ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-16-10927-2016Not all feldspars are equal: a survey of ice nucleating properties
across the feldspar group of mineralsHarrisonAlexander D.https://orcid.org/0000-0003-1522-0391WhaleThomas F.CarpenterMichael A.HoldenMark A.NeveLesleyO'SullivanDanielVergara TempradoJesushttps://orcid.org/0000-0002-3105-0946MurrayBenjamin J.https://orcid.org/0000-0002-8198-8131School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, UKDepartment of Earth Sciences, University of Cambridge, Downing
Street, Cambridge CB2 3EQ, UKBoth authors contributed equally to this work.T. F. Whale (t.f.whale@leeds.ac.uk), B. J. Murray (b.j.murray@leeds.ac.uk)5September20161617109271094012February201619February201622July201625July2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/16/10927/2016/acp-16-10927-2016.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/16/10927/2016/acp-16-10927-2016.pdf
Mineral dust particles from wind-blown soils are known to act as effective
ice nucleating particles in the atmosphere and are thought to play an
important role in the glaciation of mixed phase clouds. Recent work suggests
that feldspars are the most efficient nucleators of the minerals commonly
present in atmospheric mineral dust. However, the feldspar group of minerals
is complex, encompassing a range of chemical compositions and crystal
structures. To further investigate the ice-nucleating properties of the
feldspar group we measured the ice nucleation activities of 15 characterized
feldspar samples. We show that alkali feldspars, in particular the potassium
feldspars, generally nucleate ice more efficiently than feldspars in the
plagioclase series which contain significant amounts of calcium. We also
find that there is variability in ice nucleating ability within these
groups. While five out of six potassium-rich feldspars have a similar ice
nucleating ability, one potassium rich feldspar sample and one sodium-rich
feldspar sample were significantly more active. The hyper-active Na-feldspar
was found to lose activity with time suspended in water with a decrease in
mean freezing temperature of about 16 ∘C over 16 months; the mean
freezing temperature of the hyper-active K-feldspar decreased by
2 ∘C over 16 months, whereas the “standard” K-feldspar did not
change activity within the uncertainty of the experiment. These results, in
combination with a review of the available literature data, are consistent
with the previous findings that potassium feldspars are important components
of arid or fertile soil dusts for ice nucleation. However, we also show that
there is the possibility that some alkali feldspars may have enhanced ice
nucleating abilities, which could have implications for prediction of ice
nucleating particle concentrations in the atmosphere.
Introduction
Clouds containing supercooled liquid water play an important role in our
planet's climate and hydrological cycle, but the formation of ice in these
clouds remains poorly understood (Hoose and Möhler, 2012). Cloud
droplets can supercool to below -35 ∘C in the absence of particles
capable of nucleating ice (Riechers et al., 2013; Herbert et al., 2015),
hence clouds are sensitive to the presence of ice nucleating particles
(INPs). A variety of aerosol types have been identified as INPs (Murray et
al., 2012; Hoose and Möhler, 2012), but mineral dusts from deserts are
thought to be important INPs over much of the globe and in a variety of
cloud types (DeMott et al., 2003; Hoose et al., 2008,
2010; Niemand et al., 2012; Atkinson et al., 2013).
Atmospheric mineral dusts are composed of weathered mineral particles from
rocks and soils, and are predominantly emitted to the atmosphere in arid
regions such as the Sahara (Ginoux et al., 2012). The composition and
relative concentrations of dust varies spatially and temporally but it is
generally made up of only a handful of dominant minerals. The most common
components of dust reflect the composition of the continental crust and soil
cover, with clay minerals, feldspars and quartz being major constituents.
Until recently, major emphasis for research has been placed on the most
common minerals in transported atmospheric dusts, the clays. It has now been
shown that, when immersed in water, the feldspar component nucleates ice
much more efficiently than the other main minerals that make up typical
desert dust (Atkinson et al., 2013; Augustin-Bauditz et al., 2014; O'Sullivan
et al., 2014; Niedermeier et al., 2015; Zolles et al., 2015). This is an
important finding as it has been demonstrated that feldspar is a common
component of aerosolized mineral dusts (Glaccum and Prospero, 1980; Kandler
et al., 2009, 2011; Atkinson et al., 2013; Perlwitz et al.,
2015). Feldspar particles in the atmosphere tend to be larger than
clay particles and so will have shorter lifetimes in the atmosphere, however
aerosol modelling work has suggested that feldspar particles can account for
many observations of INP concentrations around the world (Atkinson et al.,
2013). Work conducted below water saturation using a continuous flow
diffusion chamber has also concluded that feldspars, particularly orthoclase
feldspars, nucleate ice at lower relative humidity in the deposition mode
than other common dust minerals (Yakobi-Hancock et al., 2013). While all
available evidence indicates that feldspars are very effective INPs, it must
also be recognized that feldspars are a group of minerals with differing
compositions and crystal structures. Therefore, in this study we examine
immersion mode ice nucleation by a range of feldspar samples under
conditions pertinent to mixed phase clouds.
An additional motivation is that determining the nature of nucleation sites
is of significant fundamental mechanistic interest and is likely to help
with further understanding of ice nucleation in the atmosphere (Vali,
2014; Freedman, 2015; Slater et al., 2015). By characterizing a range of
feldspars and associating them with differences in ice nucleation activity
it might be possible to build understanding of the ice nucleation sites on
feldspars. Some work has been conducted in this area already.
Augustin-Bauditz et al. (2014) concluded that microcline nucleates ice more
efficiently than orthoclase on the basis of ice nucleation results looking
at a microcline feldspar and several mixed dusts. Zolles et al. (2015)
recently found that a plagioclase and an albite feldspar nucleated ice less
well than a potassium feldspar and suggested that the difference in the ice
nucleation activity of these feldspars is related to the difference in ionic
radii of the cations and the local chemical configuration at the surface.
They suggested that only potassium feldspar will nucleate ice efficiently
because the K+ is kosmotropic (structure making) in the water hydration
shell while Ca2+ and Na+ are chaotropic (structure
breaking).
There has been much interest in the study of ice nucleation using molecular
dynamics simulations (e.g. Hu and Michaelides, 2007; Cox et al.,
2012; Reinhardt and Doye, 2014; Lupi and Molinero, 2014; Lupi et al.,
2014; Zielke et al., 2015; Cox et al., 2015a, b; Fitzner et al., 2015; Pedevilla et al., 2016). To date
there has been little overlap between work of this nature and laboratory
experiments. This has been due to difficulties in conducting experiments on
similar timescales and spatial extents between real-world and computational
systems. While these obstacles are likely to remain in place for some time,
the feldspars may offer the opportunity to address this deficit by providing
qualitative corroboration between computational and laboratory results. For
instance, it may be possible to study ice nucleation on different types of
feldspar computationally. If differences in nucleation rate observed also
occur in the laboratory greater weight may be placed on mechanisms
determined by such studies and so a mechanistic understanding of ice
nucleation may be built up.
In this paper we have surveyed 15 feldspar samples with varying composition
for their ice nucleating ability in the immersion mode. It will be shown
that feldspars rich in alkali metal cations tend to be much better at
nucleating ice than those rich in calcium. First, we introduce the feldspar
group of minerals.
The ternary composition diagram for the feldspars group
based on similar figures in the literature (Wittke and Sykes, 1990; Deer et
al., 1992).
Plagioclase feldspars used in this study.
SampleComposition*Source locationSource of composition/phase dataSpace groupPointgroupCrystal systemAnorthite glassAn100Synthetic sampleCarpenter (1991)–––ANC 68An100Synthetic sampleCarpenter (1991) describes similar feldsparsP1¯1¯triclinic148559An99.5Ab0.5University of Cambridgemineral collection–P1¯1¯triclinic21704aAn86Ab14Viakfontein, Bushveldcomplex, Transvaal (Harker collection no. 21704)Carpenter et al. (1985)P1¯–I1¯1¯triclinicSurt MAn64Ab36Surtsey (no. 7517, IcelandNatural History Museum)Phenocrysts from volcanicejectaCarpenter (1986)C1¯1¯triclinic67796bAn60Or1Ab39Gulela Hills, Tanzania (Harker collection no. 67796)Carpenter et al. (1985)Incommensurate order–triclinic97490An27Or1Ab71Head of Little Rock Creek, Mitchell co., N. Carolina (P. Gay, U.S.N.M. no. 97490)Carpenter et al. (1985)Incommensurate order–triclinic
* This refers to the chemical makeup of the feldspars. An stands for
anorthite, the calcium end-member, Ab stands for albite, the sodium
end-member and Or stands for orthoclase, the potassium end-member.
Alkali feldspars used in this study.
SampleDominant feldspar phaseSource locationSource of composition/ phase dataSpace groupPoint groupCrystal systemLD1 microclinemicroclineUniversity of Leeds rock collectionXRDC1¯1¯triclinicLD2 sanidinesanidineUniversity of Leeds rock collectionXRDC2/m2/mmonoclinicLD3 microclinemicroclineUniversity of Leeds rock collectionXRDC1¯1¯triclinicBCS 376 microclinemicroclineBureau of Analysed Samples LtdReference sample/XRD; Atkinson et al. (2013)C1¯1¯triclinicAmelia Albite (un-ground)albiteAmelia Courthouse, Amelia Co.,Virginia (Harker mineral collection)Carpenter et al. (1985)C1¯1¯triclinicAmelia Albite groundalbiteAmelia Courthouse, Amelia Co., Virginia (Harker mineral collection)Carpenter et al. (1985)C1¯1¯triclinicTUD#1 microclinemicroclineMinas Gerais, BrazilXRDC1¯1¯triclinicTUD#2 albitealbite*NorwayXRDC1¯1¯triclinicTUD#3 microclinemicroclineMt. Maloso, MalawiXRDC1¯1¯triclinicBCS 375 albitealbiteBureau of Analysed Samples LtdReference sample/XRD Atkinson et al. (2013)C1¯1¯triclinic
* We note that the XRD pattern was also consistent with oligoclase, which is
close to albite in composition. The identification of albite is consistent
with that of Alexei Kiselev (personal communication, 2016).
The feldspar group of minerals
The feldspars are tectosilicates (also called framework silicates) with a
general formula of XAl(Si,Al)Si2O8, where X is usually potassium,
sodium or calcium (Deer et al., 1992). Unlike clays, which are
phyllosilicates (or sheet silicates), tectosilicates are made up of three-dimensional frameworks of silica tetrahedra. Substitution of Si with Al in
the structure is charge balanced by cation addition or replacement within
the cavities in the framework. This leads to a large variability of
composition in the feldspars and means that most feldspars in rocks have
compositions between end-members of sodium-, calcium- or potassium-feldspars
(Deer et al., 1992; Wenk and Bulakh, 2004). A ternary representation of
feldspar compositions is shown in Fig. 1. All feldspars have very similar
crystal structures, but the presence of different ions and degrees of
disorder related to the conditions under which they crystallized from the
melt (lava or magma) yields subtle differences which can result in differing
symmetry.
There are three polymorphs (minerals with the same composition, but
different crystal structure) of the potassium end-member, which are
microcline, orthoclase and sanidine. The polymorphs become more disordered
in terms of Al placement in the tetrahedra from microcline to sanidine,
respectively. The structures of feldspars which form from a melt vary
according to their cooling rate. If cooling is fast (volcanic), sanidine is
preserved. If cooling is slow, in some granites for example, microcline may
be formed. Feldspars formed in metamorphic rocks have high degrees of Al/Si
order. The sodium end-member of the feldspars is albite and the calcium
end-member is anorthite. Feldspars with compositions between sodium and
calcium form a solid solution and are collectively termed the plagioclase
feldspars with specific names for different composition ranges. Feldspars
between sodium and potassium end-members are collectively termed the alkali
feldspars and can be structurally complex. A solid solution series exists
between high albite and sanidine (“high” refers to high temperature
character which is preserved on fast cooling), but not between low albite
and microcline (“low” refers to low temperature character which is
indicative of slow cooling rates). In contrast to the series between sodium
and calcium, and sodium and potassium, there are no feldspars between
calcium and potassium end-members because calcium and potassium ions do not
actively substitute for one another within the framework lattice due to their
difference in size and ionic charge (Deer et al., 1992; Wenk and Bulakh,
2004).
There is limited information about the composition of airborne atmospheric
mineral dusts (Glaccum and Prospero, 1980; Kandler et al., 2007, 2009),
where mineralogy is reported the breakdown of the feldspar
family has only been done in a limited way. Atkinson et al. (2013) compiled
the available measurements and grouped them into K-feldspars and plagioclase
feldspars (see the Supplement Table 1 in Atkinson et al., 2013). This
compilation indicates that the feldspar type is highly variable in
atmospheric dusts, with K-feldspars ranging from 1 to 25 % by mass (with a
mean of 5 %) and plagioclase feldspars ranging from 1 to 14 % (with a
mean of 7 %). The feldspar component of airborne dusts is highly variable
and the nucleating ability of the various components needs to be
investigated.
In order to aid the discussion and representation of the data we have
grouped the feldspars into three groups: the plagioclase feldspars (not
including albite), albite (the sodium rich corner of the ternary diagram)
and potassium (K-) feldspars (microcline, sanidine and orthoclase). The
K-feldspars contain varying amounts of sodium, but their naming is
determined by their crystal structure. We also collectively refer to albite
and potassium feldspars as alkali feldspars.
Samples and sample preparation
A total of 15 feldspars were sourced for this study. Details of the
plagioclase feldspars tested are in Table 1 and details of the alkali
feldspars are in Table 2. We have made use of a series of characterized
plagioclase feldspars which were assembled for previous studies (Carpenter
et al., 1985; Carpenter, 1986, 1991). The other samples were
sourced from a range of repositories, detailed in Tables 1 and 2. The naming
convention we have used in this paper is to state the identifier of the
specific sample followed by the mineral name. For example, BCS 376
microcline is a microcline sample from the Bureau of Analysed Samples with
sample code 376. In other cases, such as Amelia Albite, the sample is from a
traceable source and is commonly referred to with this name and when a code
is used, such as 97490 plagioclase, the code links to the cited
publications.
Anorthite glass and synthetic anorthite ANC 68 were tested for ice
nucleating efficiency to investigate the impact of crystal structure in
feldspar. The anorthite glass was produced by Carpenter (1991) by melting
natural calcite with reagent grade SiO2 and Al2O3 at
1680 ∘C for 3 h. The melt was then stirred before air cooling.
The resulting glass was then annealed at 800 ∘C to relieve
internal stresses. The composition of the resulting glass was shown to be
stoichiometric CaAl2Si2O8. Synthetic anorthite ANC 68 was
produced by heating a sample of this glass to 1400 ∘C for 170 h.
As these two samples are chemically identical, differing only in that
one is amorphous and the other crystalline, comparison of the ice nucleating
efficiency of the two samples has the potential to reveal information about
the impact of feldspar crystal structure on ice nucleating efficiency.
Feldspars 148559, 21704a, 67796b and 97490 plagioclase and Amelia albite are
natural samples that form a solid solution series covering the plagioclase
series from nearly pure anorthite to nearly pure albite as seen in Table 1.
The alkali feldspars used here have not previously been characterized.
Rietveld refinement of powder XRD patterns was carried out using TOtal
Pattern Analysis Solutions (TOPAS) to determine the phase of the feldspar
present. The results of this process are presented in Table 2. The surface
areas of all the feldspars were measured by Brunauer-Emmett-Teller (BET)
nitrogen gas adsorption (see Sect. 4). All samples, unless otherwise stated,
were ground to reduce the particle size and increase the specific surface
area using a mortar and pestle which were scrubbed with pure quartz then
cleaned with deionized water and methanol before use. Grinding of most
samples was necessary in order to make the particles small enough for our
experiments. Amelia albite was the only material tested both in an unground
state (or at least not a freshly ground state) and a freshly ground state.
Suspensions of known concentration were made up gravimetrically using Milli-Q
water (18.2 MΩ cm). Except where stated otherwise the suspensions
were then mixed for a few minutes using magnetic stirrers prior to use in ice
nucleation experiments.
The activity of Amelia albite and TUD #3 microcline decrease during repeat
experiments conducted approximately 30 min after initial experiments. Based
on this observation three samples, the BCS 376 microcline, ground Amelia
albite and TUD #3 microcline, were tested for changes in ice nucleating
efficiency with time, when left in suspension at room temperature. BCS 376
microcline was chosen as it has been previously studied and the activity
decrease was not seen on the timescale of ∼ 30 min previously. Ice
nucleation efficiency was quantified at intervals over 11 days. Between
experiments the suspensions were left at room temperature without stirring
and then stirred to re-suspend the particulates for the ice nucleation
experiments. Suspensions of the three dusts were also tested 16 months after
initial experiments were performed to determine the long-term impact of
contact with water on ice nucleation efficiency.
Experimental method and data analysis
In order to quantify the efficiency with which a range of feldspar dusts
nucleate ice we made use of the microlitre Nucleation by Immersed Particle
Instrument (µl-NIPI). This system has been used to make numerous ice
nucleation measurements in the past and has been described in detail by Whale
et al. (2015). Briefly, 1 ± 0.025 µl droplets of an aqueous
suspension, containing a known mass concentration of feldspar particles are
pipetted onto a hydrophobic coated glass slide. This slide is placed on a
temperature-controlled stage and cooled from room temperature at a rate of
5 ∘C min-1 to 0 ∘C and then at
1 ∘C min-1 until all droplets are frozen. Dry nitrogen is
flowed over the droplets at 0.2 l min-1 to prevent frozen droplets
from affecting neighbouring liquid droplets; droplets evaporate slowly during
experiments, however this has been shown to have no detectable effect on
freezing temperatures (Whale et al., 2015). Whale et al. (2015) demonstrated
that a dry nitrogen flow prevents condensation and frost accumulating on the
glass slide so ice from a frozen droplet cannot trigger freezing in
neighbouring droplets. Freezing is observed with a digital camera, allowing
determination of the fraction of droplets frozen as a function of
temperature. Multiple experiments have been combined to produce single sets
of data for each mineral. Suspensions of the feldspars were made up
gravimetrically and specific surface areas of the samples were measured using
the Brunauer–Emmett–Teller (BET) N2 adsorption method using a
Micromeritics TriStar 3000. Here the µl-NIPI technique is used
for immersion mode nucleation experiments.
To allow comparison of the ability of different materials to nucleate ice,
the number of active sites is normalized to the surface area available for
nucleation. This yields the ice nucleation active site density,
ns(T). ns(T) is the number of ice nucleating
sites that become active per surface area on cooling from 0 ∘C to
temperature Tand can be calculated using (Connolly et al., 2009):
n(T)N=1-exp(-ns(T)A),
where n(T) is the number of droplets frozen at temperature T, N is the
total number of droplets in the experiment and A is the surface area of
nucleator per droplet.
Active sites may be related to imperfections in a crystal structure, such as
cracks or defects, or may be related to the presence of quantities of other
more active materials located in specific locations at a surface. While the
fundamental nature of active sites is not clear, and may be different for
different materials, ns is a pragmatic parameter which allows us
to empirically define the ice nucleating efficiency of a range of materials
(Vali, 2014).
This description is site specific and does not include time dependence. The
role of time dependence in ice nucleation has recently been extensively
discussed (Vali, 2008, 2014; Vali et al., 2014, 2015; Herbert et al., 2014; Wright
et al., 2013). For feldspar (at least for BCS 376 microcline) it is thought
that the time dependence of nucleation is relatively weak and that the
particle to particle, or active site to active site, variability is much more
important (Herbert et al., 2014). The implication of this is that specific
sites on the surface of most nucleators, including feldspars, nucleate ice
more efficiently than the majority of the surface. As this study is aimed at
comparing and assessing the relative ice nucleating abilities of different
feldspars we have not determined the time dependence of observed ice
nucleation in this work, although this would be an interesting topic for
future study.
By assuming that the BET surface area of the feldspar powders is made up of
monodisperse particles it can be estimated that droplets containing
1 wt % of feldspar will each contain around 106 particles. While
there will be a distribution of particle sizes we assume that there are
enough particles per droplet that the uncertainty in surface area per droplet
due to the distribution of particles through the droplets is negligible. In
contrast, it has been suggested that ice nucleation data could be explained
by variability of nucleator surface area through the droplet population
(Alpert and Knopf, 2016). Our assumption that each droplet contains a
representative surface area is supported by our previous work where we show
that ns derived from experiments with a range of feldspar
concentrations are consistent with one another (Whale et al., 2015; Atkinson
et al., 2013). If the particles were distributed through the droplets in such
a way that some droplet contained a much larger surface area of feldspar than
others we would expect the slope of ns with temperature to be
artificially shallow. The slope would be artificially shallow because
droplets containing more than the average feldspar surface area would tend to
freeze at higher temperatures and vice versa. However, the fact that
ns data for droplets made from suspensions made up with a wide
range of different feldspar concentrations all line up shows that the droplet
to droplet variability in feldspar surface area is minor (Atkinson et al.,
2013; Whale et al., 2015). Hence, the droplet to droplet variability in
feldspar surface area is neglected and the uncertainty in surface area per
droplet in these experiments is estimated from the uncertainties in weighing,
pipetting and specific surface area of the feldspars. Indeed, Murray et
al. (2011) found that even with picolitre droplets containing 10's of
particles per droplet median nucleation temperatures scaled well with surface
area per droplet calculated in the way used in this work.
Droplet fraction frozen as a function of temperature for 1 wt %
suspensions of ground powders of various feldspar samples. The K-feldspars
are coloured red, the plagioclase feldspars are coloured blue, the albites
are coloured green and the feldspar glass is coloured black. A fit to the
background freezing of pure MilliQ water in the µl-NIPI instrument
used by Umo et al. (2015) is also included. The shaded area around this fit
shows 95 % confidence intervals for the fit. It can be seen that all the
feldspar samples tested nucleate ice more efficiently than the background
freezing of the instrument.
In order to estimate the uncertainty in ns(T) due to the
randomness of the distribution of the active sites in droplet freezing
experiments, we conducted Monte Carlo simulations. Wright and Petters (2013)
previously adopted a similar approach to simulate the distribution of active
sites in droplet freezing experiments. In these simulations, we generate a
list of possible values for the number of active sites per droplet
(μ). The theoretical relationship between the fraction of
droplets frozen and μ can be derived from the Poisson
distribution:
n(T)N=1-exp(-μ).
The simulation works in the following manner. First, we take a value of μ
and we simulate a corresponding random distribution of active sites through
the droplet population for an experiment. Every droplet containing one or
more active sites is then considered to be frozen. In this way, we can obtain
a simulated value of the fraction frozen for a certain value of μ.
Repeating this process many times and for all the possible values of μ,
we obtain a distribution of possible values of μ that can explain each
value of the observed fraction frozen. This resulting distribution is neither
Gaussian nor symmetric, so in order to propagate the uncertainty to
nsT values, we take the following steps. First,
we generate random values of μ following the corresponding previously
simulated distribution for each value of the fraction frozen. Then, we
simulate random values of A following a Gaussian distribution centred on
the value derived from the specific surface area per droplet with the
standard deviation derived from the uncertainty in droplet volume and
specific surface area. We assume that each droplet contains a representative
surface area distribution as discussed above. This process results in two
distributions, one for A and one for μ, with these distributions we can
calculate the resultant distribution of ns(T) values, and from
that distribution we obtain the 95 % confidence interval.
Results and discussionIce nucleation efficiencies of plagioclase and alkali feldspars
Droplet fraction frozen from µl-NIPI for the 15 feldspar samples
are shown in Fig. 2. The values of ns(T) derived from these
experiments are shown in Fig. 3 along with the ns(T)
parameterization from Atkinson et al. (2013) for BCS 376 microcline. The
various groups of feldspars are indicated by colour which corresponds to the
regions of the phase diagram in Fig. 1. We define potassium (K-) feldspars
(red) as those rich in K including microcline, orthoclase and sanidine; the
Na end-member is albite (green); and plagioclase series feldspars (blue) are
a solid solution between albite and the calcium end-member, anorthite.
Ice nucleation efficiency expressed as ns (T) for the
various feldspars tested in this study. The symbols are the same as those
used for Fig. 2. The K-feldspars are coloured red, the plagioclase feldspars
are coloured blue, the albites are coloured green and the feldspar glass is
coloured black. Except for Amelia albite and TUD#1 microcline all samples
were tested twice and the data from the two runs combined. Sample information
can be found in Tables 1 and 2. Temperature uncertainty
is ± 0.4 ∘C. Y-Error bars calculated using the Poisson Monte
Carlo procedure described in Sect. 4. Data points with large uncertainties
greater than an order of magnitude have been removed, these are invariably
the first one or two freezing events of a given experiment. For clarity error
bars have only been included on a selection of data sets (TUD#3
microcline, LD1 microcline, BCS 375 albite and 67796b plagioclase). The error
bars shown are typical.
Out of the six K-feldspars studied, five fall on or near the line defined by
Atkinson et al. (2013). These include three microcline samples and one
sanidine sample, which have different crystal structures. Sanidine has
disordered Al atoms, microcline has ordered Al atoms and orthoclase has
intermediate order; these differences result in differences in symmetry and
hence space group (see Tables 1 and 2). The freezing results indicate that
Al disordering does not play an important role in nucleation for the
analysed weight concentration range. However, one K-feldspar sample,
TUD#3 microcline, was substantially more active. This indicates that
crystal structure and composition are not the only factors dictating the ice
nucleating ability of K-feldspars.
All plagioclase feldspars tested were less active ice nucleators than the
K-feldspars which were tested. There was relatively little variation in the
ice nucleation activities of the plagioclase solid solution series
characterized by Carpenter (1986) and Carpenter et al. (1985). For instance,
of those feldspars that possess the plagioclase structure, greater sodium
content does not systematically increase effectiveness of ice nucleation.
Overall, the results for plagioclase feldspars indicate that they have an
ice nucleating ability much smaller than that of the K-feldspars.
It is also interesting to note that the ANC 68 synthetic anorthite had
different nucleating properties to the anorthite glass from which it was
crystallized (and had the same composition). The ANC 68 synthetic anorthite
sample has a much more shallow ns(T) curve than the glass. This is
noteworthy, because the composition of these two materials is identical, but
the phase of the material is different. It demonstrates that crystallinity
is not required to cause nucleation, but the presence of crystallinity can
provide rare active sites which can trigger nucleation at much higher
temperatures. In a future study it would be interesting to attempt to probe
the nature of these active sites.
We tested three predominantly Na-feldspars (albites). Amelia albite was
found to be highly active, approaching that of TUD#3 microcline. The
others, BCS 375 albite, and TUD#2 albite were less active, intermediate
between the K-feldspars and plagioclase feldspars.
To ensure that the high activity of Amelia albite and microcline TUD#3
was not caused by contamination from biological INPs the samples were heated
to 100 ∘C in Milli-Q water for 15 min. This treatment will
disrupt any protein-based nucleators present (O'Sullivan et al.,
2015). No significant reduction in freezing temperatures (beyond what would
be expected from the activity decay described in Sect. 5.2) was observed
suggesting that the highly active INPs present are associated with the
feldspars rather than biological protein contamination. Certain biological
nucleators have been observed to retain their ice nucleating activity
despite heat treatment of this type (Pummer et al., 2012; O'Sullivan et al.,
2014; Tobo et al., 2014) however, to the best of our knowledge, no biological
species has been observed to nucleate ice at such warm temperatures after
heat treatment. This behaviour does not seem consistent with biological
nucleators, unless the biological entity is within the Amelia albite
particles and is somehow dispersed through the particle population during
grinding. While we cannot exclude the possibility that some unknown
biological species is present on microcline TUD#3 and Amelia albite it
seems more likely that the minerals themselves are responsible for the
observed ice nucleation activity. Additionally, it is known that certain
organic molecules can nucleate ice efficiently (Fukuta, 1966). It is not
possible to exclude the possibility of the presence of these or other,
unknown, heat resistant contaminants that nucleate ice very efficiently.
It has been noted by Vali (2014) that there is an indication that nucleators
which are more active at higher temperatures tend to have steeper slopes of
ln J (nucleation rate). We have observed this trend here in the data shown
in Fig. 3 (ns(T) is proportional to J for a single component).
The slopes of experiments where freezing occurred at lower temperatures
(plagioclases) generally being flatter than those where freezing took place
at higher temperatures (alkali feldspars). Vali (2014) suggests that this
maybe the result of different observational methods. In this study we have
used a single method for all experiments so the trend is unlikely to be due
to an instrument artefact. The implication is that active sites with lower
activity tend to be more diverse in nature. This may indicate that there are
fewer possible ways to compose an active site that is efficient at
nucleating ice and that there will be less variation in these sites as a
result. The active sites of lower activity may take a greater range of forms
and so encompass a greater diversity of freezing temperatures. The lower
diversity in the sites active at higher temperatures may explain the steep
slopes in ns seen, however it should be noted that classical nucleation
theory also predicts steeper slopes at higher temperatures assuming a single
contact angle.
(a) The dependence of ns on time spent in water
for three feldspar samples. The time periods indicate how long samples were
left in contact with water. Fresh samples were tested minutes after
preparation of suspensions. Note that ice nucleation temperatures of BCS 376
are almost the same after 16 months in water while those of Amelia albite
decreases by around 16 ∘C. TUD #3 microcline loses activity
quickly in the first couple of days of exposure to water but total decrease
in nucleation temperatures after 16 months is only around 2 ∘C.
(b) Median freezing temperature against time left in suspension for
BCS 376 microcline, TUD#3 microcline and Amelia albite. For clarity, not
all curves represented by points in panel (b) are included in panel
(a).
To summarize, plagioclase feldspars tend to have relatively poor ice
nucleating abilities, all K-feldspars we tested are relatively good at
nucleating ice and the albites are variable in their nucleating activity.
Out of the six K-feldspars tested, five have very similar activities and are
well approximated by the parameterization of Atkinson et al. (2013) in the
temperature-ns regime we investigated here. However, we have identified
two alkali feldspar samples, one K-feldspar and one albite, which are much
more active than the others indicating that a factor or factors other than
the polymorph or composition determines the efficiency of alkali feldspars
as ice nucleators.
The stability of active sites
It was observed that the ice nucleation activity of ground Amelia albite and
ground TUD #3 microcline declined over the course of ∼ 30 min.
Only the initial run is shown in Fig. 3 where the feldspar had
spent only about 10 min in suspension. This decay in activity over the
course of ∼ 30 min was not seen in the other feldspars. To
investigate this effect samples of BCS 376 microcline, Amelia albite and TUD
#3 microcline were left in water within a sealed vial and tested at
intervals over the course of 16 months, with a focus on the first 11 days.
TUD #3 microcline and Amelia albite were chosen for this
experiment as they contained highly active sites, represented two different
types of feldspar and were the only feldspars observed to exhibit this rapid
decay in activity. BCS 376 microcline was also included in this activity
decay experiment as it had provided consistent data over repeated runs and
served as a standard in the Atkinson et al. (2013) paper which could
therefore be tested. The results of these experiments are shown in Fig. 4.
The median freezing temperature of the Amelia albite sample was most
sensitive to time spent in water, decreasing by 8 ∘C in 11 days and by
16 ∘C in 16 months. The TUD#3 microcline sample decreased by about
2 ∘C in 16 months, but the freezing temperatures of the BCS 376 did not
change significantly over 16 months (within the temperature uncertainty of
± 0.4 ∘C). Clearly, the stability of the active sites
responsible for ice nucleation in these samples is highly variable.
Amelia albite is a particularly interesting case, where the highly active
sites are also highly unstable. For Amelia albite we observed that the ice
nucleation ability of the powder directly as supplied (the sample had been
ground many years prior to experiments) was much lower than the freshly
ground sample. The ns values for the “as-supplied” Amelia albite are
shown in Fig. 4. This suggests that the active sites on Amelia albite are
unstable and in general are sensitive to the history of the sample. We note
that from previous work that BCS 376 feldspar ground to varied extents
nucleates ice similarly (Whale et al., 2015) and we have not observed a
decay of active sites of the BCS 376 microcline sample when stored in a dry
vial over the course of 2 years. It is also worth noting that freshly
ground BCS 376 microcline did not nucleate ice as efficiently as Amelia
albite or TUD#3 microcline. These results indicate that BCS 376
microcline contains very active sites, but that these sites are much more
stable than those found in Amelia albite. This result is in agreement with
the observation that albite weathers faster than microcline in soils as
Na+ is more readily substituted for hydrogen than K+ (Busenberg
and Clemency, 1976; Blum, 1994).
Zolles at al. (2015) have suggested that grinding can lead to active sites
being revealed, or the enhancement of existing active sites. It was shown in
Whale et al. (2015) that differently ground samples of BCS 376 microcline
nucleate ice similarly. In contrast Hiranuma et al. (2014) show that ground
hematite nucleates ice more efficiently (normalized to surface area) than
cubic hematite. The evidence suggests that the ice nucleating efficiencies
of different materials respond differently to grinding processes. Indeed, it
is evident from this study that highly active sites in Amelia albite are
generated by grinding but lose activity when exposed to liquid water, and
probably lose activity during exposure to (presumably wet) air, returning to
an activity level comparable to that of the plagioclase feldspars. TUD#3
microcline also possesses a highly active site type sensitive to water
exposure but falls back to a level of activity higher than the other
K-feldspars we have tested. This second, less active site type is shown to
be stable in water over the course of 16 months. TUD#3 must possess
populations of both more active, unstable sites and less active (although
still relatively active compared to the sites on other K-feldspars) stable
sites. Amelia albite possesses only unstable sites and much less active
sites similar to those found on the plagioclase feldspars we have tested.
These results indicate something of the nature of the active sites on
feldspars. Throughout this paper we refer to nucleation occurring on active
sites, or specific sites, on the surface of feldspar. It is thought that
nucleation by most ice active minerals is consistent with nucleation on
active sites with a broad spectrum of activities (Marcolli et al.,
2007; Lüönd et al., 2010; Niedermeier et al., 2010; Augustin-Bauditz et
al., 2014; Herbert et al., 2014; Vali, 2014; Wex et al., 2014; Wheeler et al.,
2015; Hiranuma et al., 2015; Niedermeier et al., 2015; Hartmann et al., 2016).
However, the nature of these active sites is not known. It is postulated
that active sites are related to defects in the structure and therefore that
each site has a characteristic nucleation ability, producing a spectrum of
active sites. Defects are inherently less stable than the bulk of the
crystal and we might expect these sites to be affected by dissolution
processes, or otherwise altered, in preference to the bulk of the crystal
(Parsons et al., 2015). The fact that we observe ice nucleation by
populations of active sites with different stabilities in water implies that
these sites have different physical or chemical characteristics.
Furthermore, the fact that some populations of active sites are sensitive to
exposure to water suggests that the history of particles can be critical in
determining the ice nucleating ability of mineral dusts. This raises the
question of whether differences in ice nucleation efficiency observed by
different instruments (Emersic et al., 2015; Hiranuma et al., 2015), could be
related to the different conditions particles experience prior to
nucleation.
Comparison of literature data from Atkinson et al. (2013), Emersic
et al. (2015), Niedermeier et al. (2015) and Zolles et al. (2015) with data
from this study. Feldspars are coloured according to their composition, as
in Fig. 3. 0.1 wt % data for Amelia albite and LD1 microcline, which is
not shown in Fig. 3, has been included. Where samples are known to lose
activity with time the most active runs have been shown. Note that data from
Niedermeier et al. (2015) include some data from Augustin-Bauditz et al. (2014).
Comparison to literature data
We have compared the nsT values for various
feldspars from a range of literature sources with data from this study in Fig. 5.
Inspection of this plot confirms that K-feldspars nucleate ice
more efficiently than the plagioclase feldspars. Also, with the exception of
the hyper-active Amelia albite sample, the K-feldspars are more active than
the albites.
Results for BCS 376 microcline have been reported in several papers
(Atkinson et al., 2013; O'Sullivan et al., 2014; Whale et al., 2015; Emersic et
al., 2015). There is a discrepancy between the cloud chamber data from
Emersic et al. (2015) and the picolitre droplet cold stage experiments at
around -18 ∘C, whereas the data at about -25 ∘C are in
agreement. Emersic et al. (2015) attribute this discrepancy to aggregation
of feldspar particles in microlitre scale droplet freezing experiments
reducing the surface area of feldspar exposed to water leading to a lower
ns(T) value. It is unlikely that this effect can account for
the discrepancy because in the temperature range of the Emersic et al. (2015)
data the comparison is being made to results from picolitre droplet
freezing experiments which Emersic et al. (2015) argue should not be
affected by aggregation because there are not enough particles present in
each droplet to result in significant aggregation. Atkinson et al. (2013)
estimated that on average even the largest droplets only contained a few 10 s
of particles. We also note that our microscope images of droplets show many
individual particles moving independently around in the picolitre droplets
in those experiments, indicating that the feldspar grains do not aggregate
substantially. Hence, the discrepancy between the data of Emersic et al. (2015)
and Atkinson et al. (2013) at around -18 ∘C cannot be accounted
for by aggregation. Furthermore, Atkinson et al. (2013) report that the
surface area determined from the laser diffraction size distribution of BCS 376
microcline in suspension is 3.5 times smaller than that derived by the
gas adsorption measurements (see Supplementary Fig. 5 in Atkinson et al. (2013)
and the corresponding discussion). This difference in surface area
can be accounted for by the fact that feldspar grains are not smooth
spheres, as assumed in the analysis of the laser diffraction data. Feldspar
grains are well-known to be rough and aspherical (Hodson et al., 1997).
Atkinson et al. (2013) also note that the laser diffraction technique lacks
sensitivity to the smallest particles in the distribution which will also
lead to an underestimate in surface area. Nevertheless, the data presented
by Atkinson et al. (2013) suggest that aggregation of feldspar particles
leading to reduced surface area is at most a minor effect. As such the
discrepancy between different instruments remains unexplained and more work
is needed on this topic.
Ice nucleation by single size-selected particles of TUD#1 microcline has
been investigated by Niedermeier et al. (2015) at temperatures below
-23 ∘C. We found that TUD#1 microcline was in good agreement
with the K-feldspar parameterization from Atkinson et al. (2013) between
about -6 and -11 ∘C. Between -23 and -25 ∘C, the
ns(T) values produced by Niedermeier et al. (2015) are similar
(lower by a factor of roughly four) to that of the Atkinson et al. (2013)
parameterization, despite the different sample types. Niedermeier et
al. (2015) used the Leipzig Aerosol Cloud Interaction Simulator (LACIS), in
which they size selected particles, activated them to cloud droplets and then
quantified the probability of freezing at a particular temperature. It is
interesting that the Niedermeier et al. (2015) ns(T) values
curve off at lower temperatures to a limiting value which they term
ns∗, indicating that nucleation by K-feldspars may hit a
maximum value and emphasizes why we need to be cautious in extrapolating
ns(T) parameterizations beyond the range of experimental data.
The data for a microcline, a plagioclase (andesine) and albite from Zolles et
al. (2015) are consistent with our finding that plagioclase feldspars are less
effective nucleators than K-feldspars. It is also consistent with Atkinson et
al. (2013) who found that albite is less efficient at nucleating ice than
microcline. However, the data for K-feldspar from Zolles et al. (2015) sit
below the line from Atkinson et al. (2013) for BCS 376 microcline and are
lower than the points from Niedermeier et al. (2015) for TUD#1 microcline.
Their measurements involved making up suspensions (2-5wt %) and then
creating a water-in-oil emulsion where droplets were between
10 and 40 µm. They quote their particle sizes as being between
1 and 10 µm for the feldspars. Atkinson et al. (2013) worked with
0.8 wt % suspensions, with droplets of 9 to 19 µm where the mode
particle size was ∼ 700 nm. Hence, Zolles et al. (2015) worked with
more concentrated suspensions and larger particles than used by Atkinson et
al. (2013). In principle, ns should be independent of droplet
volume and particle concentration, but differences between instruments and
methods have been reported (Hiranuma et al., 2015). Additionally, Zolles et
al. (2015) estimated the surface area of their feldspar particles using a
combination of SEM images and the BET surface area of quartz. This leads to
an unspecified uncertainty in their ns values. However, it is not
possible to determine whether the observed difference in ns is due
to differences in the sample or the techniques used, but may mean that
certain K-feldspars nucleate ice less well than those defined by the Atkinson
et al. (2013) line in this temperature regime. This would be a very
interesting result as it may provide a point of difference that could provide
insight into why K-feldspars nucleate ice efficiently. There has been
relatively little work on what makes feldspar a good nucleator of ice. Zolles
et al. (2015) suggest that only K-feldspars will nucleate ice well on the
basis that Ca2+ and Na+ are chaotropic (structure breaking in
water) while K+ is kosmotropic (structure making in water). We have only
observed one feldspar that contains little K+ but nucleates ice
relatively efficiently, Amelia albite. This feldspar loses its activity
quickly in water and eventually becomes more comparable to the plagioclase
feldspars. It may be that the strong nucleation observed is associated with
the small amount of K+ it contains and that once this dissolves away the
feldspar behaves like a plagioclase.
Augustin-Bauditz et al. (2014) tentatively concluded that microcline may
nucleate ice more efficiently than orthoclase at ns(T) values above about
106 cm-2 and at temperatures below -23 ∘C, the conditions
in which they performed their measurements. They arrived at this conclusion by
noting that NX-illite and Arizona test dust both contain orthoclase (8 and
20 %, respectively), but the ns(T) values they report for these
materials are more than one order less than microcline.
Within the surface area regime examined in this study we have observed some
variability amongst the K-feldspars (see Fig. 2), but no difference
between sanidine and four out of five microclines which fall around the line
defined by Atkinson et al. (2013). As discussed above, the Al in sanidine is
the least ordered, with microcline the most ordered and orthoclase at an
intermediate order, hence we observe no clear dependency on the ordering of
Al in K-feldspars. Further investigations of the ice nucleating ability of
the various K-feldspar phases at low temperature would be valuable. We could
not do this in the present study with the samples used here because we did
not have sufficient quantities of the samples.
Conclusions
In this study we have analysed the ice nucleating ability of 15 characterized
feldspar samples. These minerals include plagioclase feldspars (in the solid
solution series between Ca and Na end-members), the K-feldspars (sanidine and
microcline) and albite (the Na end-member). The results indicate that the
alkali feldspars, including albite and K-feldspars, tend to nucleate ice more
efficiently than plagioclase feldspars. The plagioclase feldspars nucleate
ice at the lowest temperatures with no obvious dependence on the Ca / Na ratio.
The albites have a wide variety of nucleating abilities, with one sample
nucleating ice much more efficiently than the microcline sample Atkinson et
al. (2013) studied. This hyper-active albite lost its activity over time
while suspended in water. Five out of six of the K-feldspar samples we
studied nucleated ice with a similar efficiency to the BCS 376 microcline
studied by Atkinson et al. (2013). A single K-feldspar we studied had a very
high activity, nucleating ice as warm as -2 ∘C in our microlitre
droplet assay. The striking activity of this hyperactive microcline decayed
with time spent in water, but not to the same extent as the hyperactive
albite sample. While the hyperactive sites are sensitive, to varying degrees,
to time spent in water, the activity of the BCS 376 microcline sample used by
Atkinson et al. (2013) did not change significantly. We have
not excluded the possibility that other entities on the surfaces of the
feldspar may be responsible for the ice nucleation observed.
In light of these findings, we suggest that there are at least three classes
of active site present in the feldspars studied here: (i) sites of
relatively low activity associated with plagioclase feldspars; (ii) sites
which are more active associated with K-feldspars that are stable in
water over the course of many months; (iii) hyper-active sites
associated with one albite and one K-feldspar that we studied that loses
activity when exposed to water. It is possible that the sites of type i are
present on the typical K-feldspars, but we do not observe them because ice
nucleates on more active sites. Whether these different sites are all related
to similar features on the surfaces or if they are each related to different
types of features is not known. Nevertheless, it appears that feldspars are
characterized by a range of active site types with varying stability and
activity.
The specific details of these active sites continue to elude us, although it
appears that they are only present in alkali feldspars and in particular, the
K-feldspars. Unlike the plagioclase feldspars which form a solid solution,
the Na and K feldspars in alkali feldspars are often exsolved, possessing
intergrowths of the Na and K feldspars referred to as microtexture (Parsons
et al., 2015). It is possible that the boundaries between the two phases in
the intergrowth provide sites for nucleation that are not present in
plagioclase feldspars. If the high energy defects along exsolution boundaries
are responsible for higher ice nucleation activity of K-feldspars then this
may offer an insight into acid passivation of ice nucleating ability observed
in laboratory studies (Wex et al., 2014; Augustin-Bauditz et al., 2014).
Berner and Holdren (1979) suggest that the acid mediated weathering of
feldspar occurs in multiple stages and suggest dissolution of feldspars is
concentrated at high surface energy sites such as dislocations and crystal
defects, sites which may be related to ice nucleation. More work is needed to
explore the significance of exsolution, microtexture and the impact of
weathering on feldspars with respect to ice nucleation activity.
In a previous study Atkinson et al. (2013) used an ns(T)
parameterization of a single K-feldspar (BCS 376 microcline) to approximate
the ice nucleating properties of desert dust in a global aerosol model. Given
that five out of six of the K-feldspars we studied here have very similar ice
nucleating abilities, this approximation seems reasonable. However, we have
identified two hyper-active feldspars and do not know how representative
these samples are of natural feldspars in dust emission regions. We also note
that the active sites on these feldspars are less stable than those of
BCS 376 microcline. Nevertheless, there is the possibility that the
parameterization used by Atkinson et al. (2013) underestimates the
contribution of feldspars at higher temperatures above about
-15 ∘C.
In the longer term it may be possible to identify what it is that leads to
the variation in ice nucleation activity between the different feldspar
classes. In particular, the nature of the active sites in the hyper-active
feldspars and the reason plagioclase is so much poorer at nucleating ice are
subjects of interest. The instability of the active sites in the hyperactive
feldspars may be related to dissolution of feldspar in water and
investigation of this process may allow progress towards understanding of
nucleation by feldspars. The results presented here are empirical in nature
and do not provide a thorough underpinning understanding of the nature of the
active sites. Nevertheless, the fact that the feldspar group of minerals have
vastly different ice nucleating properties despite possessing very similar
crystal structures may provide us with a means of gaining a fundamental
insight to heterogeneous ice nucleation.
Data availability
All experimental data in this study is available at
http://doi.org/10.5518/85.
Acknowledgements
We would like to acknowledge Theodore Wilson and Alexei Kiselev for helpful
discussions and John Morris for introducing TFW and MAC. We are grateful to
Alexei Kiselev and Martin Ebert for providing the TUD samples. Alex Harrison
thanks the School of Earth and Environment for an Undergraduate Research
Scholarship which allowed him to make many of the measurements presented in
this paper. We would like to thank the National Environmental Research
Council, (NERC, NE/I013466/1; NE/I020059/1; NE/K004417/1; NE/I019057/1;
NE/M010473/1) the European Research Council (ERC, 240449 ICE; 632272 IceControl;
648661 MarineIce), and the Engineering and Physical Sciences
Research Council (EPSRC, EP/M003027/1) for funding.Edited by: A. Huffman
Reviewed by: two anonymous referees
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