Modeling distribution of polymerized anions on
the liquidus of the Na2O-SiO2
system
In previous studies, we have presented a STRUCTON computer program
designed for statistical simulations of molecular-size distribution of
Si-anions in polymerized silicate melts (Polyakov, Ariskin, 2008). The INPUT file of this model includes
proportions of five Qn- structons– “SiO4-groups” (0 ≤ n ≤
4, Q0 corresponds to
the SiO44- ion) which are distinguished by the number of bridging
oxygen. A system of OUTPUT files includes statistical characteristics for the
modeled ensembles of the chain, branched, and ring complexes corresponding to a
general formula (SiiO3i+1-j)2(i+1-j)-, where i
is the size of an anion, j is the number of the ring bonds. First
calculations were conducted for the compositions ranging from ortho- to metasilicates based on a random distribution of Qn-structons
suggesting an equal reactivity of various atoms of non-bridging oxygen.
According to our calculations, the number of polymerized anion species varies
from 3 (SiO44-,
Si2O76-, Si3O108-)
in Na4SiO4 composition to
Qn-1 + Qn+1 = 2Qn ,š
kn=[Qn]2/{[Qn-1][Qn+1]} (1 ≤ n ≤ 3). (1)
These reactions cause fractions of Q1, Q2 and Q3-structons
to increase with respect to fractions of “end-member” Q0- andš Q4-structons,
The distribution of Qn-structons
observed from Raman and NMR spectroscopy is never coincides with the random
distribution for a given melt composition (Mysen,
Richet, 2005) Such deviation of the real Q4-structon
distribution fromš the random one affects
the distribution of polymer species and should be taken into account in correct
models. For
this purpose, a special subroutine of the STRUCTON program was developed. It
calculates the distributionš
of
Qn-structons
for a given composition using values of constants of the disproportionation
reactions (1). The spectroscopic data on these constants were rationalized
using the following Arrhenius temperature dependence:
, (2)
where sn
is the entropic factors; H is the enthalpy; n = 1,2,3; and R and T
have a common meaning. s1 = s3 = 8/3 and s2 = 9/4
are the same for silicate systems in our model, whereas the enthalpy depends on
a cation type. The realistic distribution of Qn-structons
calculated using the disproportination constants (2)
is used as an INPUT information for the STRUCTON
model. This allows one to estimate distribution of (SiiO3i+1-j)2(i+1-j)- species more correctly than in
the the scope of the random model. Since the random
distribution of Qn-structons
corresponds to infinite temperature in (2), one can conclude that accounting
for the disproportionation reactions provides
evaluations of the effect of temperature on the distribution of anion polymer
species in the melt.
As an example, we have carried out a set of testing calculations for the
Na2O-SiO2 system in the range of 33.3-50.0 mol% of SiO2.
Results of these calculations along the liquidus of
the binary system have demonstrated a systematic decrease in the proportion of Q0-species with respect to
that following from stochastic modeling. Na6Si2O7
(40 mol% SiO2) and Na2SiO3 (50 mol% SiO2)
melts are enriched in Q1-
and Q2-structons,
respectively (see Shildt et al., 2009). This results in the predominance of Si2O76- dimers in the Na6Si2O7 melt and “flat” rings
SinO3n2n-(Si3O96-,
Si4O128-, Si5O1510-,
etc.) in the Na2SiO3 melt. Such
predominance of stoichiometric polymeric compounds in
the melts follows directly from our modeling.
References:
Ariskin
A.A., Polyakov V.B. Simulation of molecular mass
distributions and evaluation of O2- concentrations in polymerized
silicate melts // Geochemistry International. 2008. Vol. 46. P. 429-447.
Mysen B., Richet P. Silicate glasses and melts:
properties and structure.
Polyakov V.B., Ariskin A.A. Simulation of the
composition and proportions of anions in polymerized silicate melts // Glass Physics
and Chemistry. 2008. Vol. 34. P. 50-62.
Shildt A.V., Polyakov V.B., Ariskin
A.A. Calculation of distributions of Q-species on the liquidus of the Na2O-SiO2 system:
Updating the STRUCTON model // Abs. of Annual seminar on experimental
mineralogy, petrology, and geochemistry (Moscow, Vernadsky
Institute, 2009).š
This study was
supported by the RFBR grant 08-05-00194-a.