nucleation experiments that will allow ready comparison between different
specific measurement systems, leading to more uniform analysis that will
accelerate our understanding of ice nucleation. We believe the ice
nucleation community has acquired many useful strategies for dealing with
issues such as contamination but that this knowledge remains largely
internal within research groups and is rarely properly communicated to the
larger and quickly growing community. This can discourage further advances
and improvements to current designs of droplet freezing systems for INP
measurements and create barriers to new groups beginning ice nucleation
research. We desire to make it common practice to report these important
method details and observations of pure water controls that are currently
often overlooked, and begin a discussion of best practices in the community
for ice nucleation experiments and droplet freezing spectrum analysis.
Droplet freezing methodology
The droplet freezing system used in this study has been updated slightly
since we first described it in Polen et al. (2016). Briefly, we
use an oil-immersion droplet freezing system composed of a cascade
three-stage thermoelectric air-chiller (TECA, AHP-1200CAS) as the heat sink,
mounted under a single-stage thermoelectric element (TE Technology Inc.,
VT-127-1.4-1.5-72) for fine temperature control. An aluminum sample dish
sits atop an aluminum block that contains the single-stage thermoelectric
element and a thermistor (TE Technology Inc., MP-3176) for temperature
measurements. Our temperature measurement has an uncertainty of ±0.5∘C based on the thermistor's accuracy and our temperature
calibrations. Droplets immersed in oil are placed in the aluminum dish,
which is covered by a clear acrylic case for imaging by optical microscopy.
No air is flown into the chamber over the oil.
Droplets are created using a variable electronic micropipette (SEOH,
3824-1LC) to deposit droplets of 1 or 0.1 µL volume. Droplets are
deposited on a substrate that sits under squalene oil (VWR, squalene, ≥98 %), mineral oil (VWR, mineral oil light), or just air. Several types
of substrates were tested in this study: hydrophobic silanized glass
CSs (Hampton Research, HR3-231), silicon wafer chips (Ted Pella,
16007), Vaseline®, a gold wafer (Ted Pella,
16012-G), a “new” gold wafer (Angstrom Engineering, 2WAU500-Q1), gold-coated cover slips (Ted Pella, 260156-G), and solid polydimethylsiloxane
(PDMS) polymer (Dow Corning, Sylgard 184). Water for these experiments is
either from our in-house MilliQ water purifier (EMD Millipore) or bottled
HPLC-grade water (Sigma Aldrich, HPLC Plus 34877).
Substrates were cleaned or prepared in the following ways for these
experiments. Silanized cover slips were used fresh from the box without any
additional cleaning. A new silanized cover slip was used for each subsequent
experiment. Silicon wafer chips were cleaned with HPLC water and acetone and
allowed to air dry before use. Gold wafer and gold cover slips were cleaned
with acetone and allowed to air dry before use. PDMS solid substrates were
soaked in squalene oil for several days before use.
A CMOS camera attached to the microscope (5× magnification) acquires an
image every 5–6 s. We are able to view on average 40–50 1 µL
droplets or 70–90 0.1 µL droplets. Frozen droplets appear black,
except in the case of a gold background in which the droplets become white.
These images are processed using a custom MATLAB program that determines
freezing events based on a grayscale value
(Budke and Koop,
2015; Jung et al., 2012; Reicher et al., 2018) and also determines the
diameter of each droplet. Sizing is calibrated using a 1 mm micrometer with
0.01 mm divisions. Initial tests run on gold substrates could not be
analyzed by this program because of the inverted color scale produced by the
dark gold background, so they were analyzed manually; “new” runs were
analyzed using an updated version of the program.
Data compilation and analysis is performed in one of two ways. The first is
a typical statistical analysis to determine the average and standard
deviation of all runs of the droplet frozen fraction as a function of
temperature. This analysis is performed when numerous arrays of many droplets
have been measured, where each array is treated as a replicate experiment.
This allows us to determine standard deviations to evaluate
experiment-to-experiment variability for replicate droplet arrays. The
second approach combines all the individual arrays into a single dataset.
As an example, in two arrays of the same sample type, one of the arrays had
a single droplet freeze early at -25∘C and the second array had
two droplets freeze at -25∘C. In this case, combining the data
would result in 3 droplets freezing at -25∘C. This second method
increases the number of droplets in a set when the number of droplets is
fairly low per run; it is also used when the number of runs is small (e.g.,
two tests of a single substrate) because statistical methods are less meaningful
for low droplet counts. Figure 1 shows an example of these two methods of
data compilation of the freezing spectra. There is some deviation between
the combined unified dataset (blue) and the average of the individual
replicates (red), but the combined data never fall outside the standard
deviation of the averaged data and thus we believe the combination approach
is an acceptable representation of our results, especially when there are
low droplet counts available for a given set of experimental parameters.
Comparison of averaging data from droplet freezing experiments on
120 total droplets measured using three replicate arrays of 40 droplets each
(red with error bars) versus combining those 120 droplets into one single
hypothetical array of droplets (blue). The standard deviation from the
average of the three replicate arrays is shown by the vertical error bars.
Ice-nucleating particle analysis
We present our data as the fraction of frozen droplets in combination with a
metric derived from that freezing spectrum – the ice nuclei concentration
(cIN) – using Eq. (1)
(DeMott
et al., 2017; Hader et al., 2014; Hill et al., 2016; Vali, 1971, 2008).
cIN is a droplet volume-normalized representation of the unfrozen
fraction of droplets,
cIN=-ln(Nunfrozen)/Vd,
where Vd is the average volume of the droplets as determined by the
image analysis program and Nunfrozen is the fraction of droplets
unfrozen at a given temperature. cIN has also been referred to as a
cumulative nucleus concentration in Vali (1971) and depicted as K(θ) in
his Eq. (13). We assume the droplets are close to spherical during
imaging in determining Vd. Hader et al. (2014) describe
the derivation of Eq. (1) and present the apparent INP (or ice nuclei)
concentration for pure water spectra in comparison to their particle
samples. The concentration of ice nuclei per droplet volume provides a way
to directly assess the impact on freezing caused by a sample as compared to
any contaminants or artifacts within the measurement. Normalizing the ice
nuclei concentration by the surface area (or mass) of particles within the
droplets defines the metric known as the ice nucleation active site density,
ns (or nm). ns and nm are often used in the ice nucleation
literature to compare different measurements of INPs. However, there are
known discrepancies when assigning ns or nm values and then
comparing identical particles under widely varying particle concentrations
(Beydoun et al., 2016). In DFT one typically
does not have any knowledge of the contaminants that induce freezing in pure
water and thus we cannot determine the density of active sites (e.g., ns or
nm) of the contaminants, unlike in studies of heterogeneous ice
nucleation where the particle surface area or mass concentration is known or
can be estimated. However, we still want to directly compare droplet
freezing spectra from different experiments, and normalizing to the droplet
volume provides a simple and useful way to do this. More importantly, the
INP concentration is also the relevant parameter for assessing how INPs
interact with and affect clouds (Hoose and Möhler, 2012).
Finally, the cIN metric allows the ready comparison of droplet freezing
spectra obtained using different droplet volumes, as different research
groups use a range of droplet sizes in DFT. However, this is only possible
if similar particle-in-water concentrations are used. ns or nm are
often used to account for these particle concentration differences, but as
discussed these metrics may not properly account for changes caused by
differing surface area or mass concentrations. The cIN metric, when
appropriately used, is advantageous as it only assumes that the INP concentration
scales linearly with the droplet volume.
We include the theoretical homogeneous freezing spectrum for our droplet
sizes in all our droplet freezing temperature spectra below. This was
produced using the parameterization of Koop and Murray (2016) to calculate
the freezing rate, J(T), and Eq. (9) from Beydoun et al. (2016) to determine
the frozen fraction, Pf, using J(T).
Results and discussion
Our results are divided into several sections that assess experimental
variables tested in our DFT measurements such as substrate type and pure
water generation methods. Each section begins with a brief review of
previous results obtained by other ice nucleation groups using an analogous
method and a discussion of why that specific method was chosen. The first
section compares droplet freezing using oil immersion compared to in-air droplet freezing.
The next section goes into detail on the impact of using different sources
and water purification. Then we discuss a variety of substrates examined and
compare them to identify what substrates display the best performance for
droplet freezing. The final section discusses tests on two droplet
generation methods we used.
Droplet immersion matrix: oil versus air
A number of droplet freezing methods have created droplets without an oil
matrix, exposing the droplets directly to air
(Li
et al., 2012; Mason et al., 2015; Whale et al., 2015). This method requires
very clean, dry conditions to avoid artifacts such as the
Wegener–Bergeron–Findeisen process and droplet contamination by aerosolized
INP. In the case of the BINARY system, droplets are physically separated
from one another by a PDMS mask (Budke and Koop, 2015). For
systems where droplet separation is not possible, dry air or nitrogen is
typically flowed over the droplets to remove ambient water vapor
(Whale et al., 2015). Flowing
dry air, however, exacerbates the issue of droplet evaporation and thus
large droplets must be used to limit the impact of evaporation over the
whole course of the temperature ramp. One unique droplet-in-air measurement
was achieved by sealing a chamber completely with a single water drop
deposited on the substrate in the chamber and then evaporating and
re-condensing the water vapor into many smaller droplets (Li
et al., 2012). This method avoids the issue of ambient water vapor
altogether by turning all the sample water into vapor and re-condensing
before freezing.
We have attempted droplet-in-air measurements within our own system but
consistently had issues with frost halo formation upon reaching -20∘C using a standard cooling rate of
1 ∘C min-1
(Budke and Koop, 2015; Jung et al., 2012). A
series of images in Fig. 2 shows this frost growth, which resulted in
the freezing of nearly all pure water droplets by -20∘C on
hydrophobic cover slips when oil was not used. Frost growth similar to this
has been reported previously by Whale et al. (2015) in their cold stage system.
This suggests that our system is not airtight enough to perform this type
of experiment when ambient humidity levels are elevated, such as during
summer. Li et al. (2012) froze their samples between two glass
cover slides which were sealed together with vacuum grease for the entire
experiment. Our chamber must be opened between runs which causes water vapor
to condense onto the sample dish and elsewhere within the sample chamber. In
this experiment, we flushed the chamber with dry nitrogen similar to
previous methods, but frost growth still occurred, though at much lower
temperatures than tests without the nitrogen flow. Figure 2 shows the
progression of frost starting at the bottom of the cover slip and continuing
to grow toward the top of the glass. We consistently found that freezing and
frost growth initiated around -20∘C, and we were never able to
approach homogeneous freezing, likely due to our slow but realistic 1 ∘C min-1 cooling rate.
Progression of frost halos in one pure water droplet freezing
experiment without an oil matrix. Dark droplets are frozen. The black line
highlights the frost growth (which is visible in the image but difficult to
see) spreading from the bottom left toward the top of the image. Aside from
the indicated frost growth, we can also see that other droplets induce
freezing in neighboring droplets, such as the droplet on the far right in
image 1 (red arrow) and the top right droplet in image 2 (yellow arrow).
Subsequently induced droplets are indicated by similarly colored arrows.
Many droplet freezing measurements use an oil matrix to prevent frost halos,
droplet evaporation, and external contamination
(Broadley
et al., 2012; Pummer et al., 2015; Wright et al., 2013; Zolles et al.,
2015), which is why we chose to use squalene oil for our measurements. Oil
also facilitates droplet refreeze experiments to evaluate the repeatability
of the ice nucleation process, and any time-dependent effects such as
particle sedimentation or aggregation
(Emersic et al., 2015; Wright et
al., 2013). In Polen et al. (2016), we proposed the use of
mineral oil for biological samples, such as Snomax, to prevent changes in
freezing behavior due to hydrophobic partitioning, which we suspected to be
the case for refreezes performed in squalene oil (C30H50).
However, in our attempts to use mineral oil (light) in pure water
measurements, the mineral oil froze around -30∘C. We consistently
saw what we at first assumed to be fogging, but upon closer inspection we
found that the mineral oil had frozen completely solid, precluding droplet
freezing experiments. Though we never saw mention of the freezing point in
the material safety data sheets provided for the mineral oils, this is a
known issue in the use of mineral oil for liquid chilling in desktop
computers. However, the WISDOM microfluidic DFT device uses mineral oil for
droplet creation and storage (Reicher et
al., 2018). The device has successfully measured homogeneous ice nucleation
down to -36∘C. Perhaps the surfactant (Span80, 2 wt %) used to
stabilize the immersed droplets prevents freezing of the mineral oil. There
are also a wide range of different mineral oils available from common
chemicals suppliers, and the specific type of oil used in WISDOM is not
known. Alternatively, the optical fogging may not be visible when such a
small volume of oil is above the droplets, as is the case for microfluidic
devices. Despite the promising results from the WISDOM method, we are wary
to suggest that any other groups attempt the use of mineral oil for droplet
freezing measurements before further investigation into how the oil's
freezing may impact water droplet freezing. For all oil-immersion
experiments mentioned in the following sections, squalene oil was used as
the oil matrix, following the method of Wright and Petters (2013). Previously, we have shed light on squalene oil
reducing the observed ice nucleation activity of Snomax bacterial particles
and concluded that this was due to hydrophobic partitioning of large protein
aggregates (Polen et al., 2016). This was only observed in
droplet refreeze experiments of Snomax, and we do not observe this effect on
any other particle sample type we have tested. Squalene oil remains our
recommended immersion oil for most droplet freezing experiments.
Water sources and purification
Many in the ice nucleation community use MilliQ water or similar commercial
systems to purify their laboratory's in-house water
(Inada
et al., 2014; Pummer et al., 2015; Rigg et al., 2013; Tobo, 2016; Umo et
al., 2015; Wright and Petters, 2013). Some groups have used bottled HPLC
grade or other similar water for their DFT
(Fornea et al., 2009; Wright and
Petters, 2013). Still others use alternative methods, such as condensation,
to create droplets
(Campbell
et al., 2015; Li et al., 2012; Mason et al., 2015). We compared water
produced by our in-house MilliQ system with bottled HPLC-grade water from
Sigma Aldrich (Fig. 3). Both water types were also filtered using 0.02 µm pore size Anotop filters before droplet generation. In general,
the droplet freezing spectra obtained from the two types of water are very
similar to one another. With ∼1000 droplets for each water
type, we find little difference in the apparent INP concentration as well.
The biggest deviation came from runs of HPLC water that was filtered
multiple times over many weeks using the same Anotop filter, which shows an
increase in ice nuclei around -25∘C, though this is not outside
the standard deviation of our other samples. This result indicates that
either purchased HPLC or produced MilliQ water could be useable for droplet
freezing experiments. As MilliQ water systems use a series of filter
cartridges and a membrane filter to remove dissolved contaminants,
particles, and ions from the supplied water, the quality of the produced
water achieved will depend on the quality of the original water supply
source. The “house” water supply is beyond the control of most research
groups. Along with other issues we have experienced using MilliQ water that
we discuss below, high-quality bottled water with additional filtration may
be a better and more reliable water source for ice nucleation studies.
Comparison of pure water freezing using filtered MilliQ-produced
water and filtered purchased HPLC water. Shown are the measured droplet
freezing temperature spectra (a) and the derived INP concentration (b).
HPLC water was filtered using a new Anotop 0.02 µm filter for
each bottle of water (blue), or the same filter for multiple stock bottles
of water (black). The results from typical MilliQ water arrays are shown in
purple. The parentheses next to each legend entry contain the number of
arrays of droplets (A) and the total number of droplets across all arrays
(N). The gray dashed line indicates the theoretical homogeneous freezing
curve of 0.1 µL droplets, using the parameterization of Koop and
Murray (2016).
We experienced significant and unexpected issues in continuing to use MilliQ
water for our droplet freezing tests and experiments that caused us to
switch to bottled HPLC water for all our future experiments. The
MilliQ-produced water can result in very inconsistent results for pure water
droplet controls if the particle membrane filter is not changed on a regular
basis. These contaminants were apparently not removed by filtering the
poor-quality MilliQ water with a 20 nm pore Anotop filter, for reasons
unknown to us. This is a serious concern as there is no easy way to
determine the status of the filter; the MilliQ system only measures the
resistivity of the water as a measurement of the ionic strength, as well as
total carbon concentration. Figure 4 shows results from trying to diagnose
the issue behind a much warmer than typical background freezing spectrum for
MilliQ water droplets. The results were highly inconsistent, with droplets
in some arrays freezing in temperatures as warm as -13∘C, some droplet arrays
freezing completely before -25∘C, and one array with a median
freezing temperature, N50, of -28∘C that rivaled our least
contaminated pure water tests at the time. We also found a significant
decrease in the early freezing droplets when we let the MilliQ system run
for 5 min before collecting water used to generate the control droplets.
These caveats in using MilliQ water will likely depend greatly on different
lab environments, protocols, the number of users, and differences in the
original water supply sources. Thus, we chose to perform future experiments
with bottled HPLC water in an attempt to improve experiment-to-experiment
consistency by removing the variability posed by the MilliQ system's water
quality. Additionally, we filter our water before use with a 20 nm pore-size
Anotop filter to further reduce variability and remove small particles that
may be a source of INPs. The use of an Anotop filter was suggested to us by
Thomas Hill, as is used in the CSU Ice Spectrometer system
(Garcia
et al., 2012; Hill et al., 2016).
A series of tests on MilliQ-generated water droplets to determine
contamination sources. Droplets displayed inexplicably high freezing
temperatures compared to filtered HPLC water at the time (solid brown with
error bars). Temperatures for N50 ranged from -20 to -29∘C from day to day. Error bars indicate standard deviation of
data for the filtered HPLC water. The gray dashed line indicates the
theoretical homogeneous freezing curve of 0.1 µL droplets, using the
parameterization of Koop and Murray (2016).
Substrate tests
In this section we discuss an extensive series of experiments in which we
tested the effect of various substrates on the observed freezing spectra for
pure water droplets. Our goal is to identify substrates that display a
reproducibly low amount of interference in the pure water controls by
allowing the droplets to freeze close to the expected homogeneous freezing
temperature. This is -33 to -35∘C for the droplet volumes used
here based on Eq. (7) from Pruppacher (1995) and the homogeneous freezing
spectrum predicted using the parameterization of Koop and Murray (2016)
(dashed lines in all the droplet freezing temperature spectra). Except when
noted, all arrays were created using filtered HPLC water. Each of these
substrates has been shown to work reasonably well for droplet freezing
experiments in the past.
Hydrophobic cover slips
Hydrophobic cover slips are one of the most used substrates for DFTs
(Bigg,
1953; Durant et al., 2008; Iannone et al., 2011; Mason et al., 2015; Murray
et al., 2011; Wright and Petters, 2013). These can be made in-laboratory by
silanizing a standard glass slide
(Fornea et al., 2009; Wright and
Petters, 2013) or can be purchased presilanized
(Beydoun
et al., 2016, 2017; Iannone et al., 2011; Mason et al., 2015; Polen et al.,
2016; Whale et al., 2015; Wheeler et al., 2015). In general, results of pure
water freezing on hydrophobic cover slips are variable. Whale et al. (2015) reported the 50 % droplet
frozen fraction (N50) close to -26∘C for 1 µL droplets.
Hader et al. (2014) reported N50 at -30∘C
for 150 nL droplets, while Iannone et al. (2011) found
N50 at -37∘C for 60 nL droplets. While an increase in
homogeneous freezing temperature is expected for larger droplets, based on
classical nucleation theory (CNT) we expect all of these droplet sizes to
freeze homogeneously below -30∘C
(Koop and
Murray, 2016; Pruppacher, 1995; Vali, 1999). This implies that the larger
droplets froze heterogeneously due to some unintended ice-nucleating
material or surface.
Our results using presilanized hydrophobic cover slips are similar to those
reported using analogous methods by Hader et al. (2014) and
Whale et al. (2015) for our larger
and smaller droplets, respectively. Figure 5 displays our freezing spectra
for large and small HPLC droplets on hydrophobic cover slips. The N50
for smaller (0.1 µL) droplets (black and blue) is -29∘C,
and -27∘C for larger (1.0 µL) droplets. Freezing onset
begins consistently around -20∘C, and final droplets freeze
between -33 to -35∘C as is expected for these droplet sizes.
Importantly, we note that freezing pure water droplets simultaneously
alongside sample droplets containing test particles (shown in green in Fig. 5) does not impact the freezing temperature spectrum when compared to the
same droplet size data (red). This, in conjunction with the similar
literature results, suggests that variability between different DFT systems for
pure water controls using hydrophobic cover slips may be explained primarily
by the droplet size. However, we find a counterintuitive trend when
comparing the apparent INP concentration, cIN, for these measurements.
When comparing larger and smaller droplets, the concentration of ice nuclei
is actually lower for larger droplets (red vs. blue points in Fig. 5). This
could mean that the INP concentration for these samples is not directly
related to the droplet volume but instead is more directly tied to the
contact surface area with the substrate. We propose that this may be caused
by one of two effects: (1) smaller droplets have larger surface
area-to-volume ratios and by normalizing to volume using cIN we are
undercorrecting interferences caused by droplet–surface contact for small
droplets, or (2) larger droplets have higher contact area with the surface
and thus by correcting to volume we are overcorrecting interference
experienced by larger droplets. More work is necessary to connect the
contact area to the elevated pure water freezing temperature. This size
effect is also observed for the gold-coated substrates discussed in Sect. 4.3.4.
Droplet freezing temperature spectra (a) and apparent ice
nuclei concentration, cIN, (b) for pure water droplet freezing
measurements on a hydrophobic cover slip. In all experiments HPLC water that
was filtered using an Anotop 0.02 µm syringe filter was used. Each data
series has been binned into 0.5 ∘C temperature increments. The
red data series is from large (1.0 µL) droplets, green is from large
(1.0 µL) droplets measured alongside biomass burning aerosol sample
droplets (Fig. 12), blue is from small (0.1 µL) droplets using a new
Anotop filter for each stock bottle of filtered water, and black is small
droplets using a singular Anotop filter for many different stock bottles of
water. The parentheses next to each legend entry contains the number of
arrays of droplets (A) and the total number of droplets across all arrays
(N) tested for each experiment type. Error bars are standard deviations for
the replicate droplet arrays. The gray and black dashed lines indicate the
theoretical homogeneous freezing curves of 0.1 and 1 µL
droplets, respectively, using the parameterization of Koop and Murray (2016).
We have also observed some batches of purchased cover slips to induce
freezing as warm as -18∘C, and with much greater variability in
the freezing spectra. Thus, it is important to evaluate each batch of
cover slips to test for these potential issues. Ideally pure water control
droplets will be placed along with droplets containing the particle sample
of interest on the same cover slip to directly evaluate the background freezing
spectrum on that specific cover slip. This is especially important when
working with particle systems of weak ice activity that freeze close to the
background water temperature range.
Silicon wafers
A few groups have utilized silicon wafers for droplet freezing experiments
(Li et al., 2012; Peckhaus et
al., 2016). Peckhaus et al. (2016)
used droplets of 107 µm in diameter and found 90 % of droplets froze
below -35∘C. All droplets reported by Li et al. (2012) froze below -37.5∘C for 10–70 µm in
diameter. Additionally, Li et al. performed detailed assessment of
hydrophobic and hydrophilic silicon wafers used in pure water ice nucleation
experiments. They found that both types of wafer produced nearly homogeneous
freezing for pure water droplets.
We investigated ice nucleation on silicon wafer chips typically used for SEM
analysis. Several silicon chips were placed in the sample dish with squalene
oil, and 0.1 µL (∼600µm) HPLC droplets were deposited
on them. Due to the small size (5×7 mm) of the chips, the number of droplets
on each wafer chip was very low (∼10), and thus we combined
all the data from twelve chips as though it were a single surface containing
120 droplets (Fig. 6). We find similar freezing activity to the hydrophobic
cover slips, with onset freezing beginning around -21∘C, reaching
50 % around -26∘C, and finishing at -35∘C. The
apparent INP concentration for the silicon wafer also falls close to the
cover slip data (Fig. 6). We are using much larger droplets (∼6–60× diameter) than the groups who have used silicon substrates previously,
so we do see higher freezing temperatures as expected. However, due to the
similar behavior and apparent INP concentration we observe using the glass
cover slips and the silicon wafer, we cannot conclude that silicon provides
a more ideal surface for INP studies than silanized hydrophobic glass. The
superior performance reported by other groups using silicon wafers may be
due to higher purity water than we have access to, or other method details
that make a direct comparison challenging.
Comparison of freezing on silicon wafer chips (green) against
hydrophobic cover slips (blue), following Fig. 5. The freezing temperature
spectrum is on (a), and the retrieved cIN is on (b). Both
datasets use 0.1 µL droplets. The data from all replicate arrays using
silicon (green) are combined into one series and thus no error bars can be
determined. The parentheses next to each legend entry contains the number of
arrays of droplets (A) and the total number of droplets across all arrays
(N). The gray dashed line indicates the theoretical homogeneous freezing
curve of 0.1 µL droplets, using the parameterization of Koop and
Murray (2016).
Vaseline®
First utilized by Tobo (2016) for the Cryogenic Refrigerator
Applied to Freezing Test (CRAFT) droplet freezing instrument,
Vaseline® petroleum jelly can be spread onto a
clean surface to create a makeshift hydrophobic substrate. The results from
Tobo (2016) indicate great promise in this substrate for DFT as
the large, 5 µL droplets froze with N50=-33∘C, approaching the temperature predicted by CNT for homogeneous freezing. We
examined large (1.0 µL) droplets on Vaseline® spread onto our aluminum sample dish in air, similar to Tobo (2016), as well as smaller droplets (0.1 µL) on
Vaseline®, and within a squalene oil matrix. The
results are shown in Fig. 7. For tests without the oil matrix, we found
quite warm onset freezing temperatures while only a few droplets approached
the homogeneous regime. We found similar trends whether we used MilliQ water
or filtered HPLC water. However, once we utilized smaller droplets in an oil
matrix, the early onset freezing vanished and we observed good background
freezing curves with lower onset and N50 temperatures. We hypothesize
that our inability to reproduce pure water freezing near the homogeneous
regime using a Vaseline® coated substrate as in
Tobo (2016) is due to the difference in cleanliness between
laboratory environments as well as differences in applying the
Vaseline® layer. The oil matrix does eliminate
much of the early, high-temperature freezing that is likely caused by
contamination or an unevenly coated surface. This suggests that the use of a
laminar flow hood or glove box may be necessary to achieve such low
background freezing temperatures without oil when the droplets are exposed
to air. Tobo prepared their droplet arrays inside a glove box within a clean
room environment, and such clean conditions are not readily available to
many research groups. Uniform application of
Vaseline® requires precision and a specialized
spatula to get around the lipped design, and nonuniform application will
increase the risk of surface-induced freezing by any exposed underlying
substrate. Interestingly, we note that one benefit to
Vaseline® is we did not observe evidence of WBF
effects on neighboring droplets when in air, which makes it favorable for
droplets-in-air experiments if interferences can be reduced. Creation of a
surface specifically designed for Vaseline®
application is an important consideration if this promising technique is to
be utilized more widely.
Pure water droplet experiments on a Vaseline-coated substrate,
following Fig. 5. The HPLC water using silanized cover slip data in blue
are displayed for comparison, as are the data from the hydrophobic cover slip
using small droplets (Fig. 5, blue). The data from replicate arrays for
Vaseline are combined as described in Sect. 2 and thus no error bars are
determined for these. Three sets of experiments on Vaseline are shown: black
is small droplets (0.1 µL) of HPLC filtered water in oil, red is large
droplets (1.0 µL) without oil, and green is large droplets of MilliQ
water without oil. The parentheses next to each legend entry contains the
number of arrays of droplets (A) and the total number of droplets across all
arrays (N). The gray and black dashed lines indicate the theoretical
homogeneous freezing curve of 0.1 and 1 µL droplets,
respectively, using the parameterization of Koop and Murray (2016).
Gold-coated substrates
Limited tests have been reported using gold-coated substrates in DFTs.
Häusler et al. (2018) etched the
surface of a gold-coated substrate and found near-homogeneous freezing
temperatures (N50≈-37.3 ∘C) for pure water
droplets (45 µm) despite obvious nanoscale features in the freezing
chip's cavities. In our tests we used two substrates: a gold-coated silicon
wafer and a gold-coated glass CS. Our results are shown in
Fig. 8. The HPLC water on gold wafer produced a very low freezing
temperature, with N50 around -32∘C; similarly small droplets
of MilliQ water on the gold CS had N50 at -30.5∘C. Additionally,
our first test on a second gold wafer (red) with many more droplets showed
N50 at -33.9∘C. However, large HPLC water droplets on the
gold CS (N50=-26.5∘C) froze froze at a similar temperature to large droplets on the
hydrophobic silanized cover slip (N50=-27∘C). When
comparing the apparent INP concentrations, we again see the trend of larger
droplets having lower cIN than smaller droplets. In this case the
difference is even starker, with nearly half an order of magnitude difference
in cIN between large and small droplets on gold CS at T < -30∘C. Additionally, we find that upon cleaning and reusing a gold
wafer (orange) the freezing spectrum and apparent INP concentration
increased compared to the first use (red) and became similar to the
silanized cover slip. This could suggest that cleaning the surface with
acetone and drying with dry, particle-free air affects the surface in some
way, making it more ice active, or just does not adequately clean the
substrate. More analysis should be performed to identify the impacts of
cleaning on the gold surface. If this issue can be solved or avoided and the
surface can be cleaned without introducing contamination or ice-active
surfaces, then gold has the potential to be a near-ideal substrate. One
issue with gold surfaces is they are soft and easy to scratch, even with
careful handling using Teflon-coated tweezers. This could create more ice-active surface sites over time and also be an interference in the droplet
optical microscopy imaging. Gold is also much darker than the other
substrates we tested, requiring manual retrieval of the droplet freezing
spectrum.
Measurements of pure water droplet freezing on gold substrates are
shown following Fig. 5. The data from small HPLC water droplets on a
silanized cover slip are displayed in blue for comparison (Fig. 5, blue).
The gold data displayed are using HPLC water droplets on a gold wafer
substrate (brown), small MilliQ droplets on a gold-coated glass cover slip
(gold CS) (lilac), and large HPLC water droplets on a gold CS (green). Also
displayed are data from small droplets on another gold wafer upon first use
(red), and subsequent small droplet arrays on the same wafer following
cleaning and drying, with associated error bars (orange). The parentheses
next to each legend entry contains the number of arrays of droplets (A) and
the total number of droplets across all arrays (N). Error bars show standard
deviation from replicate droplets arrays. The data from the gold wafer
(brown and red) and small droplets on a gold CS (lilac) were combined into one
series and so no error bars are derived. The gray and black dashed lines
indicate the theoretical homogeneous freezing curve of 0.1 and 1 µL droplets, respectively, using the parameterization of Koop and
Murray (2016).
Polydimethylsiloxane (PDMS)
Polydimethylsiloxane (PDMS) is a widely used hydrophobic, cross-linked
polymeric material. PDMS has been used in microfluidic droplet freezing
approaches
(Reicher et al.,
2018; Stan et al., 2009), but not as a substrate for conventional DFT.
Reicher et al. (2018) provided a comparison
of microfluidic systems with other DFTs that showed comparable homogeneous
ice nucleation rates for all methods. The excellent performance of these
published microfluidic techniques, and our own experience with microfluidic
devices fabricated from PDMS for DFTs, led us to test PDMS as a droplet
freezing substrate. We studied two types of PDMS: a squalene oil-soaked
hydrophobic PDMS surface (untreated), and a surface that was exposed to a
plasma, then baked at 180 ∘C, and soaked in squalene oil for
several days (treated). The latter represents PDMS as would be typical for a
microfluidic device fabricated using conventional soft lithography. One
important note is the treated PDMS did return to its original hydrophobic
form following plasma treatment and oil soaking and displayed similar
freezing results to the untreated PDMS (Fig. 9). The pure water freezing
spectra are again similar to our silanized cover slip results, as we have
seen for most of the other substrates tested. Each of the PDMS tests was
within the standard deviation of the glass CS data, suggesting that the PDMS
surface does not provide any inherent benefit over hydrophobic silanized
glass. However, PDMS is quite cheap and easy to manipulate if you
have the resources to do so, which makes it a quite useful substrate for ice
nucleation studies. The hydrophobic nature of the polymer can make it prone to
contamination, however, and PDMS is often used as a sorbent in environmental
contaminant sampling (Choi et al., 2011;
Thomas et al., 2014). One other potential downside to PDMS for DFTs is its
poor heat transfer properties. The thickness of the PDMS layer must be
consistent for each experiment or the temperature calibration will be
inaccurate.
Measurements of HPLC pure water droplet freezing on polydimethylsiloxane (PDMS) are shown
in red and green, following Fig. 5. The data from small droplets on a
silanized cover slip are displayed for comparison in blue (Fig. 5, blue). The
PDMS data were obtained using treated (red) and untreated (green) PDMS
polymer with small droplets. The parentheses next to each legend entry
contains the number of arrays of droplets (A) and the total number of
droplets across all arrays (N). Error bars on green data show standard
deviation from replicate arrays, while the red data are combined into one
series as explained in section 2. The gray dashed line indicates the
theoretical homogeneous freezing curve of 0.1 µL droplets, using the
parameterization of Koop and Murray (2016).
We have recently developed a new “store-and-create” microfluidic device
that shows great promise in eliminating the interferences from surface
interactions as seen in our and other groups' DFTs
(Bithi and Vanapalli, 2010; Boukellal et al.,
2009; Sun et al., 2011). This device will be fully described in a
forthcoming paper. The PDMS device holds up to 600 droplets of
∼6 nL volume encased in squalene oil. Each droplet is stored
in an isolated microwell, completely engulfed by oil. Initial results for
pure water droplet freezing are shown in Fig. 10 and compared with
hydrophobic silanized cover slips. We find a N50 around -34∘C, with less than 10 % of droplets freezing above -32∘C.
Interestingly, we see that the apparent INP concentration continues the same
trend as the 0.1 µL droplets on a hydrophobic cover slip. This is likely
because the droplets lack contact with any solid surface inside the
microfluidic device and the contaminants causing this nonhomogeneous
freezing are related to water or oil contaminants.
Comparison of pure water droplet freezing in our new microfluidic
chip (red) versus using a silanized cover slip (CS) (blue), following Fig. 5. Droplets in the microfluidic chip are 6 nL in volume and droplets on the
CS are 0.1 µL. Error bars show variability of droplet freezing
between different replicate arrays. The parentheses next to each legend
entry contains the number of arrays of droplets (A) and the total number of
droplets across all arrays (N). The gray and dark red dashed lines indicate the
theoretical homogeneous freezing curves of 0.1 µL and 6 nL droplets,
respectively, using the parameterization of Koop and Murray (2016).
Droplet creation methods
Some experimentation was done to compare two types of droplet creation
techniques, using a syringe or autopipette. We have experienced issues with
both approaches that we briefly describe here so that other users can be
vigilant in avoiding these problems. Syringes create droplets with volumes
of 0.1 µL that are very consistent in droplet size, much more
consistent than pipettes working at similar volumes. However, using syringes
has long-term usage issues when the water is not completely particle-free as
they are difficult to clean. Each syringe (Hamilton Company, model 7001 KH)
we used eventually became contaminated beyond use (evaluated by pure water
control freezing spectra) and needed to be replaced. This becomes expensive
when running freezing assays repeatedly for weeks and months at a time.
Syringes are also not automated and can be fragile, requiring careful use
that can be time consuming when creating an array of 50+ droplets.
Switching from a syringe to an electronic pipette with disposable tips
improved the long-term consistency of droplet creation. In our experience
sterilized tips in boxes remain contamination-free the longest. However, we
are still uncertain about the amount of contamination introduced by the
pipette tips. The best freezing experiments with pipetted droplets still
show them freezing significantly above the homogeneous freezing regime, which could be
caused in whole or in part by pipette tips, remaining water
contaminants, or the silanized glass cover slip substrate.
Discussion
The results presented above provide a detailed account of many tests run on
pure water ice nucleation measurements using our cold-plate DFT. Figure 11
displays a summary of the major findings from different substrate tests.
Vaseline provided the least consistency between droplet freezing
temperatures with the highest onset freezing (T=-18.5∘C),
even when droplets were surrounded by oil. However,
Vaseline® had the one benefit of preventing
frost-induced freezing compared to hydrophobic cover slips, when droplets
were not in oil. Despite this, Vaseline® poses a
significant number of issues, such as uneven surface coating and an unclean lab
environment, which makes it impractical for many researchers. The gold wafer
showed the most promise for our standard droplet freezing method, with
N50 at -33.9∘C, but it also had some quite warm onset
freezing (T=-19∘C) and when cleaned with acetone produced a
similar freezing curve to other substrates (Fig. 11). Gold wafers have the
caveats that they are quite expensive and the surface is easily scratched,
as well as the potential for contamination when cleaning, which we saw when using
the gold wafer (“Cleaned” vs. “New”, Fig. 11). PDMS, hydrophobic cover
slips (both shown in Fig. 11), and silicon wafer chips (not shown) displayed
very similar freezing behavior, with N50 between -27 and -29∘C, only slightly warmer than the gold wafer. Our new microfluidic device
shows enormous improvements over these other methods, with less than 10 %
of droplets freezing at temperatures warmer than -32∘C, consistently. The reason
this device has such low freezing is likely because droplets are completely
engulfed by a layer of oil and have little to no contact with the PDMS
surface, unlike typical droplet-in-oil DFTs. We also observed mineral oil
freezing at temperatures warmer than homogeneous freezing, and thus it should
not be used for this type of analysis. We found that MilliQ water, when the
system is operating properly, displays similar ice-nucleating properties to
filtered HPLC water. Few studies in the past have analyzed and compared
different water sources, so it is difficult to assess its impact on the ice
nucleation results. We experienced significant interferences using MilliQ water, caused by the final particle membrane filter failing with no other indication of failure. This issue cost us several weeks of intensive testing to
identify and resolve, which is why we recommend the use of bottled
HPLC-grade water, with additional particle filtering, to remove the
variability in the quality of the water used.
Summary of pure water droplet freezing spectra for different
substrates tested. Boxes show the 25 % and 75 % frozen quartiles and the
median, N50, is indicated by the line inside each box. Red markers are
the temperatures of the first onset freezing droplets with error bars
showing variability between different replicate droplet arrays. CS represents droplets on a silanized glass cover slip. No error
bars for the onset freezing for Vaseline and gold wafer are shown because
only one array was run of each. Whiskers show the 10 % and 90 % droplet
frozen fractions. Next to each substrate name in parentheses is the number
of droplets tested. Filtered HPLC water droplets produced by an electronic
pipette were used in all of these measurements, except for the microfluidic
chip which generated the droplets on-chip. Droplets were 0.1 µL in
volume, except for the 1.0 µL on the cover slip, and the 6 nL droplets
created in the microfluidic chip.
Image of droplets containing biomass burning aerosol (left half)
and pure water droplets (right half) immersed in squalene oil on a silanized
glass cover slip. Droplets containing aerosol sample have mostly frozen
(turned dark) and pure water droplets have remained largely unfrozen (grey)
at -23∘C.
Recommendations for droplet freezing method and analysis
protocols
The intent of this study is to bring to light some of the unpublished and
underreported results, experiences, and insights that are required to
effectively examine heterogeneous ice nucleation using droplet freezing
methods, especially when the ice-nucleating particles have low freezing
activity. Providing a basic overview of the best results obtained for pure
water controls in our tests and the literature can lead to a series of best
practices or recommendations and more method standardization. While DFTs
have improved to produce accurate and reliable immersion freezing
measurements, we have certainly not achieved the ideal experimental methods
and strategies. To continue to advance DFTs it is important that researchers
present their raw data with all their imperfections, including pure water
controls, comprehensive descriptions of method details and data analysis
procedures, and raw droplet freezing temperature spectra. This is the
information required for the ice nucleation community to learn from each
other and continue to improve our experimental methods. This will also
enable new research groups to start making accurate and reproducible
freezing measurements more quickly and reliably. The following are
recommendations that we propose all research groups incorporate into their
droplet freezing experiments and publications of these results.
We suggest that researchers present an assessment of raw frozen fraction
curves or spectra for all types of analysis performed (homogeneous and
heterogeneous freezing). This practice is often followed in the literature,
but there are plenty of instances where these data are not provided and
instead the retrieved ice-active site density (ns, nm) is the
exclusive result published. Frozen fraction spectrum is a base level
analysis that all groups must do to retrieve any further parameters such as
ns and nm. Thus, presenting the raw frozen fraction curves for all
data is a simple addition to any paper, even if it is presented within
the supplemental section. The raw spectra can be used by the authors and
others to diagnose contaminants or inconsistencies between similar droplet
freezing experiments and methods.
We encourage retrieving the apparent INP concentration, cIN, as an
especially useful metric for quantifying the background freezing spectrum,
and for comparison of different DFTs. This metric has often been used as an
intermediate step to determine ice-active site density, but we believe it,
in and of itself, is a useful metric that should be reported, especially
when examining pure water controls. Since there is no way to know the
specific properties of any contaminants within pure water droplets directly,
having an idea of the level of contamination per volume of water provides
useful insights into what may be preventing the homogeneous
freezing temperature regime from being reached. Contrary to the frozen fraction curves, INP
concentration is normalized to the droplet volume, which makes it an
effective way to compare pure water controls in different DFTs that
invariably measure different droplet sizes.
We will note there are some unexpected trends for our results regarding the
retrieved cIN spectra when dealing with different droplet sizes. In
particular, we see a lower concentration of ice nuclei when we use larger
droplets, despite normalizing to the volume even when the same experimental
conditions are used. This suggests that normalizing to volume may
overcompensate for the differences between droplet sizes. We believe this
may be because the apparent INP concentration is less influenced by the
concentration of particles in the water and more influenced by the contact
surface area between the droplet and the surface. Thus, normalizing to
volume may not be the best metric for determining the activity of contaminants
in homogeneous nucleation. Fixing the droplet volume can remove this issue
and is another one of our recommendations below.
Procedures to correct the raw freezing spectra for interference from
background freezing observed in “pure” water droplets should be reported.
Retrieval of the cumulative nucleus concentration, K(T) or cIN, following
previous approaches
(DeMott
et al., 2017; Hader et al., 2014; Hill et al., 2016; Vali, 1971, 2008) and
as we have done here, is our recommended approach. This background freezing
spectrum should be reported, and then subtracted from the sample's spectrum.
Alternatively, retrieval of the differential nucleus concentration, referred
to as k(θ) in Vali (1971), is also recommended to assess the INP
concentration in the sample versus that caused by background freezing. This
approach can be used as a means of quantitatively attributing the INP signal
to the sample versus the background for each droplet over the entire
freezing spectrum. The differential nucleus concentration can be calculated
using the following:
k(T)=-1/(Vd×ΔT)×ln[1-ΔN/N(T)],
where k(T) is the differential ice nucleus concentration, Vd is the
droplet volume, ΔT is a temperature step that must be prescribed in
the analysis, ΔN is the number of droplets that froze in that ΔT temperature step, and N(T) is the total number of unfrozen droplets at T. An
important aspect is that ΔT is not the temperature step
of the actual measurements, such as from the frequency at which images are
acquired. To produce meaningful k(T) spectra the ΔT should be large
enough so that more than one droplet typically freezes in a given
temperature step. In our initial k(T) analysis we found that a ΔT interval
of 0.05 or 0.1 ∘C worked well for our experimental conditions.
ΔT should be varied until a reasonable representation of the droplet
freezing spectrum is produced that displays the important features of the
spectrum and allows the sample to be distinguished from the background
freezing of a control. Realizing that this is an important and nuanced
detail, Gabor Vali is planning to produce a tutorial explaining the use of
k(T) and selection of ΔT, using some of our data to illustrate this
method. Referring back to Eq. (2), as an example, given an array of 100
droplets and a specified ΔT of 0.1 ∘C intervals, if the
first 2 droplets freeze within one measurement interval, then ΔT=0.1∘C, ΔN=2, and N(T)=98. Using this metric, each
freezing event in the interval ΔT is the result of at least one
active INP, but given a small ΔT and a large N the interval can be
approximately attributed to a single active INP.
Inherent to all droplet freezing methods is the assumption that the freezing
of any droplet at a given temperature interval is caused by the combination
of INPs present from the sample plus any background freezing due to
impurities and substrate artifacts. The differential ice nucleus method,
k(T), provides a quantitative assessment of the sample versus the background
INP concentration at each temperature interval. k(T) is an alternative
approach to the more commonly used method of just subtracting the cumulative
K(T) or cINP background spectrum from the cumulative sample spectrum. This
k(T) analysis method is discussed in detail by Gabor Vali in the comment
(10.5194/amt-2018-134-SC1) he provided on the discussion version of this
paper
(https://www.atmos-meas-tech-discuss.net/amt-2018-134/amt-2018-134-SC1-supplement.pdf,
last access: 18 September 2018), based on the framework originally laid out in Vali (1971).
Restricting the freezing curve analysis to the 5 %–95 % frozen droplet
fraction, as is now being done by some groups to exclude anomalously early
and late freezing droplets, is not recommended. The ice activity of
individual particles is very much a diverse spectrum, resulting in some
droplets in a freezing array containing more rare ice-active INPs that
induce freezing at warmer temperatures
(Augustin-Bauditz
et al., 2016; Conen et al., 2011; O'Sullivan et al., 2015; Petters and
Wright, 2015; Pummer et al., 2012, 2015). This can occur even in experiments
on “pure” single-particle-type samples such as Snomax bacterial and illite
NX mineral particles
(Beydoun et al., 2016,
2017). Excluding the early freezing droplets would erroneously omit
information on these important rare INPs whose greater ice activity cause
freezing at anomalously warm but atmospherically relevant temperatures.
Important method details should be documented. These include details
related to the production of pure water used for droplet generation
(including any additional filtration steps), any characterization of the
purity of the water, and presentation of the freezing spectra for control
droplets. Details regarding the substrate used and how it was prepared and
cleaned are also important. Temperature calibration procedures should also
be documented. DFTs are very subject to contamination, requiring new clean
surfaces and sample handling vessels to be used. This is especially a
concern when working with very ice-active biological particles such as
Snomax and other bacteria. Droplet preparation methods such as the pipette,
syringe, or microfluidic technique used; how the particle sample was
(re-)suspended in the water; and the length of time the particles spent in
water prior to analysis are additional method details that may appear
trivial but can have important consequences on the observed ice nucleation
properties. This is especially critical in DFT comparison studies between
different groups using the same samples.
We recommend the use of bottled HPLC-grade or similar purchased water for
droplet generation, as opposed to MilliQ-produced water. MilliQ systems can
certainly produce high-quality water with freezing temperatures near the
homogeneous regime but are subject to sudden unannounced changes in their
water quality, and are also limited by the quality of the source water fed
into the MilliQ system. Our own experiences and frustrations caused by the
variability of MilliQ water has caused us to exclusively use HPLC-grade
bottled water that we further filter with a 0.02 µm Anotop filter and
then store in a clean glass bottle in the refrigerator. Interestingly, we
have also heard that other research groups found bottled water is not as
consistent as their MilliQ-produced water. This demonstrates the
inconsistencies and variabilities that are common between research groups
and supplies, further emphasizing the importance of routinely assessing and
reporting the water background freezing spectrum that each group and method
observes. We suggest that no matter what source of water is used that
researchers regularly test it and report their findings in all publications
when possible.
Based on the findings in this study, we recommend silanized cover slips
as the primary substrate for DFT as they are the least expensive option that
display the most consistent freezing behavior. Alternatively, if the cost of
gold wafers is not prohibitive and measures are taken to avoid scratching
the surface, then gold is a suitable substrate. Additionally, we note the
incredible potential of microfluidic devices used in this study and others.
We also recommend autopipettors over syringes for droplet generation due to
their ease of use and reduction of potential contamination from repeated use
compared to syringes.
Droplet volumes and particle-in-water concentrations should be
standardized as much as possible. The commonly used ice-active surface site
density metric (ns, nm) has regrettably been found to not properly
normalize and correct for differences in the particle surface area or mass
present in droplets during DFT. For example, just by changing particle
concentration the ns values we retrieved for illite NX shifted by
several orders of magnitude (Beydoun et al., 2016). Many groups purposefully
vary particle concentration to access different observable freezing
temperatures, but the ice nucleation properties retrieved using different
concentrations of the same system may not be consistent. The best way to
evaluate this (in)consistency is to ensure overlap in the ns spectrum
retrieved versus temperature, so these values can be directly compared at
the same temperature. This requires using small steps in particle
concentration of about a factor of 5. Reporting the raw freezing spectra
also helps to evaluate these issues. Standardizing the total particle
surface area present, by standardizing the droplet volume and particle
concentration used, may also reduce these discrepancies.
Interferences from the substrate and/or immersion oil used, the pure
water, and other potentially unrecognized sources should be regularly
evaluated using pure water controls that are prepared using
procedures identical to those used for the sample droplets. Controls should be run with a
frequency determined by the level of variability in the background freezing
spectrum observed using these controls, and by how close the particle
sample's freezing spectrum lies compared to the background spectra. We also
suggest that researchers perform handling or method blanks alongside any
experimental particle collection. Method blanks simulate all aspects of the
particle collection and extraction process, without having a particle
sample. This accounts for contamination or other issues that may
occur as the sample filter is being handled better than methods running
only a pure water control blank. For example, Vergara-Temprado et al. (2018a) found that the freezing spectrum of their filter
handling blanks for their soot aerosol measurements showed similar droplet
freezing spectra to the soot samples themselves, and significantly higher
than their water blanks. Any new batch of purchased substrates must be
evaluated to assess batch-to-batch differences, which we have observed for
silanized glass cover slips. Studies of low ice-activity systems such as
soot particles and biomass burning aerosol require careful and extensive
background control experiments. In our measurements of biomass burning
aerosol we prepare a droplet array on a silanized cover slip that consists
of a 1:1 ratio of pure water control droplets and biomass burning aerosol-containing sample
droplets (Fig. 12). This provides a direct assessment of any interferences
from the same substrate used for sample analysis, and equal statistics for
control and sample droplets.
DFTs are often evaluated by comparing measurements to published results
for the same particle system. Unfortunately, we lack good reliable INP
standards for proper comparison and calibration. Snomax is commonly used
(Wex et al., 2015) but we identified
serious issues stemming from the instability of the most ice-active ice
nucleants in Snomax over time (Polen et al., 2016). This precludes Snomax as
a reliable INP standard. Good comparisons have been found using illite NX
minerals, but it is critical to ensure that an identical particle sample is
used by each method
(Hiranuma et al., 2015).
Methods that collect aerosolized particles must take special care to account
for their collection efficiency versus size. Just placing some material from
the bulk sample into water can avoid these issues. The ice activity of
mineral particles can also change with time spent in water, or by attack
from strong acids. The very ice-active K-feldspar minerals are especially
subject to degradation in water due to surface ion etching, particularly for
those displaying hyperactive ice activity
(Banfield
and Eggleton, 1990; Holdren and Berner, 1979; Kumar et al., 2018; Peckhaus
et al., 2016). Harrison et al. (2016) found that a
particular and common type of feldspar that does not display hyper
ice activity, BCS 376, was able to maintain its IN activity over many months
in water. Engineered nanoparticles from inert metal oxides with reproducible
particles sizes, surface properties, and pore sizes may be the most reliable
type of INP standard, though this has not yet been evaluated and may be
restricted to a narrow freezing temperature range
(Alstadt et
al., 2017; Archuleta et al., 2005; Findenegg et al., 2008; Marcolli et al.,
2016). Until then illite NX mineral particles are likely the best INP
standard choice, provided all the above caveats are accounted for.
This study and the above series of recommendations are intended to shine
light on some potential sources of inconsistencies between droplet freezing
methods and create a simple, unified analysis and representation for all ice
nucleation community members to follow for future publications. Many
researchers already have much of the above information available before
publication and use that data for detailed analysis. In the interest of
moving the community forward, we seek increased transparency regarding the
aforementioned information by documenting important method details and the
raw spectra for background water freezing control in all publications using
droplet freezing methods.
The data used to produce each figure are available in the online supplement. A tab-delimited text file or set of files is
provided corresponding to each figure. The data files contain variables including temperature, frozen fraction, standard deviation,
ns (ice active surface site density), and other relevant parameters.
The supplement related to this article is available online at: https://doi.org/10.5194/amt-11-5315-2018-supplement.
MP and JS performed droplet freezing experiments and
analysis. TB designed the microfluidic chip and analysis program and
performed microfluidic device experiments. RS devised the project and
recommendations for future DFT analysis. MP and RS wrote the paper, with
input from all co-authors.
The authors declare that they have no conflict of interest.
Acknowledgements
We thank Tom Hill at Colorado State University for valuable suggestions and
providing the Anotop filters for our testing. Hassan Beydoun and Leif Jahn
provided comments on a draft of the paper. Comments provided by Gabor Vali, Benjamin Murray, and an anonymous referee during review significantly
improved this paper. This work was supported by the National Science
Foundation (CHE-1554941). Michael Polen was supported by a Graduate Research Fellowship
from the National Science Foundation.
Edited by: Mingjin Tang
Reviewed by: Benjamin Murray and one anonymous referee
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