Using the diffusion couple technique, diffusion of
CO2 in a leucititic melt from the Colli Albani Volcanic District in
Italy was investigated at temperatures between 1200 and 1350 ∘C
in an internally heated pressure vessel at 300 MPa. To examine the effect of
dissolved H2O in the melt, experiments were performed for a nominally
dry melt (0.18 ± 0.03 wt % H2O) and for a hydrous melt
containing 3.36 ± 0.28 wt % H2O. Diffusion experiments were run
for 40 to 120 min and terminated by rapid quench. CO2 concentration
profiles were subsequently measured via attenuated total reflection
Fourier transform infrared spectroscopy (ATR-FTIR) and fitted with error
functions to obtain individual diffusion coefficients.
For the anhydrous and hydrous sample series, seven diffusion coefficients
were determined each. Diffusivity was found to increase exponentially with
temperature for both melts following an Arrhenius behaviour. The Arrhenius
equation for the nominally dry leucititic melt is described by
logDCO2=-1.44(±0.24)⋅10000T-1.95(±1.59),
where DCO2 is the diffusion coefficient in m2 s-1 and T is the
temperature in K. In the experimental temperature range, H2O has an
accelerating effect on CO2 diffusion. At 1200 ∘C,
diffusivity increases from 1.94 × 10-12 m2 s-1 in
the dry melt to 1.54 × 10-11 m2 s-1 in the hydrous
melt. The Arrhenius equation for the leucititic melt containing 3.36±0.28 wt % H2O is given by
logDCO2=-1.09(±0.30)⋅10000T-3.41(±1.99).
The activation energies for CO2 were determined to be 275 ± 47 kJ mol-1 for the anhydrous melt and 209 ± 58 kJ mol-1 for the
hydrous melt.
The high CO2 activation energy in the leucititic melt indicates that
the diffusion might be partly attributed to the carbonate species. At high
magmatic temperatures above 1200 ∘C, CO2 diffusivity in the
leucititic melt is only slightly lower than CO2 diffusion in rhyolitic
and basaltic melts, suggesting that CO2 diffusion in natural melts is
relatively independent from the bulk melt composition at such temperatures.
CO2 diffuses slower than other volatile components such as halogens and
H2O in depolymerized silicate melts. Thus, a fractionation of volatiles
can occur during magma ascent and degassing. The experimental data on
CO2 diffusion can be used for modelling the degassing mechanisms of
ultrapotassic mafic melts.
Introduction
Volatiles play a crucial role in the eruption dynamics of volcanoes. At
great depth and under high pressure, volatiles are soluble in silicate
melts. During magma ascent and the associated pressure decrease, silicate
melts can become oversaturated in their volatiles. Consequently, bubbles can
nucleate and volatiles start to degas. Further decompression and outgassing
cause bubble growth and expansion of the fluids within vesicles. As a
result, a fragmentation of the magma can occur during the eruption (e.g.
Zhang et al., 2007). However, bubble growth rates are limited by transport
properties (i.e. diffusion) of the associated volatile components within the
melt (Sparks et al., 1994). Diffusion data on volatiles at magmatic
temperatures and high pressures are therefore important for understanding
and modelling the degassing and eruption mechanisms of silicate melts.
Compared to H2O, the diffusion of CO2, the second most abundant
volatile in the Earth's crust (e.g. Anderson, 1975; Symonds et al., 1994;
Johnson et al., 1994), has however only been studied for a few natural melt
compositions. For example, CO2 diffusion coefficients for foiditic
melts are missing. Moreover, the effect of dissolved H2O on the
CO2 diffusivity has not been investigated in great detail.
Carbon dioxide can be present in the form of molecular CO2 and
carbonate (CO32-) within silicate glasses (e.g. Fine and Stolper,
1985). The carbon speciation in a silicate glass depends on the bulk
composition of the glass: with increasing depolymerization, the fraction of
carbonate increases and the portion of molecular CO2 decreases (e.g.
Fine and Stolper, 1985). However, the carbon speciation in a quenched glass
does not necessarily represent the speciation in the corresponding melt at
high temperatures. Experimental studies (Morizet et al., 2001; Nowak et al.,
2003; Spickenbom et al., 2010; Konschak and Keppler, 2014) and simulations
(Guillot and Sator, 2011) indicate that the molecular species of CO2
species is favoured as the temperature increases. This means that
depolymerized melts (e.g. basaltic melts) most likely feature a certain
proportion of molecular CO2 even though their quenched glasses only
show carbonate groups. Accordingly, the total diffusive flux of CO2 is
based on the individual diffusive fluxes of the two species involved. The
diffusion coefficient for total CO2DCO2total can therefore be
divided into the individual diffusion coefficients of the carbon species
DCO2mol and DCO32- in consideration of their relative fractions
XCO2mol and XCO32- (Nowak et al., 2004) according to the equation
DCO2total=DCO2mol⋅XCO2mol+DCO32-⋅XCO32-.
CO2 diffusion data were experimentally obtained for (simplified)
natural melts (Watson et al., 1982; Fogel and Rutherford, 1990; Watson,
1991; Zhang and Stolper, 1991; Blank, 1993; Nowak et al., 2004; Baker et
al., 2005) and for melts in the Na2O–Al2O3–SiO2 system
(Watson et al., 1982; Sierralta et al., 2002; Spickenbom et al., 2010).
Moreover, CO2 diffusion was investigated by molecular dynamics
simulations by Guillot and Sator (2011). The most recent review on CO2
diffusion is given by Ni and Keppler (2013). The experimental studies
generally revealed that CO2 diffusion does not heavily depend on the
bulk composition of the melt at high magmatic temperatures. For instance,
Nowak et al. (2004) showed that CO2 diffusion in rhyolitic to hawaiitic
melts at 1350 ∘C and 500 MPa is equal within error. In contrast
to this, Sierralta et al. (2002) and Spickenbom et al. (2010) discovered
that the addition of Na2O to an albitic melt accelerates CO2
diffusion, indicating a dependency on melt composition. These examples show
that the diffusion mechanism of CO2 in silicate melts is complex: based
on the melt composition and the temperature, specific proportions of
molecular CO2 and CO32- are coexisting in a melt and both
species show different individual diffusion coefficients with molecular
CO2 diffusing faster than the carbonate species (Nowak et al., 2004;
Spickenbom et al., 2010).
The aim of this study was the determination of diffusion coefficients for
CO2 in a synthetic leucititic melt that is based on an eruption product
of the Pozzolane Rosse eruption of the Colli Albani Volcanic District in
Italy (Freda et al., 2011: sample “SULm”). The melt composition is
characterized by a low SiO2 concentration (≈ 44 wt %) and a
high alkali content (K2O + Na2O > 12 wt %). The
explosivity of the Pozzolane Rosse eruption was supposedly caused by
CO2 outgassing due to decompression and leucite crystallization (Freda
et al., 2011) and was further enhanced by the relative high viscosity of the
bulk melt composition (Kleest et al., 2020). Hence, information on CO2
diffusivity is useful for understanding the degassing behaviour during the
eruption. Furthermore, this melt composition has the advantage that
absorption coefficients for transmission FTIR (Fourier transform infrared) spectroscopy were already
calibrated (Schanofski et al., 2019) and that a second method for CO2
quantification via attenuated total reflection
Fourier transform infrared (ATR-FTIR) spectroscopy has been established (Schanofski et
al., 2023).
Diffusion couple experiments were performed in an internally heated pressure
vessel (IHPV) at a constant pressure of 300 MPa. Experiments were conducted
at temperatures between 1200 and 1350 ∘C and with different water
concentrations dissolved in the melt (anhydrous vs. hydrous).
Experimental and analytical methodsSynthesis of starting glass
A leucititic glass was produced by melting oxides (SiO2, TiO2,
Al2O3, Fe2O3, MnO2, MgO) and carbonates
(CaCO3, Na2CO3, K2CO3) based on the chemical
composition of the leucitite sample “SULm” from Freda et al. (2011). For
decarbonation, the batch was heated at 1000 ∘C for 1 h in a
Carbolite box furnace. The decarbonated mixture was then melted at 1600 ∘C for 1.5 h in a Pt crucible in a 1 atm Nabertherm box furnace.
The melt was rapidly quenched by placing the hot crucible in water. The
glass was then ground for 20 min using a motorized Retsch mortar
(RM100). The glass powder was melted again at 1600 ∘C for 2 h to
improve its homogeneity and was again rapidly quenched. Subsequently, the
chemical composition of the starting glass was checked with a Bruker M4
Tornado micro X-ray fluorescence (μ-XRF) spectrometer (X-ray beam
spot size: 20 µm). The chemical composition is given in Table 1.
Chemical composition of leucititic glasses given in wt %.
Note: 2σ of repeated measurements given in parentheses (last decimals). a Freda et al. (2011): EPMA, five-point analyses. b This study: μ-XRF (type calibrated based on 50 in-house standard glasses), six-point analyses (300 s per point), X-ray tube: 50 kV and 200 µA. c All iron calculated as Fe2O3.
Synthesis of volatile bearing glasses
Volatile bearing glasses were synthesized in a vertically operating
internally heated pressure vessel (IHPV) with a rapid quench device. It uses
Ar gas as pressure medium and has an intrinsic oxygen fugacity fO2 of NNO + 3 ± 1. In the internal furnace, heat is generated by two platinum
windings which are controlled independently by a Eurotherm controller. In
the hot zone of the furnace, temperatures are measured with three S-type
thermocouples over a vertical distance of 3 cm. The temperature gradient
over this distance is typically between 10 and 15 ∘C. Pressure is
measured with a pressure transducer (accuracy: ± 5 MPa).
Au75Pd25 capsules (30 mm length, 4 mm outer diameter, 0.2 mm wall
thickness) and Pt capsules (30 mm length, 3 mm outer diameter, 0.2 mm wall
thickness) were cleaned with acetone and annealed with a propane flame.
After one side of the capsules was shut by arc welding using a Lampert PUK
U3 welding apparatus, a mixture of powdered leucititic starting glass and
silver oxalate (Ag2C2O4) was loaded into the capsules. Silver
oxalate serves as the CO2 source during the experiments since it
dissociates into CO2 and elemental silver at high temperatures. For
capsules of the hydrous sample series, 3 wt % deionized water was added to
the mixture. The targeted CO2 and H2O concentrations for all
synthesis capsules are listed in Table 2. The capsules were finally sealed
by welding the second opening. Subsequently, the capsules were put in a
hydrothermal autoclave at a pressure of about 120 MPa at room temperature
for 1 h to check for leakage. In the IHPV, the capsules were put into a Pt
cup suspended from a thin quench wire out of Pt90Rh10 (0.2 mm
diameter). Synthesis experiments were run at 300 MPa at temperatures between
1250 and 1300 ∘C for time spans between 91 and 117 h. At these
experimental conditions, the melts were fluid undersaturated according to
the solubility data for the same leucititic melt of Schanofski et al. (2019). Synthesis experiments were stopped with a rapid quench: by applying
an electric current to the quench electrodes, the quench wire fuses and the
sample containing cup drops into the cold part of the pressure vessel. In a
similar experimental setting, Benne and Behrens (2003) determined the
cooling rate of the rapid quench to be about 150 ∘C s-1.
Experimental conditions and results of synthesis experiments.
SampleCapsuleTt (h)Target H2OTarget CO2H2O glassNotes*(∘C)(wt %)(ppm)(wt %)Anhydrous sample series LCT-LK-S-01Au75Pd251250117060000.14 (1)LctLCT-LK-S-02Au75Pd251250117020000.14 (1)LCT-LK-S-03Pt130091060000.21 (2)LCT-LK-S-04Pt1300100020000.23 (2)Hydrous sample series LCT-LK-S-05Pt127593350003.21 (22)QCLCT-LK-S-06Pt127593330003.51 (17)
* Lct: Leucite; QC: quench crystallization.
All melts were successfully quenched to glasses and showed no bubbles. The
rapid quench caused stress cracks within all glasses making them extremely
brittle. Microscopic examination revealed that the CO2-rich, dry glass
synthesized in a Au75Pd25 capsule at 1250 ∘C
(LCT-LK-S-01) showed few idiomorphic leucite crystals (<0.1 vol %). Consequently, following synthesis experiments were run at
higher temperatures. The hydrous, CO2-rich sample LCT-LK-S-05 was
affected by quench crystallization, whereas all other samples were free of
quench crystals. The quench crystals were examined via Raman spectroscopy,
but the phase could not be identified because of its small size and weak
Raman signal.
Diffusion couple experiments
Whereas diffusion couples from other CO2 diffusion studies consisted of
a CO2-rich and a CO2-free starting glass (e.g. Nowak et al.,
2004), we used a CO2-rich and a CO2-poor starting glass for
diffusion couples. This approach was chosen because minimum CO2
concentrations of 1700 ppm are required for a reliable evaluation of
ATR-FTIR spectra (Schanofski et al., 2023) and thus for accurate
measurements of CO2 contents at the low concentration side of the
diffusion profiles.
Capsules from synthesis experiments were cut into cylindrical pieces with
lengths of 1 to 3 mm using a Buehler ISOMET saw with a diamond saw blade.
Their sawn surfaces were lapped stepwise with SiC grinding paper (600, 1200
and 2500 grit size), but the outer capsule material was not removed from the
glass cylinders because it stabilized the fragile samples (Fig. 1a). Two
cylinders (a CO2-rich and a CO2-poor one) were brought into contact
at their lapped surfaces (Fig. 1b), and the noble metal jackets were welded
together to stabilize the diffusion couple and to ensure a good contact
between the glasses (Fig. 1c). Au75Pd25-jacketed diffusion couples
were then placed into larger Au75Pd25 capsules (5.5 mm outer
diameter, 0.2 mm wall thickness), whereas Pt-jacketed diffusion couples were
put into larger Pt capsules (4.0 mm outer diameter, 0.2 mm wall thickness).
These capsules were subsequently welded shut (Fig. 1d) and brought in
hydrothermal autoclaves to 120 MPa at room temperature for 1 min. By
applying the external pressure, the outer capsule was pressed against the
inner cylindrical diffusion couple, and the encased air got compressed (Fig. 1e). Subsequently, this compressed air was released by cutting the outer
capsule open with a wire cutter. Afterwards, the capsules were welded shut
again. By this procedure, we reduced the presence of a mixed fluid phase
involving N2 that could lead to bubble formation due to changed
solubility behaviour of a CO2–N2 or CO2–H2O–N2 fluid
phase within the capsule during the diffusion experiment.
Photomicrographs of (a) glass cylinders with their lapped surfaces
facing upward (encased by Pt capsule; the glass on the right side is
affected by quench crystallization), (b) Pt-jacketed glass cylinders with
their lapped surfaces facing against each other, (c) a welded diffusion
couple (see welding points in the central area), (d) diffusion couple placed
in a larger Pt capsule and (e) Pt capsule with diffusion couple after
pressurization in the hydrothermal autoclave at 120 MPa.
Diffusion experiments were also performed in the IHPV. For each diffusion
experiment, one diffusion couple was attached to a hook-shaped sample holder
which got connected to the quench wire. The suspended sample was located in
the hot zone of the furnace at the height of a thermocouple. Before heating,
pressure was increased to about 190 MPa to prevent volatile degassing during
the heating process once the glass transition temperature (Tg12) is
exceeded. Temperature was then automatically increased at a rate of 40 ∘C min-1 until the target temperature was reached (target
temperatures were between 1200 and 1350 ∘C). During heating, the
targeted pressure of 300 MPa was reached and exceeded at temperatures
between 800 and 1100 ∘C (and therefore above Tg12;
Kleest et al., 2020) because of thermal expansion of the Ar gas. Excessive
gas was released stepwise by opening an outlet valve. Temperature was kept
constant once the targeted temperature was reached. From this point,
diffusion experiments were run for different durations between 40 and 120 min (nominal run-time tnom). The experiments were stopped by a rapid
quench. To quantify the diffusion during heating and cooling experimentally,
three zero time experiments were performed. The experimental approach is
similar to the procedure of normal diffusion experiments, but after reaching
the targeted temperature, diffusion was immediately stopped by a rapid
quench. Thus, zero time samples only experienced CO2 diffusion during
the heating process and the rapid quench.
All diffusion couples were successfully quenched to glasses. In contrast to
previous synthesis experiments, hydrous samples were not affected by quench
crystallization due to faster cooling rates of the smaller sample masses.
However, diffusion samples that were run at the lowest experimental
temperatures (sub-liquidus) had crystals that formed during the experiments
and not during the rapid quench (sizes above 1 µm and partly
idiomorphic). Anhydrous glasses (experimental temperatures ≤ 1250 ∘C) featured leucite and magnetite. LCT-LK-D-d04 (1200 ∘C) was affected heavily (30 vol % to 40 vol % crystals in the
CO2-rich part), whereas LCT-LK-D-d07 (1225 ∘C) and
LCT-LK-D-d02 (1250 ∘C) showed fewer crystals (up to 5 vol %).
In the case of the hydrous sample series, only LCT-LK-D-w02 (1200 ∘C)
was affected by crystallization of magnetite (≈ 1 vol %). Again,
all glasses showed stress cracks caused by the rapid quench (Fig. 2). The
diffusion couples could therefore not be unwrapped from their capsule
material because they would have broken into pieces.
Reflected light mosaic image of the diffusion sample LCT-LK-D-d05;
the glass has many quench cracks demonstrating the fragility of the sample;
the red dotted line schematically displays the diffusion profile that was
measured for this sample (size and distance of measuring points are not true
to scale); see Fig. 5a for the corresponding diffusion profile.
Quantification of H2O in the synthesized volatile bearing starting
glasses
H2O concentrations of synthesized leucititic glasses were analysed with
a Bruker Hyperion 3000 IR microscope attached to a Bruker Vertex 70
FTIR spectrometer. The spectrometer and microscope were constantly flushed with
dried air to reduce spectral noise from atmospheric H2O and CO2.
The microscope was equipped with a liquid N2 cooled MCT (mercury cadmium
telluride) detector covering the mid- to near-infrared spectral range. For
quantification, the principles of the Lambert–Beer law were used.
Single glass fragments or slabs cut from the capsules were ground and doubly
polished with 1 µm diamond paste. IR spectra were measured in the
mid-infrared (MIR) range using a KBr beam splitter and a Globar light
source. One hundred scans per spectra were collected with a resolution of 4 cm-1 in the spectral range from 6000 to 600 cm-1. At least three
spectra were measured for each sample to obtain an average H2O
concentration. The fundamental stretching band of OH at 3570 cm-1 was
used for quantification of nominally dry glasses. Here, a two-tangent
baseline between 3800 and 2500 cm-1 was applied and the peak height was
determined. A molar extinction coefficient of ε3570=65±5 L mol-1 cm-1 was used (based on molar extinction
coefficients for basaltic compositions; e.g. Yamashita et al., 1997).
Spectra of H2O-rich glasses (hydrous sample series) were also measured
with the MIR setup of the spectrometer. They featured the absorption of the
combination band of molecular H2O at 5200 cm-1 as well as the
absorption of the combination band of OH at 4500 cm-1. Total water
concentrations were determined by evaluating both absorption bands. Spectra
were corrected using a polynomial baseline that was fitted through the
minima of the spectra (for details see Schanofski et al., 2019). The molar
extinction coefficients of Schanofski et al. (2019) for the same leucititic
composition were used for quantification: ε5200=1.00±0.09 L mol-1 cm-1 (H2Omol) and ε4500=0.42± 0.02 L mol-1 cm-1 (OH). Sample
thicknesses were measured with a Mitutoyo digital micrometre, which has an
accuracy of ± 3 µm. Densities were determined using a Mettler
Toledo balance equipped with a density determination kit where glass samples
were weighted in air and in ethanol.
Quantification of CO2 in the diffusion samples
CO2 diffusion profiles were measured with the ATR objective of the IR
microscope. The ATR method (attenuated total reflection) is based on the
interaction between the IR beam and a sample in total reflection
(Fahrenfort, 1961). One advantage of this method is that it allows one to
analyse samples that reached total absorption in transmission FTIR
spectroscopy for the absorption bands of interest. Moreover, no doubly
polished sample wafers are required. This becomes relevant when samples
contain many cracks and are too brittle to be cut and doubly polished like
our leucititic glasses. ATR-FTIR spectroscopy has already been used for the
quantification of volatiles in silicate glasses (e.g. H2O: Lowenstern
and Pitcher, 2013; CO2: Schanofski et al., 2023) but also for the
quantification of H2O and CO2 in apatite (Hammerli et al., 2021).
For an ATR measurement, a crystal with a high refractive index n is pressed
onto the sample. The incoming IR beam passes through the ATR crystal and
gets totally reflected at the crystal–sample boundary. Here, the IR light
interacts with the sample forming an evanescent wave that penetrates the
sample. The Bruker ATR objective has a Germanium crystal (n=4.01) with a
circular interface (diameter: 100 µm) and a rectangular IR beam size of
about 30 by 30 µm. For pressing the Ge crystal onto the sample surface,
five different force levels are available.
At first, diffusion couples were cut in half, parallel to the cylinder axis
using a WELL 3242 diamond wire saw (wire thickness 130 µm). For each
diffusion couple, one half was embedded in epoxy resin, ground using SiC
grinding paper (grit size 600, 1200 and 2500) and finally polished with 1 µm diamond paste. The embedded samples were fixed on the IR
microscope sample stage using carbon adhesive tape. For determination of
CO2 diffusion profiles, line profiles parallel to the direction of
diffusion were measured using variable point spacing (30 to 70 µm).
Areas with cracks within the glass were avoided since the Ge ATR crystal can
be harmed by roughness (Fig. 2). Consequently, diffusion profiles often
showed gaps because of the quench cracks in the leucititic glasses. Zero
time samples were prepared and analysed similar to normal diffusion samples.
The analyses were performed in the MIR range using a KBr beam splitter. For
each spectrum, 32 scans with a resolution of 4 cm-1 were collected in
the range from 4000 to 600 cm-1. All measurements were performed using
force level 3, corresponding to about 5 N. The background was measured
repeatedly after every fourth point analysis using the same analytical
conditions.
All ATR spectra featured a carbonate doublet between 1600 and 1300 cm-1. Evaluation was done using the quantification method of Schanofski
et al. (2023): the spectra were corrected by a two-tangent baseline and
the peak height of the 1430 cm-1 CO32- absorption band was
determined accordingly (Fig. 3). The 1430 cm-1 absorption band was
always used for evaluation because the 1510 cm-1 absorption band can be
influenced by the absorption of the bending vibration of molecular H2O
at 1640 cm-1. Since the peak height also depends on the contact between
ATR crystal and the sample surface, the absorption was normalized to the
peak area of the absorption band of tetrahedral coordinated cations T–O
(with T= Si4+, Al3+, Fe3+) at 970 cm-1, which
represents the aluminosilicate network. Here, a two-tangent baseline with
base points at 1200 and 790 cm-1 was applied (Fig. 4). More details on
the normalization procedure can be found in Schanofski et al. (2023).
The total CO2 concentration can then be calculated using the
correlation coefficient of Schanofski et al. (2023) according to
CCO2=A1430⋅10000Int970⋅0.4394,
where CCO2 is the concentration of CO2 in wt %, A1430 is the
absorption of the carbonate band at 1430 cm-1 and Int970 is the
integral of the T–O band at 970 cm-1.
ATR-FTIR spectra of CO2-rich glasses (LCT-LK-S-03: anhydrous;
LCT-LK-S-05: hydrous); the dashed lines mark the TT baselines that were
applied to evaluate the peak heights of the carbonate doublet; spectra are
offset for clarity.
ATR-FTIR spectra of CO2-rich, anhydrous glass; the dashed
lines mark the TT baseline that was applied to integrate the peak area of
the T–O band for normalization.
Mathematical methods for determining diffusion coefficientsEvaluation of diffusion profiles
For every diffusion sample, CO2 concentration vs. distance plots were
compiled based on the ATR measurements. Since CO2 diffusion is
independent from its total concentration (Watson et al., 1982) and the
concentration vs. distance plots showed a symmetrical shape, diffusion could
be described by the equation for concentration independent diffusion in a
semi-infinite medium (Crank, 1975)
Cx,t=Clow+(Chigh-Clow)2⋅erfc(x-x0)2D⋅t,
where C(x, t) is the concentration of CO2 in wt % at an x-coordinate x in
mm after a time t in s, Clow is the CO2 concentration in the
CO2-poor medium in wt % at t0, Chigh is the CO2
concentration in the CO2 enriched medium in wt % at t0,
x0 is the x coordinate of the contact plane of both media in mm and D is
the diffusion coefficient in mm2 s-1. The software Table Curve 2D Version
5.01 (Systat Software Inc.) was used for fitting the diffusion profiles
using Eq. (4):
Cx,t=a+b⋅erfc(x-c)d.
The software determines the best fitting numeric values for the parameters
a, b, c and d. Finally, the fit parameter d provides the diffusion coefficient
D through Eq. (5):
D=d24⋅t.
The obtained fit parameter and calculated diffusion coefficients are given
in Table 3. Figure 5a and b exemplarily show concentration vs. distance
plots for an anhydrous and hydrous diffusion experiment. All diffusion
profiles are compiled in the supplementary material (Figs. S1–S17).
CO2 diffusion profiles of (a) an anhydrous diffusion sample
(LCT-LK-D-d05) and (b) a hydrous diffusion sample (LCT-LK-D-w06); distance = 0 mm marks the initial contact plane of the melts; the grey lines
represent the fitted complementary error functions from which the diffusion
coefficients were extracted; error bars are representative for 2σ
errors of repeated measurements.
Experimental conditions and results of the CO2 diffusion
experiments.
a Diffusion profile fit parameter from which D is calculated (see Eqs. 4 and 5). b Standard error of the fit parameter d which is used for error calculation (see Sect. 3.3). c CO2 max and CO2 min determined from average CO2 values in CO2-rich and CO2-poor parts of diffusion couples; 2σ of repeated measurements given in parentheses (last decimals). d D: discarded experiment; Lct: leucite; Mag: magnetite; ZT: zero time experiment; F: failed experiment (D is not determinable).
Correction of the heating time
Diffusion already occurs during the heating process in the IHPV before the
targeted temperature is reached and the nominal run-time tnom is
recorded. The effective time of diffusion during the heating process
theating was therefore calculated for every diffusion experiment with an
iterative procedure described by Nowak and Behrens (1997). However, instead
of using the experimentally determined diffusion coefficients in the final
calculation step, the diffusion coefficients were extracted from the
preliminary Arrhenius equations (based on diffusion coefficients that are
not corrected for the heating time). By this, the results are not biased by
the deviations of each diffusion coefficient from the Arrhenius equation and
the calculated theating values increase with increasing experimental
temperature. The calculation procedure was only performed once because
further iterations only caused insignificant changes in theating and D
respectively.
The validity of the iterative procedure was furthermore tested with the zero
time experiments for the anhydrous sample series. The diffusion profiles of
these zero time samples were also fitted with Eq. (4) to obtain the d
parameter (see supplementary material for zero time profiles). The zero time
approach is based on the relation between the length of a diffusion profile
and the effective run-time of the experiment (Zhang and Behrens, 2000). This
relation is expressed by
ddiffd02=(theating+tnom)theating,
where ddiff is the d parameter obtained from the diffusion profile of a
normal diffusion experiment with the nominal run-time tnom in s;
d0 is the d parameter from the zero time experiment at the same
temperature, pressure and heating rate; and theating is the diffusion
time during heating and cooling in s. Therefore, theating can be
calculated
theating=tnomddiffd02-1.
As a result, theating was obtained for three experimental temperatures
(1250, 1275 and 1300 ∘C). These experimentally determined values
were in good agreement with the iteratively obtained values (same order of
magnitude; maximum deviation of 40 s; see Table 4). The theating values
from the iterative procedure were finally used to correct all diffusion
coefficients by adding theating to the nominal run-time tnom of the
initial experiment (resulting in teff) and recalculating the diffusion
coefficient using Eq. (5).
Determined diffusion times of the IHPV heating process.
ExperimentTtnomtheating(iterative)theating(zerotime)(∘C)(s)(s)(s)Anhydrous sample series LCT-LK-D-d041200360089–LCT-LK-D-d071225360092–LCT-LK-D-d01125072009565LCT-LK-D-d021250360095134LCT-LK-D-d06127536009996LCT-LK-D-d0513003600102142LCT-LK-D-d1013252700105–LCT-LK-D-d1113502790108–Hydrous sample series LCT-LK-D-w0212004500115–LCT-LK-D-w0712153600117–LCT-LK-D-w0312253600119-LCT-LK-D-w0112502700122–LCT-LK-D-w0412753600126–LCT-LK-D-w0612752400126–LCT-LK-D-w0513003600130–LCT-LK-D-w0813252520134–Calculation of errors
Errors of determined diffusion coefficients are based on the uncertainties
of different experimental and analytical parameters. For estimating the
absolute errors of the diffusivities, the mathematical approach of Böhm
and Schmidt (2013) was used. In total, four relevant error sources were
considered. For the effective run-time teff of all diffusion
experiments, an uncertainty of ± 60 s was assumed. Another uncertainty
is based on the mathematical fit of the diffusion profiles. The error is
represented by the standard error Δd of the fit parameter d which is
given by the fitting software individually for every profile. An uncertainty
in temperature ΔT of ± 10 ∘C was assumed for every
diffusion experiment. A fourth error source is given by the uncertainty of
the length of a diffusion profile. If the measured profile is not strictly
parallel to the direction of diffusion or if the initial cutting and
polishing were already oblique to the direction of diffusion, it will result
in an overestimation of the diffusion coefficient because the distance of
the measured profile is elongated. This error source only affects the
negative error of D since the length of the measured profiles is always
longer than the length of an ideal profile. A maximum deviation of
10∘ between the measured profile and the actual diffusion
direction was assumed.
New absolute diffusion coefficients were calculated using minimum and
maximum values of teff and d for every diffusion profile using Eq. (5).
The effect of uncertainty in temperature on the diffusion coefficient was
estimated using the constructed Arrhenius equation. Here, maximum and
minimum values for D were calculated for every diffusion experiment by
extracting such diffusion coefficients for minimum and maximum temperatures.
To evaluate the uncertainty in diffusion profile length, the distances of
all diffusion profiles were recalculated and finally all profiles were
refitted. As a result, a new d parameter was obtained from which new minimum
diffusion coefficients were calculated with Eq. (5). By this, three maximum
and four minimum diffusion coefficients were obtained for every diffusion
experiment (individual errors given by the difference between these
coefficients and the initial diffusion coefficients). Finally, positive and
negative errors (ΔmaxD and ΔminD) were calculated by
error propagation (given as logarithmic values in Table 3). As described
above, all these calculations were done for every experiment and every
diffusion coefficient respectively.
ResultsSynthesis experiments
The dry leucititic glasses were not completely water free. Due to the adsorption
of atmospheric water on the starting materials (glass powder and silver
oxalate), small amounts of water were added to the capsules. Moreover, water
is formed by reduction of iron during the experiment because of the presence
of hydrogen within the autoclave (e.g. Botcharnikov et al., 2006). The
determined H2O concentrations in the synthesized glasses are given in
Table 2. The nominally dry sample series showed an averaged water content of
0.18 ± 0.03 wt %. Water concentrations of water bearing samples were
higher than the amount of water originally loaded into the capsules. Again,
adsorption of water on the starting materials and reduction of iron by
hydrogen increased the total H2O concentration. Moreover, a discrepancy
in water content between both water enriched glasses was found:
LCT-LK-S-06 (CO2-poor) showed about 3000 ppm more total
H2O. This results in an averaged water concentration of 3.36 ± 0.28 wt % for diffusion couples of the hydrous sample series.
Maximum and minimum CO2 concentrations obtained from diffusion profiles
should represent the CO2 concentrations of the CO2-rich and
CO2-poor starting glasses from synthesis experiments. Since different
starting concentrations in diffusion couples from the same starting glass
were detected, CO2 was not homogenously distributed within the capsules
of the synthesis experiments. Higher concentrations were detected in
diffusion couples that consisted of glass blocks from the upper part of the
synthesis capsules (the upper part corresponds to the capsule part that
faced upward in the IHPV). This implies that the experimental time for
synthesis experiments (3 to 4 d) was not sufficient to completely
homogenize CO2 in the melt at fluid undersaturated conditions.
Consequently, CO2 concentrations of the synthesized glasses are not
given as average values but the individual starting CO2 concentrations
for each diffusion experiment are listed in Table 3.
Sierralta et al. (2002) found a zoning of CO2 and H2O in glasses
synthesized in Pt capsules. H2O was enriched at the capsule rims,
whereas CO2 was enriched in the capsule centre. In contrast to this, no
such zoning of CO2 and H2O was observed in our samples. Both
volatiles showed a homogeneous distribution in the glasses within error. Pt-jacketed glass cylinders were therefore suitable for diffusion experiments.
Considering the experimental duration of 91 to 100 h for synthesis
experiments in Pt capsules and the high initial iron concentration of the
starting glass, loss of iron from the melt to the platinum of the
surrounding capsule was expected. μ-XRF analyses confirmed that a
fraction of iron was lost during synthesis experiments of dry melts in Pt
capsules. Fe2O3 concentrations of 9.57 ± 0.09 wt % and
9.66 ± 0.05 wt % were determined for LCT-LK-S-03 and LCT-LK-S-04
(initial Fe2O3 concentration: 10.69 ± 0.06 wt %).
Contrarily, no decrease in Fe2O3 was measured for hydrous glasses in
Pt capsules using μ-XRF. Furthermore, no iron gradients between capsule
centre and capsule wall were detected for dry and hydrous glasses, indicating
that a steady state was reached during the experiments. Further iron loss
during diffusion experiments is unlikely due to the short experimental
duration and the apparent steady state between iron concentration of melt
and capsule material.
Diffusion couple experiments
For the nominally dry and hydrous melt series, seven valid diffusion
coefficients were obtained each. The diffusion coefficient from experiment
LCT-LK-D-d01 (anhydrous; 1250 ∘C) was not used for
further evaluation because of the large melt movement and the associated
error source (see discussion of error sources in Sect. 5.1). Experiment
LCT-LK-D-w08 (hydrous; 1325 ∘C) is considered as a fail because
no diffusion coefficient could be determined from the concentration profile
due to convection and the resulting melt movement as well as bubble
formation.
Figure 6 shows the diffusion coefficients (logD) plotted as a function of
inverse temperature (Arrhenius diagram). For both sample series, CO2
diffusivity increases with increasing temperature. The addition of 3.36 wt % H2O has an accelerating effect on CO2 diffusion in the
leucititic melt. For instance, CO2 diffusivity is 0.75 log units faster
in the hydrous melt (3.36 ± 0.28 wt % H2O) compared to the
nominally dry melt (0.18 ± 0.03 wt % H2O) at 1300 ∘C.
Arrhenius diagram for CO2 diffusion in leucititic melt; the
grey lines represent the linear Arrhenius equations.
The data points of each sample series were fitted with a linear equation
(Arrhenius equation). The Arrhenius behaviour for diffusion is generally
described by the exponential equation
D=D0⋅e-EAR⋅T,
where D is the diffusion coefficient in m2 s-1, D0 is the diffusion
coefficient in m2 s-1 at infinite temperature, EA is the activation
energy in J mol-1, R is the universal gas constant (8.3145 J K-1 mol-1) and T is the temperature in K. Equation (8) can be modified to a
linear equation when diffusion coefficients are plotted in an Arrhenius
diagram (logD vs. 1/T):
logD=-EAR⋅ln10⋅1T+logD0.
The term -EAR⋅ln10 corresponds to
the slope of the linear equation, and logD0 is given by the intersection
with the y axis. Consequently, the activation energies for CO2 and the
D0 values can be calculated (Table 5). For the nominally dry melt, the
activation energy for CO2 is 275 ± 47 kJ mol-1, whereas it is
209 ± 58 kJ mol-1 in the hydrous melt. Accordingly, water seems
to decrease the activation energy of CO2; however, given the large
uncertainties of EA, which were calculated using the standard errors of
the slopes, a definite distinction is not possible.
Activation energies of CO2 and D0 values for the
leucititic melt.
Sample seriesEA (kJ mol-1)ΔEA (kJ mol-1)D0 (m2 s-1)D0 max (m2 s-1)D0 min (m2 s-1)Anhydrous275471.1 × 10-24.4 × 10-12.9 × 10-4Hydrous209583.9 × 10-43.5 × 10-24.4 × 10-6DiscussionExperimental uncertainties and error sources
Deviations of experimentally determined diffusion coefficients from the
Arrhenius equations partly exceed the calculated individual errors. A
similar observation was made in other CO2 diffusion studies. Based on
the scatter of their data, Spickenbom et al. (2010) suggested errors of
about ± 0.15 log units for individual diffusion coefficients even
though their calculations produced smaller errors. Applying such error bars
to the diffusion coefficients presented here leads to a better intersection
between Arrhenius equations and data points. It illustrates that further
experimental error sources must exist.
For our experiments, melt convection presents an experimental uncertainty
which is hard to quantify and difficult to consider during error
calculation. Shifts of diffusion interfaces (discrepancy between initial
glass interface and mean CO2 concentration) were observed for several
diffusion samples. It indicates that convection or some sort of melt
movement occurred during the experiment. By comparing the coordinates of
mean CO2 concentrations (extracted from diffusion profiles) with the
coordinates of the initial glass interface (identifiable from small offsets
between the two capsule jackets), the displacement distance xshift was
determined for all samples (Table 3). The highest displacements were
observed for the diffusion samples LCT-LK-D-d01 (≈ 750 µm) and LCT-LK-D-w08 (>1 mm). Accordingly, both
samples were discarded. Other samples that experienced more moderate shifts
of 50 to 450 µm were used for further evaluation. A strong intermixing
of melts is excluded since diffusion profiles always showed a symmetric
shape. We therefore believe that the measured concentration profiles only
represent the diffusional transport of CO2.
Crystallization of leucite and magnetite at sub-liquidus conditions in some
of our experiments will also impact the diffusion mechanism of CO2. The
chemical composition of the residual melt gets changed slightly, and crystals
might act as some sort of barrier for diffusing molecules or ions. The
temperature range for experiments is limited by the furnace of the IHPV
towards higher temperatures. Therefore, experiments affected by
crystallization were not discarded to cover a temperature range of at least
100 to 150 ∘C for a reliable fit of the Arrhenius equations.
Heterogeneities such as inhomogeneous distribution of H2O or CO2
in the synthesized glasses present another error source for the diffusion
experiments. Imperfect preparation of diffusion couples could cause problems
such as oblique contact between the glasses or the encasement of large
amounts of ambient air and atmospheric H2O.
An analytical error source is given by convolution of CO2
concentrations in diffusion profiles due to the relatively large spot size
of 30 µm of the ATR setup. Convolution will result in an
overestimation of the diffusion coefficients because the diffusion profile
is elongated (Ganguly et al., 1988). Like Schmidt et al. (2013), we used
equation 20 from Ganguly et al. (1988) to estimate the effect of convolution
on the diffusion coefficients:
DDc=1-8⋅εxc2,
where D is the true diffusion coefficient, Dc is the diffusion
coefficient obtained from the fitted profile, ε is the standard
deviation of the intensity distribution and xc is the half width of the
diffusion profile. Based on the observations of Ni and Zhang (2008) we
assumed that the spatial distribution of the ATR-IR signal is also Gaussian
and that the true spatial resolution is 1.5 times the spot size diameter (45 µm), which corresponds to the full width at half maximum (FWHM). The
standard deviation of the spatial distribution ε then
corresponds to about 19.2 µm (ε= FWHM / 2.35).
According to Eq. (10) we overestimate the diffusion coefficient for the sample
with the shortest diffusion profile (LCT-LK-D-d04) by about 2 % (0.01 log
units). The uncertainties due to convolution are therefore negligible. Since
the profiles of the zero time experiments are almost 1 order of magnitude
shorter than the profiles of normal diffusion experiments, their length is
overestimated more drastically. The calculated theating values from the
zero time approach (Table 4) therefore represent maximum values. This also
demonstrates the preferred application of the iterative calculation of
theating.
CO2 activation energies for different anhydrous silicate
melts.
MeltEA (kJ mol-1)ΔEA (kJ mol-1)P (MPa)Data pointsReferenceTrachytic100–12002Baker et al. (2005)Rhyolitic145850 to 1059Blank (1993)Basaltic2493015004Watson et al. (1982)Leucititic275473007This studyEffect of H2O on CO2 diffusion
Water was found to increase the diffusivity of CO2. For the
experimental temperature range, CO2 diffuses almost 1 order of magnitude
faster in a leucititic melt containing 3.36 ± 0.28 wt % H2O
than in a nominally dry melt (0.18 ± 0.03 wt % H2O). Similar
observations were made by Watson (1991) and Sierralta et al. (2002), who also
examined the effect of water. At 1250 ∘C, the effect of water in
the leucititic melt is very similar to the water effect in an albitic melt
measured by Sierralta et al. (2002): about 4 wt % H2O causes an
increase in diffusivity by 1 order of magnitude. In contrast, Baker et al. (2005) did not find an accelerating effect of water on CO2 diffusion in
a trachytic melt in their preliminary calcite dissolution experiment at 1200 ∘C. Zhang and Ni (2010) noted that this result could however be
imprecise due to the inadequate analytical method (CO2 quantification
with electron microprobe using the “difference from 100 %′′ technique).
Therefore, it is inferred that dissolved water generally accelerates the
CO2 diffusion in silicate melts. Assuming that dissolved water forms OH
groups within the melt structure, more non-bridging oxygen is formed and
the melt gets depolymerized upon addition of water (increasing NBO/T: 0.509
for the nominally dry leucititic melt and 0.825 for the hydrous leucititic;
NBO/T calculated after Mysen (1988) assuming that dissolved H2O only
forms OH groups and that the molar fraction of Fe2O3 is equal to
the molar fraction of FeO). As a result, molecules and ions can diffuse more
easily through the melt structure.
Watson (1991) found that the presence of water lowers the activation energy
for CO2 diffusion in the rhyolitic obsidian melt. This finding
coincides with the trend observed for the leucititic melts even though our
calculated activation energies intersect within the error range. Since the
activation energy can be described as an energetic barrier that must be
overcome to start a chemical reaction or diffusion, it gives information on
how strongly CO2 is incorporated within the melt structure (not
necessarily bonded). A lowered activation energy therefore indicates that
CO2 is incorporated less strongly in the melt structure and that more
diffusive pathways are available in the presence of water because of the
melt depolymerization.
An accelerating effect of water was also observed for diffusion of other
volatile components in silicate melts. For instance, halogens diffuse faster
in hydrous phonolitic melts (Balcone-Boissard et al., 2009; Böhm and
Schmidt, 2013) compared to their anhydrous analogues. Even for water itself, a
higher water concentration in the initial melt accelerates the diffusivity
of total H2O (e.g. Zhang and Stolper, 1991; Nowak and Behrens, 1997;
Zhang and Behrens, 2000; Schmidt et al., 2013). These results support the
validity of the experimental data on the effect of H2O on CO2
diffusion in the leucititic melt.
Comparison with CO2 diffusion in other natural silicate melts
A comparison of CO2 diffusivity in the leucititic melt with diffusivity
in rhyolitic, basaltic and trachytic melts is shown in Fig. 7. An
extrapolation of the Arrhenius equations implies that CO2 diffuses
slightly slower in the leucititic melt compared to the basaltic melt in the
temperature range of the experimental data (1200 to 1500 ∘C).
Below 1325 ∘C, CO2 diffusion in the leucititic melt is also
slower than in the rhyolitic melt. At 1300 ∘C for instance,
diffusion in the rhyolitic melt (extrapolated) is about 1.2 times faster
than in the leucititic melt. Above 1325 ∘C, diffusion in the
leucititic melt exceeds diffusion in the rhyolitic melt. However, a clear
distinction is not possible when the uncertainties of each Arrhenius
equation (especially upon extrapolation) are taken into account.
Arrhenius diagram for CO2 diffusion in natural melts;
rhyolite and H2O from Watson (1991); rhyolite from Blank (1993); basalt
from Watson et al. (1982); trachyte from Baker et al. (2005).
Based on these observations, one can assume that CO2 diffusion is
relatively insensitive to the bulk melt composition when looking at high
temperatures. A comparison with diffusion in the trachytic melt from Baker
et al. (2005) is difficult since the Arrhenius equation is only based on two
data points and our data demonstrate that there can be a certain scatter.
The Arrhenius equations for hydrous melts shown in Fig. 7 illustrate the
strong effect of dissolved H2O on diffusion. Its effect clearly exceeds
the influence of the bulk melt composition. Sierralta et al. (2002) showed
that Na2O also strongly accelerates CO2 diffusion (albitic melt + Na2O). The trends for H2O and Na2O therefore contradict
the general observation that CO2 diffusion does not depend on the melt
composition. Consequently, depolymerization due to the addition of H2O
and Na2O affects the CO2 diffusion behaviour differently than a
general change from polymerized (rhyolitic) to depolymerized (basaltic)
melts. Nowak et al. (2004) argued that the observed CO2total
diffusion is an interplay between CO2 speciation and the individual
diffusivities of the two species (see Eq. 1). For a fundamental change in
melt composition (from rhyolite to basalt), the two effects compensate each
other (Nowak et al., 2004). The observed increase in CO2 diffusivity
due to the addition of H2O (or Na2O) could therefore potentially be
explained by an increase of the individual diffusivities (CO2mol
and CO32-) due to depolymerization of the melt structure (but a
constant carbon speciation) or by a change of the carbon speciation in the
melt (increasing CO2mol/ CO32-).
The Arrhenius equations for leucititic and basaltic melts show steeper
slopes than the Arrhenius equations for rhyolitic and trachytic melts,
indicating a higher activation energy for CO2 diffusion in the
depolymerized systems (Table 6). Carbonate ions are bound to the silicate
network (e.g. Fine and Stolper, 1985; Kohn et al., 1991), whereas CO2
molecules are arranged in interstitial places, and thus they are only weakly
associated to the silicate network (e.g. Guillot and Sator, 2011).
Spickenbom et al. (2010) suggested that CO2 diffusion in depolymerized
melts could be attributed to an interconversion mechanism of CO32-
and CO2: structurally bound carbonate converts into a CO2 molecule
which moves through melt “doorways” and reconverts into carbonate, which
is incorporated into the melt structure. Consequently, more energy is
required for total CO2 diffusion. An elevated activation energy could
therefore indicate that CO2 is at least partly present in the form of
carbonate in the depolymerized melts.
Comparison with diffusion of other volatiles and implications for magma
degassing
Besides CO2, natural silicate melts contain several volatile components
such as water and halogens (e.g. Anderson, 1975). The comparison of
diffusion coefficients for different volatile components is important
because the rate of diffusive bubble growth is directly proportional to the
diffusion coefficient of the diffusing species (Sparks et al., 1994).
Profound differences in diffusivities could cause a fractionation of the
volatile components in the degassing fluid phase (Watson et al., 1982).
Unfortunately, there is no published diffusion data available for leucititic
melts. H2O diffusion is constrained for basaltic and differentiated
melts (e.g. Zhang and Stolper, 1991; Zhang and Behrens, 2000). Sulfur and
halogen diffusion are also experimentally investigated for such melt
compositions (e.g. Freda et al., 2005; Alletti et al., 2007). We therefore
compare the determined CO2 diffusion in the leucititic melt with
volatile diffusion in basaltic melts at anhydrous conditions (Fig. 8). The
comparability of the diffusion data is limited since different melt
compositions and experimental techniques are compared. Moreover, Arrhenius
equations were extrapolated to the temperature range of our study, which
further increases the uncertainties in diffusion coefficients. Nevertheless,
the comparison demonstrates some general trends for mafic melts.
Arrhenius diagram for diffusion of different volatiles in
depolymerized melts at anhydrous conditions; H2O diffusion in a
basaltic melt from Zhang and Stolper (1991); F and Cl diffusion in a
basaltic melt from Alletti et al. (2007); S diffusion in a basaltic melt
from Freda et al. (2005); CO2 diffusion in a leucititic melt from this
study; Arrhenius equations for H2O, F, Cl and S are extrapolated to the
temperature range of our study for better comparison.
The diffusion of H2O (at low total water concentrations) is more than
1 order of magnitude faster than CO2 diffusion, and it therefore marks
the largest difference. The halogens F and Cl diffuse roughly half an order
of magnitude faster than CO2, whereas S diffusion is similar to CO2
diffusion. Assuming that the diffusivity of these volatiles in a basaltic
melt is similar to diffusion in the leucititic melt, a diffusive
fractionation (especially between CO2 and H2O or between S and
H2O) could occur due to decompression and bubble growth during fast
magma ascent. Alletti et al. (2007) developed a model for the behaviour of
volatiles during bubble growth in a basaltic melt. The model shows that the
fluid composition of bubbles should remain constant during steady-state
degassing. During fast bubble growth however, the volatile ratios of the
fluid change as a function of bubble growth rate due to different
diffusivities and partitioning coefficients of the volatiles. Given the slow
CO2 diffusivity (this study) and the strong partitioning of CO2
into the vapour phase (e.g. Lesne et al., 2011), CO2 will be relatively
enriched in the gas phase during slow bubble growth, whereas its portion will
decrease at higher bubble growth rates. For instance, a high CO2/ F
ratio in the emitted gas of an active volcano would indicate slow bubble
growth, whereas a decreasing CO2/ F ratio would indicate faster bubble
growth and increased volcanic activity.
Pichavant et al. (2013) and Le Gall and Pichavant (2016a, b) observed an
anomalous degassing behaviour for CO2 in their decompression
experiments. CO2 concentrations in their run products exceeded the
equilibrium solubilities of CO2 at final pressure indicating a
disequilibrium degassing mechanism. Pichavant et al. (2013) identified the
slow diffusion of CO2 (compared to H2O) as the driving force for
this phenomenon. Our data further confirm the slow diffusivity of CO2
and imply that disequilibrium degassing could also occur in the leucititic
melt. Large degrees of CO2 supersaturation could be maintained during
magma ascent, promoting the explosive eruption behaviour of the Colli Albani
melts. Disequilibrium degassing and diffusive fractionation should therefore
be considered for monitoring and interpreting gas emission of active
volcanoes (Pichavant et al., 2019).
Conclusions
The first data set for CO2 diffusion with reliable Arrhenius relations
determined at constant conditions (pressure, oxygen fugacity, experimental
and analytical approach) for an anhydrous and hydrous melt is presented
here. The diffusivity in the leucititic melt increases exponentially with
temperature and shows Arrhenius behaviour. Dissolved H2O in the melt
accelerates diffusion of CO2 by about 1 order of magnitude at water
concentrations of 4 wt %. The data confirm that the diffusion of CO2
in nominally dry melts is not heavily dependent on the melt composition. So
far, there is no satisfactory explanation as to why CO2 diffusion is
insensitive to the bulk melt composition but is strongly affected by
dissolved H2O.
The study further demonstrated that the ATR-FTIR method is a suitable method
for spatially resolved CO2 quantification in silicate glasses. It
presents a good alternative to the traditional transmission FTIR
spectroscopy when samples are too brittle for polishing too-small thicknesses
or when absorption bands reach total absorption due to high concentrations.
Data availability
The raw data are stored at the Department of Mineralogy at the University of
Göttingen. All diffusion profiles are either part of the paper or
shown in the Supplement.
The supplement related to this article is available online at: https://doi.org/10.5194/ejm-35-117-2023-supplement.
Author contributions
LK performed the experiments and analyses as part of his Master's thesis.
BCS initiated and supervised the research project and helped with the
experiments and analyses. LK wrote the paper draft. BCS reviewed and
edited the paper.
Competing interests
The contact author has declared that neither of the authors has any competing interests.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Special issue statement
This article is part of the special issue “Probing the Earth: magma and fluids, a tribute to the career of Michel Pichavant”. It is a result of the Magma & Fluids workshop, Orléans, France, 4–6 July 2022.
Acknowledgements
The authors would like to thank Sara Fanara for
co-supervising Lennart Koch's Master's thesis. Maximilian Schanofski is thanked
for the help with IR analyses. The authors thank two anonymous reviewers for
their constructive comments and Fabrice Gaillard for editorial handling of
the special issue.
Financial support
This research has been supported by the Deutsche Forschungsgemeinschaft (grant no. FA 1477/1-1 | SCHM 1622/9-1).This open-access publication was funded by the University of Göttingen.
Review statement
This paper was edited by Fabrice Gaillard and reviewed by two anonymous referees.
ReferencesAlletti, M., Baker, D. R., and Freda, C.: Halogen diffusion in a basaltic
melt, Geochim. Cosmochim. Ac., 71, 3570–3580,
10.1016/j.gca.2007.04.018, 2007.Anderson, A. T.: Some basaltic and andesitic gases, Rev. Geophys., 13, 37–55,
10.1029/RG013i001p00037, 1975.Baker, D. R., Freda, C., Brooker, R. A., and Scarlato, P.: Volatile
diffusion in silicate melts and its effects on melt inclusions, Ann.
Geophys., 48, 699–717, 10.4401/ag-3227, 2005.Balcone-Boissard, H., Baker, D. R., Villemant, B., and Boudon, G.: F and Cl
diffusion in phonolitic melts: Influence of the Na / K ratio, Chem. Geol.,
263, 89–98, 10.1016/j.chemgeo.2008.08.018, 2009.Benne, D. and Behrens, H.: Water solubility in haplobasaltic melts, Eur. J.
Mineral., 15, 803–814, 10.1127/0935-1221/2003/0015-0803,
2003.Blank, J. G.: An experimental investigation of the behavior of carbon
dioxide in rhyolitic melt, Dissertation (Ph.D.), California Institute of
Technology, 10.7907/tq3x-2059, 1993.Böhm, A. and Schmidt, B. C.: Fluorine and chlorine diffusion in
phonolitic melt, Chem. Geol., 346, 162–171,
10.1016/j.chemgeo.2012.09.005, 2013.Botcharnikov, R. E., Behrens, H., and Holtz, F.: Solubility and speciation
of C–O–H fluids in andesitic melt at T=1100–1300 ∘C and
P=200 and 500 MPa, Chem. Geol., 229, 125–143,
10.1016/j.chemgeo.2006.01.016, 2006.
Crank, J.: The mathematics of diffusion, Second edition, Clarendon Press,
Oxford, 421 pp., ISBN: 0198533446, 1975.Fahrenfort, J.: Attenuated total reflection: A new principle for the
production of useful infra-red reflection spectra of organic compounds,
Spectrochim. Ac., 17, 698–709, 10.1016/0371-1951(61)80136-7,
1961.Fine, G. and Stolper, E.: The speciation of carbon dioxide in sodium
aluminosilicate glasses, Contrib. Mineral. Petr., 91, 105–121,
10.1007/bf00377759, 1985.
Fogel, R. A. and Rutherford, M. J.: The solubility of carbon dioxide in
rhyolitic melts; a quantitative FTIR study, Am. Mineral., 75, 1311–1326,
1990.Freda, C., Baker, D. R., and Scarlato, P.: Sulfur diffusion in basaltic
melts, Geochim. Cosmochim. Ac., 69, 5061–5069,
10.1016/j.gca.2005.02.002, 2005.Freda, C., Gaeta, M., Giaccio, B., Marra, F., Palladino, D. M., Scarlato,
P., and Sottili, G.: CO2-driven large mafic explosive eruptions: the
Pozzolane Rosse case study from the Colli Albani Volcanic District (Italy),
Bull. Volcanol., 73, 241–256, 10.1007/s00445-010-0406-3,
2011.
Ganguly, J., Bhattacharya, R. N., and Chakraborty, S.: Convolution effect in
the determination of composition profiles and diffusion coefficients by
microprobe step scans, Am. Mineral., 73, 901–909, 1988.Guillot, B. and Sator, N.: Carbon dioxide in silicate melts: A molecular
dynamics simulation study, Geochim. Cosmochim. Ac., 75, 1829–1857,
10.1016/j.gca.2011.01.004, 2011.Hammerli, J., Hermann, J., Tollan, P., and Naab, F.: Measuring in situ
CO2 and H2O in apatite via ATR-FTIR, Contrib. Mineral. Petrol.,
176, 1–20, 10.1007/s00410-021-01858-6, 2021.Johnson, M. C., Anderson Jr., A. T., and Rutherford, M. J.: Pre-eruptive
volatile contents of magmas, in: Volatiles in Magmas, edited by: Carroll, M.
R. and Holloway, J. R., Mineralogical Society of America, Washington, DC,
281–330, 10.1515/9781501509674-014, 1994.Kleest, C., Webb, S. L., and Fanara, S.: Rheology of melts from the colli
albani volcanic district (Italy): a case study, Contrib. Mineral. Petr.,
175, 82, 10.1007/s00410-020-01720-1, 2020.Kohn, S. C., Brooker, R. A., and Dupree, R.: 13C MAS NMR: A method for
studying CO2 speciation in glasses, Geochim. Cosmochim. Ac., 55,
3879–3884, 10.1016/0016-7037(91)90082-G, 1991.Konschak, A. and Keppler, H.: The speciation of carbon dioxide in silicate
melts, Contrib. Mineral. Petr., 167, 998,
10.1007/s00410-014-0998-2, 2014.Le Gall, N. and Pichavant, M.: Experimental simulation of bubble nucleation
and magma ascent in basaltic systems: Implications for Stromboli volcano,
Am. Mineral., 101, 1967–1985, 10.2138/am-2016-5639, 2016a.Le Gall, N. and Pichavant, M.: Homogeneous bubble nucleation in H2O-
and H2O-CO2 -bearing basaltic melts: Results of high temperature
decompression experiments, J. Volcanol. Geoth. Res., 327, 604–621,
10.1016/j.jvolgeores.2016.10.004, 2016b.Lesne, P., Kohn, S. C., Blundy, J., Witham, F., Botcharnikov, R. E., and
Behrens, H.: Experimental Simulation of Closed-System Degassing in the
System Basalt–H2O–CO2–S–Cl, J, Petrol., 52, 1737–1762,
10.1093/petrology/egr027, 2011.Lowenstern, J. B. and Pitcher, B. W.: Analysis of H2O in silicate glass
using attenuated total reflectance (ATR) micro-FTIR spectroscopy, Am.
Mineral., 98, 1660–1668, 10.2138/am.2013.4466, 2013.Morizet, Y., Kohn, S. C., and Brooker, R. A.: Annealing experiments on
CO2-bearing jadeite glass: an insight into the true temperature
dependence of CO2 speciation in silicate melts, Mineral. Mag., 65,
701–707, 10.1180/0026461016560001, 2001.
Mysen, B. O.: Structure and properties of silicate melts, Developments in
geochemistry, 4, Elsevier, Amsterdam, 354 pp., ISBN: 0444416358, 1988.Ni, H. and Keppler, H.: Carbon in Silicate Melts, in: Carbon in Earth,
edited by: Hazen, R. M., Jones, A. P., and Baross, J. A., Mineralogical
Society of America, 251–287, 10.2138/rmg.2013.75.9, 2013.Ni, H. and Zhang, Y.: H2O diffusion models in rhyolitic melt with new
high pressure data, Chem. Geol., 250, 68–78,
10.1016/j.chemgeo.2008.02.011, 2008.Nowak, M. and Behrens, H.: An experimental investigation on diffusion of
water in haplogranitic melts, Contrib. Mineral. Petr., 126, 365–376,
10.1007/s004100050256, 1997.Nowak, M., Porbatzki, D., Spickenbom, K., and Diedrich, O.: Carbon dioxide
speciation in silicate melts: a restart, Earth Planet. Sc. Lett., 207,
131–139, 10.1016/S0012-821X(02)01145-7, 2003.Nowak, M., Schreen, D., and Spickenbom, K.: Argon and CO2 on the race
track in silicate melts: A tool for the development of a CO2 speciation
and diffusion model, Geochim. Cosmochim. Ac., 68, 5127–5138,
10.1016/j.gca.2004.06.002, 2004.Pichavant, M., Di Carlo, I., Rotolo, S. G., Scaillet, B., Burgisser, A., Le
Gall, N., and Martel, C.: Generation of CO2-rich melts during basalt
magma ascent and degassing, Contrib. Mineral. Petr., 166, 545–561,
10.1007/s00410-013-0890-5, 2013.Pichavant, M., Le Gall, N., and Scaillet, B.: Gases as Precursory Signals:
Experimental Simulations, New Concepts and Models of Magma Degassing, in:
Volcanic unrest: From science to society, edited by: Gottsmann, J., Neuberg,
J., and Scheu, B., Springer, Cham, 139–154,
10.1007/11157_2018_35, 2019.Schanofski, M., Fanara, S., and Schmidt, B. C.: CO2–H2O
solubility in K-rich phonolitic and leucititic melts, Contrib. Mineral.
Petr., 174, 52, 10.1007/s00410-019-1581-7, 2019.Schanofski, M., Koch, L., and Schmidt, B. C.: CO2 quantification in
silicate glasses using μ-ATR FTIR spectroscopy, Am. Mineral.,
10.2138/am-2022-8477, in press, 2023.Schmidt, B. C., Blum-Oeste, N., and Flagmeier, J.: Water diffusion in
phonolite melts, Geochim. Cosmochim. Ac., 107, 220–230,
10.1016/j.gca.2012.12.044, 2013.Sierralta, M., Nowak, M., and Keppler, H.: The influence of bulk composition
on the diffusivity of carbon dioxide in Na aluminosilicate melts, Am.
Mineral., 87, 1710–1716, 10.2138/am-2002-11-1221, 2002.Sparks, R. S. J., Barclay, J., Jaupart, C., Mader, H. M., and Phillips, J.
C.: Physical aspects of magma degassing, I. Experimental and theoretical
constraints on vesiculation, in: Volatiles in Magmas, edited by: Carroll, M.
R. and Holloway, J. R., Mineralogical Society of America, Washington, DC,
413–446, 10.1515/9781501509674-017, 1994.Spickenbom, K., Sierralta, M., and Nowak, M.: Carbon dioxide and argon
diffusion in silicate melts: Insights into the CO2 speciation in
magmas, Geochim. Cosmochim. Ac., 74, 6541–6564,
10.1016/j.gca.2010.08.022, 2010.Symonds, R. B., Rose, W. I., Bluth, G. J. S., and Gerlach, T. M.:
Volcanic-gas studies: methods, results, and applications, in: Volatiles in
Magmas, edited by: Carroll, M. R. and Holloway, J. R., Mineralogical Society
of America, Washington, DC, 1–66,
10.1515/9781501509674-007, 1994.
Watson, E. B.: Diffusion of dissolved CO2 and Cl in hydrous silicic to
intermediate magmas, Geochim. Cosmochim. Ac., 55, 1897–1902,
10.1016/0016-7037(91)90031-Y, 1991.Watson, E. B., Sneeringer, M. A., and Ross, A.: Diffusion of dissolved
carbonate in magmas: Experimental results and applications, Earth Planet.
Sc. Lett., 61, 346–358, 10.1016/0012-821X(82)90065-6, 1982.Yamashita, S., Kitamura, T., and Kusakabe, M.: Infrared spectroscopy of
hydrous glasses of arc magma compositions, Geochem. J., 31, 169–174,
10.2343/geochemj.31.169, 1997.Zhang, Y. and Behrens, H.: H2O diffusion in rhyolitic melts and
glasses, Chem. Geol., 169, 243–262,
10.1016/S0009-2541(99)00231-4, 2000.Zhang, Y. and Ni, H.: Diffusion of H, C, and O Components in Silicate Melts,
in: Diffusion in Minerals and Melts, edited by: Zhang, Y. and Cherniak, D.
J., Mineral. Soc. Am., 171–225,
10.2138/rmg.2010.72.5, 2010.Zhang, Y. and Stolper, E. M.: Water diffusion in a basaltic melt, Nature,
351, 306–309, 10.1038/351306a0, 1991.Zhang, Y., Xu, Z., Zhu, M., and Wang, H.: Silicate melt properties and
volcanic eruptions, Rev. Geophys., 45, 4, 10.1029/2006RG000216,
2007.