Modeling NiO Activities in Silicate Melts Considering Separate

46th Lunar and Planetary Science Conference (2015)
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Modeling NiO Activities in Silicate Melts Considering Separate Contributions from Ni 2+ and O2-: Dependence
of O2- on melt polymerization. L. Anderson1, E. Young1, and R. O. Colson1, 2, 1Minnesota State University Moorhead, Moorhead MN, [email protected]
Introduction: Partition coefficients for trace element components in silicate melts depend on activity
coefficients in the melt. These activity coefficients
vary with composition in complex ways. We have
previously proposed that modeling of trace element
activities might be improved if the oxide component is
modeled as the composite of separate activities for
cation and oxide anion [1, 2].
Our modeling in this way has been quite successful, but we have found that a few points from the most
depolymerized melts from [3] and [4] deviate from the
trend of other data, suggesting unmodeled variations in
depolymerized melts. We report here additional experiments in depolymerized melts to test these results and
to understand what might be causing these deviations.
Experimental Methods:
End-member compositions for two series between
more polymerized and less polymerized melts were
prepared from reagent grade chemicals and ground
with an agate mortar and pestle under methanol. These
end member compositions were: Diopsidic, FMS, and
SCMA10-5 (labled as Depoly in Table 1). A series of
compositions for each trend was prepared by mixing
these end-member mixtures in the proportions shown
in Table 1, again grinding under methanol. About
0.3wt% NiO was added to each composition.
Experiments were run on Pt wire loops in a 1-atm
gas mixing furnace in a CO2 atmosphere at temperatures as shown in Table 1. Temperatures were measured with a type-S thermocouple placed adjacent to the
sample.
The activity coefficients for NiO in the Fe-free experiments were measured using square wave voltometry after the methodology in [2]. In this method, a
voltage is imposed between platinum electrodes immersed in the melt and the resulting current measured.
This current results from the reduction of Ni+2 into the
platinum cathode while O2- is oxidized to O2 at the
anode. Free energy for the reaction, and activity coefficients for NiO, can be calculated from the voltage at
which the current reaches a peak. To check for equilibrium during the experiments, we checked for shifts
in peak position at different rates of voltage change.
The electrochemical method only works in Fe-free
systems because reduction of the Fe would mask any
current arising from the reduction of Ni2+. To measure the activity coefficients for NiO in the Fe-bearing
systems, we used an approach similar to [5], measuring
the ratio of the concentration of Ni in the Pt loop to
that in the melt (using a JEOL 733 Superprobe with
long counting times). At any one temperature, pressure, and fO2, we can compare Ni concentration in
platinum in equilibrium with a known melt to that in
an unknown melt according to the relationship
a*melt/X*Pt = amelt/XPt
where a*melt = activity of NiO in a standard melt in
which the activity coefficient is known, amelt = the activity of NiO in the melt of interest, and XPt = the concentration of Ni in Pt. We calculated the activity coefficient from the relationships: γ = a/X, with the activity
determined relative to the standard state of liquid NiO
after the free energy expression from [3].
Table 1. Experimental Compositions and Results
T (C)
100 %
DiDepoly
75% DiDepoly
50% DiDepoly
35% DiDepoly
1562
γ
NiO
1.92
SiO2
Al2O3
MgO
CaO
FeO
55.5
0
18.6
25.9
0
1564
2.24
52.6
2.5
22.9
21.9
0
1561
2.38
49.6
5
27.3
18.0
0
1600
2.67
47.9
6.5
29.9
15.6
0
FMS75%Di
FMS50% Di
FMS25%Di
FMS0%Di
1566
1.89
49.0
0
17.7
19.4
13.9
1566
1.56
42.5
0
16.9
13.0
27.7
1564
1.33
36.0
0
16.0
6.5
41.6
1564
1.32
29.5
0
15.1
0
55.4
Modeling NiO Activity: Activities for NiO in silicate melts vary in a complex way with melt polymerization, as shown both by the degree of scatter in the
data seen in Figure 1 and in the apparent reversal of
trend.
We have proposed previously [1, 2] that in order to
understand this complexity, we should look beyond
NiO acting as a simple oxide compound, and instead
consider the Ni+2 cation and the O-2 ion acting separately within the melt. The activity of NiO can be related to activites of the ionic species through a reaction
of the sort NiO ↔ Ni2+ + O2-.
We have proposed [2] that a near-ideal mixing
model can be derived for Ni2+ in silicate melts, where
the Ni2+ mixes with a ‘mixing pool’ of cations of similar size and charge to Ni+2. The proportion of a cation
46th Lunar and Planetary Science Conference (2015)
1358.pdf
7.50
6.50
γNiO
5.50
4.50
3.50
2.50
1.50
0.30
oxide ion goes to infinity as BO goes to 0, an unrealistic result. Thus, the deviation seen in Fig. 2 is not unreasonable.
0.7159 *NBO2/BO γNiO/(modeled γNi2+)
that participates in the mixing pool is inversely proportional the difference in cation size between the cation
of interest and Ni2+. The activity for Ni2+ then takes on
the form of aNi2+ = XNi2+/Mixing Pool.
2.8
2.3
1.8
2:1
1.3
0.3
-0.2
0.30
0.40
0.50
0.60
0.70
0.80
Molar SiO2 + TiO2 + 0.5*AlO1.5
Fig. 1. NiO activity coefficients relative to a standard
state of liquid NiO. Squares are from the present
study. Diamonds are from [1,2,3,4,6,7,8,9,10,11,12].
We also proposed that the O-2 oxide ion activity
can be modeled assuming that the oxide ion is buffered
by bridging oxygens and non-bridging oxygens (BO
and NBO respectively) according to the following reaction.
Si-O-Si + O-2  2Si-O
or
(BO) + O-2  2(NBO)
From this we can derive the activity of the oxide ion as
the following: aO2- = K(NBO2/BO) [2]
Figure 2 shows the residuals for this type of modeling for activity data from a number of workers. Residuals of the model are assigned to error in the modeling
of the oxide ion through the expression:
0.7159*NBO2/BO – γNiO/(modeled Ni2+). Conceptually, this expression is the difference between the activity for the oxide ion modeled from the expression
above and a value for the oxide ion activity based on
measured values for NiO activity and the modeled Ni2+
activity. This yields a deviation of the modeled oxide
ion activity from values based on actual measurements.
The sharp decrease in scatter in the data relative to
Figure 1 shows the overall success of this approach in
understanding the complex variations in NiO activity
with composition, but the residuals deviate sharply
from zero as the melt becomes increasingly depolymerized—meaning that our model for the activity of
the oxide ion appears to become increasingly wrong
for less polymerized melts. We might expect that
bridging oxygens can only buffer the oxide ion under
conditions where the concentration of bridging oxygens is large compared to the oxide ion, true only in
highly-polymerized melts. Also, we know that the
expression for the oxide ion activity above must fail at
low BO, since the model predicts that the activity of
1:1
0.8
3:1
0.40
0.50
0.60
0.70
0.80
Molar SiO2 + TiO2 + 0.5*AlO1.5
Figure 2. Data sources as in Fig 1. Ratios show ratio
NBO:BO, with NBO calculated as (2*total O)-4*(SiO2
+ TiO2+0.5*AlO1.5)/(total O), and BO = 1-NBO.
The activity of the oxide ion begins to deviate
from the model equation at values for
SiO2+TiO2+0.5*AlO1.5 less than about 0.6, corresponding to a NBO/BO ratio of about 1:1. Additional sharp
deviations occur at NBO/BO ratios of 2:1 and 3:1. The
correlation between where these deviations occur and
stoichiometric parameters for NBO/BO suggests the
possibility that we can better understand variations in
the oxide ion activity, and from that variations in activities for a wide range of trace element oxides, by considering that the buffering reactions controlling the
oxide ion in the melt change with degree of polymerization.
Conclusions: Modeling activity for NiO by considering separate variations in activities for Ni2+ and
O2- works well, but only if the variations in activity of
the oxide ion are modeled differently in regions of the
melt defined by stoichiometric ratios of NBO/BO.
This is consistent with the idea that fundamental reactions in the melt that buffer the oxide ion change as
melt polymerization changes.
Other trace oxides
might be modeled in a similar fashion, with the same
variation in the oxide ion applying to those components as well.
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H St. C., and Eggins S M (2002) Chem. Geol. 186, 151-181
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